{"type": "FeatureCollection", "features": [{"id": "10.1016/j.scitotenv.2021.152880", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:41Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2021.152880"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.152880", "name": "item", "description": "10.1016/j.scitotenv.2021.152880", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.152880"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "10.1016/j.jclepro.2008.04.009", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:24Z", "type": "Journal Article", "created": "2008-06-13", "title": "Decisions To Reduce Greenhouse Gases From Agriculture And Product Transport: Lca Case Study Of Organic And Conventional Wheat", "description": "A streamlined hybrid life cycle assessment is conducted to compare the global warming potential (GWP) and primary energy use of conventional and organic wheat production and delivery in the US. Impact differences from agricultural inputs, grain farming, and transport processes are estimated. The GWP of a 1 kg loaf of organic wheat bread is about 30 g CO<sub>2</sub>-eq less than the conventional loaf. When organic wheat is shipped 420 km farther to market, organic and conventional wheat systems have similar impacts. These results can change dramatically depending on soil carbon accumulation and nitrous oxide emissions from the two systems. Key parameters and their variability are discussed to provide producers, wholesale and retail consumers, and policymakers metrics to align their decisions with low-carbon objectives.", "keywords": ["2. Zero hunger", "13. Climate action", "0502 economics and business", "05 social sciences", "FOS: Environmental engineering", "90599 Civil Engineering not elsewhere classified", "7. Clean energy", "01 natural sciences", "FOS: Civil engineering", "90799 Environmental Engineering not elsewhere classified", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.jclepro.2008.04.009"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jclepro.2008.04.009", "name": "item", "description": "10.1016/j.jclepro.2008.04.009", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jclepro.2008.04.009"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-01-01T00:00:00Z"}}, {"id": "10.1016/j.jclepro.2020.125466", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:25Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region &gt; western region &gt; northeast region &gt; central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/10.1016/j.jclepro.2020.125466"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jclepro.2020.125466", "name": "item", "description": "10.1016/j.jclepro.2020.125466", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jclepro.2020.125466"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.1038/s41467-022-31540-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:34Z", "type": "Journal Article", "created": "2022-07-01", "title": "Global stocks and capacity of mineral-associated soil organic carbon", "description": "Abstract<p>Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1\uffe2\uff80\uff89m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world\uffe2\uff80\uff99s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.</p", "keywords": ["Carbon sequestration", "550", "Permafrost", "/704/106/47/4113", "Carbon Dynamics in Peatland Ecosystems", "Digital Soil Mapping Techniques", "Oceanography", "01 natural sciences", "Agricultural and Biological Sciences", "Soil", "Soil water", "Carbon fibers", "Climate change", "2. Zero hunger", "Minerals", "Ecology", "Forestry Sciences", "Q", "Total organic carbon", "article", "Life Sciences", "Composite number", "Geology", "Agriculture", "/704/106/694/682", "Soil carbon", "Chemistry", "/704/47/4113", "CESD-Soil Quality", "Physical Sciences", "Environmental chemistry", "Engineering sciences. Technology", "Composite material", "/141", "Carbon Sequestration", "Environmental Engineering", "Life on Land", "Science", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Veterinary and Food Sciences", "Soil Science", "/704/106/694/1108", "Environmental science", "Article", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "Soil Carbon Sequestration", "Biology", "0105 earth and related environmental sciences", "Soil science", "Agricultural", "Soil organic matter", "FOS: Environmental engineering", "Soil Properties", "FOS: Earth and related environmental sciences", "15. Life on land", "Materials science", "Carbon", "Carbon dioxide", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "/119", "Climate Change Impacts and Adaptation", "Environmental Sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41467-022-31540-9.pdf"}, {"href": "https://escholarship.org/content/qt2vm0b30s/qt2vm0b30s.pdf"}, {"href": "https://doi.org/10.1038/s41467-022-31540-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41467-022-31540-9", "name": "item", "description": "10.1038/s41467-022-31540-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41467-022-31540-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-01T00:00:00Z"}}, {"id": "10.1038/s42949-024-00154-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:39Z", "type": "Journal Article", "created": "2024-03-16", "title": "Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services", "description": "Abstract<p>Greenspaces