{"type": "FeatureCollection", "features": [{"id": "10.1371/journal.pone.0092985", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:19Z", "type": "Journal Article", "created": "2014-03-25", "title": "Comparison Of Seasonal Soil Microbial Process In Snow-Covered Temperate Ecosystems Of Northern China", "description": "Open AccessMore than half of the earth's terrestrial surface currently experiences seasonal snow cover and soil frost. Winter compositional and functional investigations in soil microbial community are frequently conducted in alpine tundra and boreal forest ecosystems. However, little information on winter microbial biogeochemistry is known from seasonally snow-covered temperate ecosystems. As decomposer microbes may differ in their ability/strategy to efficiently use soil organic carbon (SOC) within different phases of the year, understanding seasonal microbial process will increase our knowledge of biogeochemical cycling from the aspect of decomposition rates and corresponding nutrient dynamics. In this study, we measured soil microbial biomass, community composition and potential SOC mineralization rates in winter and summer, from six temperate ecosystems in northern China. Our results showed a clear pattern of increased microbial biomass C to nitrogen (N) ratio in most winter soils. Concurrently, a shift in soil microbial community composition occurred with higher fungal to bacterial biomass ratio and gram negative (G-) to gram positive (G+) bacterial biomass ratio in winter than in summer. Furthermore, potential SOC mineralization rate was higher in winter than in summer. Our study demonstrated a distinct transition of microbial community structure and function from winter to summer in temperate snow-covered ecosystems. Microbial N immobilization in winter may not be the major contributor for plant growth in the following spring.", "keywords": ["Biomass (ecology)", "Atmospheric Science", "Microbial population biology", "Decomposer", "Nutrient cycle", "Physical Phenomena", "Agricultural and Biological Sciences", "Soil", "Terrestrial ecosystem", "Snow", "Soil water", "Biomass", "Phospholipids", "Soil Microbiology", "Minerals", "Glucan 1", "4-beta-Glucosidase", "Ecology", "Geography", "Mineralization (soil science)", "Q", "R", "Life Sciences", "04 agricultural and veterinary sciences", "Biogeochemistry", "16. Peace & justice", "Earth and Planetary Sciences", "Physical Sciences", "Medicine", "Seasons", "Ecosystem Functioning", "Research Article", "China", "Nitrogen", "Science", "Soil Science", "Biogeochemical cycle", "Environmental science", "Meteorology", "Genetics", "Arctic Permafrost Dynamics and Climate Change", "Tundra", "Biology", "Ecosystem", "Soil science", "Bacteria", "Fungi", "Microbial Diversity in Antarctic Ecosystems", "15. Life on land", "Carbon", "Temperate climate", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems"], "contacts": [{"organization": "Xinyue Zhang, Wei Wang, Weile Chen, Naili Zhang, Hui Zeng,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0092985"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLoS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0092985", "name": "item", "description": "10.1371/journal.pone.0092985", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0092985"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-03-25T00:00:00Z"}}, {"id": "10.1371/journal.pone.0153415", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:19:20Z", "type": "Journal Article", "created": "2016-04-12", "title": "Seasonality, Rather Than Nutrient Addition Or Vegetation Types, Influenced Short-Term Temperature Sensitivity Of Soil Organic Carbon Decomposition", "description": "Open AccessLa r\u00e9ponse de la respiration microbienne de la d\u00e9composition du carbone organique du sol (COS) aux changements environnementaux joue un r\u00f4le cl\u00e9 dans la pr\u00e9diction des tendances futures de la concentration de CO2 atmosph\u00e9rique. Cependant, il n'est pas certain qu'il existe une tendance universelle dans la r\u00e9ponse de la