are important for sustaining healthy urban environments and their human populations. Yet their capacity to support multiple ecosystem services simultaneously (multiservices) compared with nearby natural ecosystems remains virtually unknown. We conducted a global field survey in 56 urban areas to investigate the influence of urban greenspaces on 23 soil and plant attributes and compared them with nearby natural environments. We show that, in general, urban greenspaces and nearby natural areas support similar levels of soil multiservices, with only six of 23 attributes (available phosphorus, water holding capacity, water respiration, plant cover, arbuscular mycorrhizal fungi (AMF), and arachnid richness) significantly greater in greenspaces, and one (available ammonium) greater in natural areas. Further analyses showed that, although natural areas and urban greenspaces delivered a similar number of services at low (&gt;25% threshold) and moderate (&gt;50%) levels of functioning, natural systems supported significantly more functions at high (&gt;75%) levels of functioning. Management practices (mowing) played an important role in explaining urban ecosystem services, but there were no effects of fertilisation or irrigation. Some services declined with increasing site size, for both greenspaces and natural areas. Our work highlights the fact that urban greenspaces are more similar to natural environments than previously reported and underscores the importance of managing urban greenspaces not only for their social and recreational values, but for supporting multiple ecosystem services on which soils and human well-being depends.</p", "keywords": ["Medio ambiente natural", "2410.05 Ecolog\u00eda Humana", "Health", " Toxicology and Mutagenesis", "0211 other engineering and technologies", "710", "Urban Green Space", "02 engineering and technology", "01 natural sciences", "zelene povr\u0161ine", "ekosistemske storitve", " zelene povr\u0161ine", " urbani gozdovi", " tla", "Urban planning", "Natural (archaeology)", "11. Sustainability", "Urban Heat Islands and Mitigation Strategies", "info:eu-repo/classification/udc/630*1:630*9", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "2417.13 Ecolog\u00eda Vegetal", "Carbon cycle", "3. Good health", "soil", " ecosystem services", " urban forests", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Archaeology", "Physical Sciences", "urban forests", "HT361-384", "Ecolog\u00eda (Biolog\u00eda)", "Urbanization. City and country", "Environmental Engineering", "711.4:911.375", "631.4", "Environmental science", "soil", "12. Responsible consumption", "Impact of Urban Green Space on Public Health", "Urban ecosystem", "XXXXXX - Unknown", "Ecosystem services", "14. Life underwater", "Agroforestry", "info:eu-repo/classification/udc/630*1", "Biology", "City planning", "Ecosystem", "0105 earth and related environmental sciences", "SDG-15: Life on land", "tla", "FOS: Environmental engineering", "15. Life on land", "ekosistemske storitve", "Urban ecology", "HT165.5-169.9", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "urbani gozdovi", "502.3", "ecosystem services"]}, "links": [{"href": "https://www.nature.com/articles/s42949-024-00154-z.pdf"}, {"href": "https://doi.org/10.1038/s42949-024-00154-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/npj%20Urban%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s42949-024-00154-z", "name": "item", "description": "10.1038/s42949-024-00154-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s42949-024-00154-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-16T00:00:00Z"}}, {"id": "10.17632/h36pb9v55v", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:40Z", "type": "Dataset", "title": "Data Analysis of the Evaluation of Sustainable Riparian Revegetation with Local Fruit Trees around a Reservoir of a Hydroelectric Power Plant in Central Brazil", "description": "An experiment was carried out in two sites with fifteen native species, selected for having ideal phytosocio-logical properties, however, nine of them showed a survivability considered satisfactory in a planting situation, with a view to large-scale planting. Assuming that the planting of the nine native fruit trees can be a quick solution to the attraction and preservation of wildlife, it would therefore provide sustainable riparian revegetation around the reservoir. Data of mean values for the fruit trees morphological features are measured: Trees\u2019 percentage of survivors (SUR); Average diameter at breast height (DBH); Average total height (HEI) and; Average number of leaves per branch (LEA). The soil chemical composition of the two sites ES-1 and ES-2 (in Portuguese) is available also.", "keywords": ["Operations Research", "Environmental Engineering", "FOS: Environmental engineering", "15. Life on land", "7. Clean energy", "Environmental Engineering Modeling", "Multicriteria Analysis"], "contacts": [{"organization": "Ribas, Jose Roberto", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.17632/h36pb9v55v"}, {"rel": "self", "type": "application/geo+json", "title": "10.17632/h36pb9v55v", "name": "item", "description": "10.17632/h36pb9v55v", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17632/h36pb9v55v"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-12T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\uffcf\uff83\uffc2\uffb0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \uffcf\uff83\uffc2\uffb0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \uffcf\uff83\uffc2\uffb0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \uffcf\uff83\uffc2\uffb0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\uffe2\uff88\uff923).</p>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10.3390/rs9111155"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs9111155", "name": "item", "description": "10.3390/rs9111155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9111155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "10.5061/dryad.rn8pk0pm8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:17Z", "type": "Dataset", "created": "2024-06-28", "title": "Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production", "description": "unspecifiedThis study investigates uncertainties in greenhouse gas (GHG) emission  factors related to switchgrass-based biofuel production in Michigan. Using  three life cycle assessment (LCA) databases\u2014 US lifecycle inventory  database (USLCI), GREET, and Ecoinvent\u2014each with multiple versions, we  recalculated the global warming intensity (GWI) and GHG mitigation  potential in a static calculation. Employing Monte Carlo simulations along  with local and global sensitivity analyses, we assess uncertainties and  pinpoint key parameters influencing GWI. The convergence of results across  our previous study, static calculations, and Monte Carlo simulations  enhances the credibility of estimated GWI values. Static calculations,  validated by Monte Carlo simulations, offer reasonable central tendencies,  providing a robust foundation for policy considerations. However, the  wider range observed in Monte Carlo simulations underscores the importance  of potential variations and uncertainties in real-world applications.  Sensitivity analyses identify biofuel yield, GHG emissions of electricity,  and soil organic carbon (SOC) change as pivotal parameters influencing  GWI. Decreasing uncertainties in GWI may be achieved by making greater  efforts to acquire more precise data on these parameters. Our study  emphasizes the significance of considering diverse GHG factors and  databases in GWI assessments and stresses the need for accurate  electricity fuel mixes, crucial information for refining GWI assessments  and informing strategies for sustainable biofuel production.", "keywords": ["Sensitivity Analysis", "Switchgrass", "FOS: Environmental engineering", "Cellulosic biofuel", "Global warming intensity", "Greenhouse gas emission factor", "LCA database", "uncertainty analysis"], "contacts": [{"organization": "Kim, Seungdo, Dale, Bruce, Basso, Bruno,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.rn8pk0pm8"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.rn8pk0pm8", "name": "item", "description": "10.5061/dryad.rn8pk0pm8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.rn8pk0pm8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-16T00:00:00Z"}}, {"id": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:31Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10.5194/hess-2019-105", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:34Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.                         </p></article>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-2019-105"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-2019-105", "name": "item", "description": "10.5194/hess-2019-105", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-2019-105"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.5194/hess-24-1781-2020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:35Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\uffe2\uff80\uff89km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\uffe2\uff80\uff89km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\uffe2\uff80\uff932018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\uffe2\uff80\uff932018). The field was seeded for the 2014\uffe2\uff80\uff932015 (S1), 2016\uffe2\uff80\uff932017 (S2) and 2017\uffe2\uff80\uff932018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\uffe2\uff80\uff932016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \uffce\uffb1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \uffce\uffb1PT remains at a mostly constant value (\uffe2\uff88\uffbc\uffe2\uff80\uff890.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\uffe2\uff80\uff89W/m2 for S1, S2, S3 and B1 respectively.                         </p>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.5194/hess-24-1781-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-24-1781-2020", "name": "item", "description": "10.5194/hess-24-1781-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-24-1781-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:35Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving  Earth system science for over a decade", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.                     </p></article>", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "Earth and Planetary Sciences", "Physical Sciences", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "name=Earth and Planetary Sciences (miscellaneous)", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "Soil Moisture International Network", "0207 environmental engineering", "Epistemology", "Environmental science", "G", "Database", "Soil Moisture; network", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Consecutive dry days", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "Globe", "Computer science", "Environmental sciences", "QE Geology", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "soil moisture", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://research.unipg.it/bitstream/11391/1498417/2/2021_The%20international%20soil_OA.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-5749-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-25-5749-2021", "name": "item", "description": "10.5194/hess-25-5749-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-5749-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "10.5194/isprs-archives-xlii-3-w6-9-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:35Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-xlii-3-w6-9-2019", "name": "item", "description": "10.5194/isprs-archives-xlii-3-w6-9-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10.60692/9nxrv-e7y75", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:55Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region &gt; western region &gt; northeast region &gt; central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/10.60692/9nxrv-e7y75"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/9nxrv-e7y75", "name": "item", "description": "10.60692/9nxrv-e7y75", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/9nxrv-e7y75"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "10.60692/g4rcv-eqz54", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:55Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.</p></article>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/10.60692/g4rcv-eqz54"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/g4rcv-eqz54", "name": "item", "description": "10.60692/g4rcv-eqz54", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/g4rcv-eqz54"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "10.60692/t1jsz-vm842", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:23:55Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.60692/t1jsz-vm842"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/t1jsz-vm842", "name": "item", "description": "10.60692/t1jsz-vm842", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/t1jsz-vm842"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "10.6084/m9.figshare.6982730", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:31Z", "type": "Journal Article", "created": "2018-08-18", "title": "Homogenisation of two fluid flow dependent on exudate concentration. from The effect of root exudates on rhizosphere water dynamics", "description": "The Cahn-Hilliard-Stokes equations are combined with the advection-diffusion equation by concentration dependent variables to model the pore scale movement of water, air and root exudates in soil. The supplementary material presents the application of multiscale homogenisation theory to derive a coupled set of equations to describe the processes on the macroscale, with parameters that are related to the underlying geometry.", "keywords": ["Geophysics", "FOS: Environmental engineering", "FOS: Mathematics", "FOS: Earth and related environmental sciences", "10299 Applied Mathematics not elsewhere classified", "6. Clean water", "90799 Environmental Engineering not elsewhere classified"], "contacts": [{"organization": "L. J. Cooper, K. R. Daly, P. D. Hallett, N. Koebernick, T. S. George, T. Roose,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.6982730"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20Royal%20Society%20A", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.6982730", "name": "item", "description": "10.6084/m9.figshare.6982730", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.6982730"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.6982730.v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:31Z", "type": "Journal Article", "created": "2018-08-18", "title": "Homogenisation of two fluid flow dependent on exudate concentration. from The effect of root exudates on rhizosphere water dynamics", "description": "The Cahn-Hilliard-Stokes equations are combined with the advection-diffusion equation by concentration dependent variables to model the pore scale movement of water, air and root exudates in soil. The supplementary material presents the application of multiscale homogenisation theory to derive a coupled set of equations to describe the processes on the macroscale, with parameters that are related to the underlying geometry.", "keywords": ["Geophysics", "FOS: Environmental engineering", "FOS: Mathematics", "FOS: Earth and related environmental sciences", "10299 Applied Mathematics not elsewhere classified", "6. Clean water", "90799 Environmental Engineering not elsewhere classified"], "contacts": [{"organization": "L. J. Cooper, K. R. Daly, P. D. Hallett, N. Koebernick, T. S. George, T. Roose,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.6982730.v1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20Royal%20Society%20A", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.6982730.v1", "name": "item", "description": "10.6084/m9.figshare.6982730.v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.6982730.v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10067/1897670151162165141", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:12Z", "type": "Journal Article", "created": "2022-07-01", "title": "Global stocks and capacity of mineral-associated soil organic carbon", "description": "Abstract<p>Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1\uffe2\uff80\uff89m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world\uffe2\uff80\uff99s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.