respiration microbienne \u00e0 l'augmentation de la temp\u00e9rature et \u00e0 l'ajout de nutriments parmi les diff\u00e9rents types de v\u00e9g\u00e9tation. Dans cette \u00e9tude, les sols ont \u00e9t\u00e9 \u00e9chantillonn\u00e9s au printemps, en \u00e9t\u00e9, en automne et en hiver \u00e0 partir de cinq types de v\u00e9g\u00e9tation dominants, y compris les for\u00eats de pins, de m\u00e9l\u00e8zes et de bouleaux, les arbustes et les prairies, dans la r\u00e9gion de Saihanba, dans le nord de la Chine. Les \u00e9chantillons de sol de chaque saison ont \u00e9t\u00e9 incub\u00e9s \u00e0 1, 10 et 20 \u00b0C pendant 5 \u00e0 7 jours. L'azote (N\u00a0; 0,035 mM sous forme de NH4NO3) et le phosphore (P\u00a0; 0,03 mM sous forme de P2O5) ont \u00e9t\u00e9 ajout\u00e9s aux \u00e9chantillons de sol, et les r\u00e9ponses de la respiration microbienne du sol \u00e0 l'augmentation de la temp\u00e9rature et \u00e0 l'ajout de nutriments ont \u00e9t\u00e9 d\u00e9termin\u00e9es. Nous avons constat\u00e9 une tendance universelle selon laquelle la respiration microbienne du sol augmentait avec l'augmentation de la temp\u00e9rature, ind\u00e9pendamment de la saison d'\u00e9chantillonnage ou du type de v\u00e9g\u00e9tation. La sensibilit\u00e9 \u00e0 la temp\u00e9rature (indiqu\u00e9e par Q10, l'augmentation du taux de respiration avec une augmentation de 10\u00b0C de la temp\u00e9rature) de la respiration microbienne \u00e9tait plus \u00e9lev\u00e9e au printemps et en automne qu'en \u00e9t\u00e9 et en hiver, quel que soit le type de v\u00e9g\u00e9tation. Le Q10 \u00e9tait significativement corr\u00e9l\u00e9 positivement avec la biomasse microbienne et le rapport champignon\u00a0: bact\u00e9rie. La respiration microbienne (ou Q10) n'a pas r\u00e9pondu de mani\u00e8re significative \u00e0 l'addition d'azote ou de phosphore. Nos r\u00e9sultats sugg\u00e8rent que l'apport en nutriments \u00e0 court terme pourrait ne pas modifier le taux de d\u00e9composition du COS ou sa sensibilit\u00e9 \u00e0 la temp\u00e9rature, alors que l'augmentation de la temp\u00e9rature pourrait am\u00e9liorer consid\u00e9rablement la d\u00e9composition du COS au printemps et en automne, par rapport \u00e0 l'hiver et \u00e0 l'\u00e9t\u00e9.", "keywords": ["Biomass (ecology)", "Atmospheric Science", "Microbial population biology", "Larix", "Carbon Dynamics in Peatland Ecosystems", "Forests", "Agricultural and Biological Sciences", "Soil", "Soil water", "Pathology", "Carbon Feedback", "Biomass", "Betula", "Soil Microbiology", "2. Zero hunger", "Ecology", "Q10", "Respiration", "Q", "R", "Temperature", "Life Sciences", "Soil respiration", "04 agricultural and veterinary sciences", "Soil carbon", "Grassland", "Earth and Planetary Sciences", "Physical Sciences", "Respiration rate", "Medicine", "Seasons", "Vegetation (pathology)", "Research Article", "China", "Nitrogen", "Science", "Soil Science", "Environmental science", "Shrubland", "Genetics", "Arctic Permafrost Dynamics and Climate Change", "Soil Carbon Sequestration", "Biology", "Ecosystem", "Soil science", "Soil organic matter", "Soil Fertility", "Bacteria", "Fungi", "Botany", "15. Life on land", "Pinus", "Vegetation Change", "Carbon", "Agronomy", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Growing season", "0401 agriculture", " forestry", " and fisheries", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Nutrient"], "contacts": [{"organization": "Yu-Qi Qian, Fangliang He, Wei Wang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0153415"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0153415", "name": "item", "description": "10.1371/journal.pone.0153415", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0153415"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-04-12T00:00:00Z"}}, {"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": "10.5194/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": "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": "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. 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