</p", "keywords": ["Carbon sequestration", "550", "Permafrost", "/704/106/47/4113", "Carbon Dynamics in Peatland Ecosystems", "Digital Soil Mapping Techniques", "Oceanography", "01 natural sciences", "Agricultural and Biological Sciences", "Soil", "Soil water", "Carbon fibers", "Climate change", "2. Zero hunger", "Minerals", "Ecology", "Forestry Sciences", "Q", "Total organic carbon", "article", "Life Sciences", "Composite number", "Geology", "Agriculture", "/704/106/694/682", "Soil carbon", "Chemistry", "/704/47/4113", "CESD-Soil Quality", "Physical Sciences", "Environmental chemistry", "Engineering sciences. Technology", "Composite material", "/141", "Carbon Sequestration", "Environmental Engineering", "Life on Land", "Science", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Veterinary and Food Sciences", "Soil Science", "/704/106/694/1108", "Environmental science", "Article", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "Soil Carbon Sequestration", "Biology", "0105 earth and related environmental sciences", "Soil science", "Agricultural", "Soil organic matter", "FOS: Environmental engineering", "Soil Properties", "FOS: Earth and related environmental sciences", "15. Life on land", "Materials science", "Carbon", "Carbon dioxide", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "/119", "Climate Change Impacts and Adaptation", "Environmental Sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41467-022-31540-9.pdf"}, {"href": "https://escholarship.org/content/qt2vm0b30s/qt2vm0b30s.pdf"}, {"href": "https://doi.org/10067/1897670151162165141"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10067/1897670151162165141", "name": "item", "description": "10067/1897670151162165141", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10067/1897670151162165141"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-01T00:00:00Z"}}, {"id": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:27Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/10459.1/60556"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10459.1/60556", "name": "item", "description": "10459.1/60556", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10459.1/60556"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "11585/910145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:41Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving  Earth system science for over a decade", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.</p></article>", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "SMOS BRIGHTNESS TEMPERATURE", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Geology", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "HEIHE RIVER-BASIN", "Earth and Planetary Sciences", "Physical Sciences", "Water Resources", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "0406 Physical Geography and Environmental Geoscience", "name=Earth and Planetary Sciences (miscellaneous)", "3709 Physical geography and environmental geoscience", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "SPATIAL VARIABILITY", "IN-SITU MEASUREMENTS", "0207 environmental engineering", "Epistemology", "0905 Civil Engineering", "Environmental science", "G", "Database", "LAND DATA ASSIMILATION", "Soil Moisture; network", "WIRELESS SENSOR NETWORK", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Science & Technology", "3707 Hydrology", "Consecutive dry days", "LEAF-AREA INDEX", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "ANTECEDENT WETNESS CONDITIONS", "Globe", "Computer science", "Environmental sciences", "QE Geology", "0907 Environmental Engineering", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "4013 Geomatic engineering", "soil moisture", "CONSECUTIVE DRY DAYS", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/11585/910145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11585/910145", "name": "item", "description": "11585/910145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11585/910145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "1959.7/uws:76472", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:54Z", "type": "Journal Article", "created": "2024-03-16", "title": "Urban greenspaces and nearby natural areas support similar levels of soil ecosystem services", "description": "Abstract<p>Greenspaces are important for sustaining healthy urban environments and their human populations. Yet their capacity to support multiple ecosystem services simultaneously (multiservices) compared with nearby natural ecosystems remains virtually unknown. We conducted a global field survey in 56 urban areas to investigate the influence of urban greenspaces on 23 soil and plant attributes and compared them with nearby natural environments. We show that, in general, urban greenspaces and nearby natural areas support similar levels of soil multiservices, with only six of 23 attributes (available phosphorus, water holding capacity, water respiration, plant cover, arbuscular mycorrhizal fungi (AMF), and arachnid richness) significantly greater in greenspaces, and one (available ammonium) greater in natural areas. Further analyses showed that, although natural areas and urban greenspaces delivered a similar number of services at low (&gt;25% threshold) and moderate (&gt;50%) levels of functioning, natural systems supported significantly more functions at high (&gt;75%) levels of functioning. Management practices (mowing) played an important role in explaining urban ecosystem services, but there were no effects of fertilisation or irrigation. Some services declined with increasing site size, for both greenspaces and natural areas. Our work highlights the fact that urban greenspaces are more similar to natural environments than previously reported and underscores the importance of managing urban greenspaces not only for their social and recreational values, but for supporting multiple ecosystem services on which soils and human well-being depends.</p", "keywords": ["Medio ambiente natural", "2410.05 Ecolog\u00eda Humana", "Health", " Toxicology and Mutagenesis", "0211 other engineering and technologies", "710", "Urban Green Space", "02 engineering and technology", "01 natural sciences", "zelene povr\u0161ine", "Urban planning", "Natural (archaeology)", "11. Sustainability", "Urban Heat Islands and Mitigation Strategies", "info:eu-repo/classification/udc/630*1:630*9", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "2417.13 Ecolog\u00eda Vegetal", "Carbon cycle", "3. Good health", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Archaeology", "Physical Sciences", "urban forests", "HT361-384", "Ecolog\u00eda (Biolog\u00eda)", "Urbanization. City and country", "Environmental Engineering", "711.4:911.375", "631.4", "Environmental science", "soil", "12. Responsible consumption", "Impact of Urban Green Space on Public Health", "Urban ecosystem", "XXXXXX - Unknown", "Ecosystem services", "14. Life underwater", "Agroforestry", "Biology", "City planning", "Ecosystem", "0105 earth and related environmental sciences", "SDG-15: Life on land", "tla", "FOS: Environmental engineering", "15. Life on land", "ekosistemske storitve", "Urban ecology", "HT165.5-169.9", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "urbani gozdovi", "ecosystem services", "502.3"]}, "links": [{"href": "https://www.nature.com/articles/s42949-024-00154-z.pdf"}, {"href": "https://doi.org/1959.7/uws:76472"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/npj%20Urban%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:76472", "name": "item", "description": "1959.7/uws:76472", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:76472"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-16T00:00:00Z"}}, {"id": "2767588274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:23Z", "type": "Journal Article", "created": "2017-11-10", "title": "Disaggregation of SMOS Soil Moisture to 100 m Resolution Using MODIS Optical/Thermal and Sentinel-1 Radar Data: Evaluation over a Bare Soil Site in Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (\u03c3\u00b0). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of \u03c3\u00b0 and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of \u03c3\u00b0 ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of \u03c3\u00b0 where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD= 0.032 m3 m\u22123).</p></article>", "keywords": ["soil moisture and ocean salinity satellite (SMOS)", "Atmospheric Science", "Artificial intelligence", "Environmental Engineering", "550", "Science", "Soil Moisture", "0211 other engineering and technologies", "Aerospace Engineering", "FOS: Mechanical engineering", "02 engineering and technology", "01 natural sciences", "Environmental science", "[SDU] Sciences of the Universe [physics]", "Engineering", "Meteorology", "DISPATCH", "Image resolution", "Arctic Permafrost Dynamics and Climate Change", "14. Life underwater", "Moisture", "0105 earth and related environmental sciences", "Soil science", "Water content", "Radar", "Geography", "soil moisture and ocean salinity satellite (SMOS); DISPATCH; radar; Sentinel-1; disaggregation; soil moisture", "Soilmoisture and ocean salinity satellite (SMOS)", "Synthetic Aperture Radar Interferometry", "Q", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Earth and Planetary Sciences", "Groundwater Extraction", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "disaggregation", "Environmental Science", "Physical Sciences", "Sentinel-1", "soil moisture", "radar"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/11/1155/pdf"}, {"href": "https://doi.org/2767588274"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2767588274", "name": "item", "description": "2767588274", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2767588274"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-10T00:00:00Z"}}, {"id": "2940609395", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:29Z", "type": "Journal Article", "created": "2019-04-23", "title": "An evapotranspiration model self-calibrated from remotely sensed surface soil moisture, land surface temperature and vegetation cover fraction: application to disaggregated SMOS and MODIS data", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Thermal-based two-source energy balance modeling is very useful for estimating the land evapotranspiration (ET) at a wide range of spatial and temporal scales. However, the land surface temperature (LST) is not sufficient for constraining simultaneously both soil and vegetation flux components in such a way that assumptions (on either the soil or the vegetation fluxes) are commonly required. To avoid such assumptions, a new energy balance model (TSEB-SM) was recently developed in Ait Hssaine et al. (2018a) to integrate the microwave-derived near-surface soil moisture (SM), in addition to the thermal-derived LST and vegetation cover fraction (fc). Whereas, TSEB-SM has been recently tested using in-situ measurements, the objective of this paper is to evaluate the performance of TSEB-SM in real-life using 1\u2009km resolution MODIS (Moderate resolution imaging spectroradiometer) LST and fc data and the 1\u2009km resolution SM data disaggregated from SMOS (Soil Moisture and Ocean Salinity) observations by using DisPATCh. The approach is applied during a four-year period (2014\u20132018) over a rainfed wheat field in the Tensift basin, central Morocco, during a four-year period (2014\u20132018). The field was seeded for the 2014\u20132015 (S1), 2016\u20132017 (S2) and 2017\u20132018 (S3) agricultural season, while it was not ploughed (remained as bare soil) during the 2015\u20132016 (B1) agricultural season. The mean retrieved values of (arss, brss) calculated for the entire study period using satellite data are (7.32, 4.58). The daily calibrated \u03b1PT ranges between 0 and 1.38 for both S1 and S2. Its temporal variability is mainly attributed to the rainfall distribution along the agricultural season. For S3, the daily retrieved \u03b1PT remains at a mostly constant value (\u223c\u20090.7) throughout the study period, because of the lack of clear sky disaggregated SM and LST observations during this season. Compared to eddy covariance measurements, TSEB driven only by LST and fc data significantly overestimates latent heat fluxes for the four seasons. The overall mean bias values are 119, 94, 128 and 181\u2009W/m2 for S1, S2, S3 and B1 respectively. In contrast, these errors are much reduced when using TSEB-SM (SM and LST combined data) with the mean bias values estimated as 39, 4, 7 and 62\u2009W/m2 for S1, S2, S3 and B1 respectively.                         </p></article>", "keywords": ["Technology", "Atmospheric sciences", "550", "Soil Moisture", "0208 environmental biotechnology", "02 engineering and technology", "Environmental technology. Sanitary engineering", "01 natural sciences", "Engineering", "Geography. Anthropology. Recreation", "Pathology", "GE1-350", "TD1-1066", "2. Zero hunger", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "T", "Soil Water Retention", "Moderate-resolution imaging spectroradiometer", "Hydrology (agriculture)", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Aerospace engineering", "Physical Sciences", "Medicine", "environment", "Vegetation (pathology)", "Latent heat", "Mechanics and Transport in Unsaturated Soils", "Land cover", "Environmental Engineering", "0207 environmental engineering", "Energy balance", "Thermal Effects on Soil", "Environmental science", "[SDU] Sciences of the Universe [physics]", "G", "Meteorology", "Civil engineering", "14. Life underwater", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Biology", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Land use", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "FOS: Civil engineering"]}, "links": [{"href": "https://hess.copernicus.org/articles/24/1781/2020/hess-24-1781-2020.pdf"}, {"href": "https://doi.org/2940609395"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2940609395", "name": "item", "description": "2940609395", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2940609395"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-23T00:00:00Z"}}, {"id": "2966009560", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:30Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/2966009560"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2966009560", "name": "item", "description": "2966009560", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2966009560"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "3111070593", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:42Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region &gt; western region &gt; northeast region &gt; central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Distribution (mathematics)", "Statistics", "FOS: Environmental engineering", "Spatial analysis", "Agriculture", "Remote sensing", "15. Life on land", "Economics", " Econometrics and Finance", "Driving factors", "Archaeology", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Spatial heterogeneity", "Common spatial pattern", "Mathematics"]}, "links": [{"href": "https://doi.org/3111070593"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cleaner%20Production", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3111070593", "name": "item", "description": "3111070593", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3111070593"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-01T00:00:00Z"}}, {"id": "34998760", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:57Z", "type": "Journal Article", "created": "2022-01-06", "title": "Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China", "description": "Open AccessLe d\u00e9veloppement d'un syst\u00e8me pr\u00e9cis de pr\u00e9diction du rendement des cultures \u00e0 grande \u00e9chelle est d'une importance primordiale pour la gestion des ressources agricoles et la s\u00e9curit\u00e9 alimentaire mondiale. L'observation de la Terre fournit une source unique d'informations pour surveiller les cultures \u00e0 partir d'une diversit\u00e9 de gammes spectrales. Cependant, l'utilisation int\u00e9gr\u00e9e de ces donn\u00e9es et de leurs valeurs dans la pr\u00e9diction du rendement des cultures est encore peu \u00e9tudi\u00e9e. Ici, nous avons propos\u00e9 la combinaison de donn\u00e9es environnementales (climat, sol, g\u00e9ographie et topographie) avec de multiples donn\u00e9es satellitaires (indices de v\u00e9g\u00e9tation optiques, fluorescence induite par le soleil (SIF), temp\u00e9rature de surface du sol (LST) et profondeur optique de la v\u00e9g\u00e9tation micro-ondes (VOD)) dans le cadre pour estimer le rendement des cultures de ma\u00efs, de riz et de soja dans le nord-est de la Chine, et leur valeur unique et leur influence relative sur la pr\u00e9diction du rendement ont \u00e9t\u00e9 \u00e9valu\u00e9es. Deux m\u00e9thodes de r\u00e9gression lin\u00e9aire, trois m\u00e9thodes d'apprentissage automatique (ML) et un mod\u00e8le d'ensemble ML ont \u00e9t\u00e9 adopt\u00e9s pour construire des mod\u00e8les de pr\u00e9diction de rendement. Les r\u00e9sultats ont montr\u00e9 que les m\u00e9thodes individuelles de ML surpassaient les m\u00e9thodes de r\u00e9gression lin\u00e9aire, le mod\u00e8le d'ensemble de ML a encore am\u00e9lior\u00e9 les mod\u00e8les de ML uniques. De plus, les mod\u00e8les avec plus d'intrants ont obtenu de meilleures performances, la combinaison de donn\u00e9es satellitaires avec des donn\u00e9es environnementales, qui expliquaient respectivement 72\u00a0%, 69\u00a0% et 57\u00a0% de la variabilit\u00e9 du rendement du ma\u00efs, du riz et du soja, a d\u00e9montr\u00e9 des performances de pr\u00e9diction du rendement sup\u00e9rieures \u00e0 celles des intrants individuels. Alors que les donn\u00e9es satellitaires ont contribu\u00e9 \u00e0 la pr\u00e9diction du rendement des cultures principalement au d\u00e9but de la pointe de la saison de croissance, les donn\u00e9es climatiques ont fourni des informations suppl\u00e9mentaires principalement \u00e0 la pointe de la fin de la saison. Nous avons \u00e9galement constat\u00e9 que l'utilisation combin\u00e9e de l'IVE, du LST et du SIF a am\u00e9lior\u00e9 la pr\u00e9cision du mod\u00e8le par rapport au mod\u00e8le d'IVE de r\u00e9f\u00e9rence. Cependant, les indices de v\u00e9g\u00e9tation bas\u00e9s sur l'optique partageaient des informations similaires et ne fournissaient pas beaucoup d'informations suppl\u00e9mentaires au-del\u00e0 de l'IVE. Les pr\u00e9visions de rendement en cours de saison ont montr\u00e9 que les rendements des cultures peuvent \u00eatre pr\u00e9vus de mani\u00e8re satisfaisante deux \u00e0 trois mois avant la r\u00e9colte. La g\u00e9ographie, la topographie, la VOD, l'IVE, les param\u00e8tres hydrauliques du sol et les param\u00e8tres nutritifs sont plus importants pour la pr\u00e9diction du rendement des cultures.", "keywords": ["Atmospheric sciences", "Climate", "Multi-source satellite data", "Normalized Difference Vegetation Index", "Engineering", "Pathology", "Climate change", "Urban Heat Islands and Mitigation Strategies", "Linear regression", "2. Zero hunger", "Global and Planetary Change", "Vegetation Monitoring", "Ecology", "Geography", "Statistics", "Agriculture", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Aerospace engineering", "Archaeology", "Physical Sciences", "Metallurgy", "Medicine", "Seasons", "Global Vegetation Models", "Biomass Estimation", "Regression analysis", "Vegetation (pathology)", "Crops", " Agricultural", "Environmental Engineering", "Environmental data", "Yield (engineering)", "Zea mays", "Environmental science", "Machine learning", "FOS: Mathematics", "Crop yield", "Biology", "Global Forest Drought Response and Climate Change", "FOS: Environmental engineering", "Predictive modelling", "Food security", "FOS: Earth and related environmental sciences", "15. Life on land", "Agronomy", "Materials science", "Yield prediction", "Satellite", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Mathematics"], "contacts": [{"organization": "Zhenwang Li, Lei Ding, Donghui Xu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/34998760"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "34998760", "name": "item", "description": "34998760", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/34998760"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Environmental+engineering&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Environmental+engineering&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Environmental+engineering&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=FOS%3A+Environmental+engineering&offset=27", "hreflang": "en-US"}], "numberMatched": 27, "numberReturned": 27, "distributedFeatures": [], "timeStamp": "2026-05-25T14:20:08.999632Z"}