{"type": "FeatureCollection", "features": [{"id": "10.5194/bg-14-1969-2017", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:07Z", "type": "Journal Article", "created": "2016-11-28", "title": "Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe", "description": "<p>Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf level chlorophyll fluorescence measurements (ChlF) to evaluate the performance of the SIF module at a coniferous forest at Hyyti\uffc3\uffa4l\uffc3\uffa4, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. The GOME-2 based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation), even though high SIF values occurred during early spring at the northern latitudes, although these are not likely to be associated with photosynthesis.                         </p>", "keywords": ["EDDY COVARIANCE", "DATA ASSIMILATION SYSTEM", "FLUX MEASUREMENTS", "SCOTS PINE FOREST", "01 natural sciences", "7. Clean energy", "Ecology", " Evolution", " Behavior and Systematics; Earth-Surface Processes", "CO2 EXCHANGE", "PHOTOSYSTEM-II", "Life", "QH501-531", "QH540-549.5", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "QE1-996.5", "Ecology", "BOREAL CONIFEROUS FOREST", "BIOCHEMICAL-MODEL", "Forestry", "Geology", "15. Life on land", "TERRESTRIAL CHLOROPHYLL FLUORESCENCE", "Physical sciences", "Environmental sciences", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "ENERGY-BALANCE", "ITC-GOLD"]}, "links": [{"href": "https://cris.unibo.it/bitstream/11585/585578/2/bg-14-1969-2017.pdf"}, {"href": "https://bg.copernicus.org/articles/14/1969/2017/bg-14-1969-2017.pdf"}, {"href": "https://doi.org/10.5194/bg-14-1969-2017"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-14-1969-2017", "name": "item", "description": "10.5194/bg-14-1969-2017", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-14-1969-2017"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-28T00:00:00Z"}}, {"id": "10.3390/rs13234893", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:34Z", "type": "Journal Article", "created": "2021-12-06", "title": "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model", "description": "<p>The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\uffe2\uff88\uff923 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\uffe2\uff88\uff923 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.</p>", "keywords": ["feature importance", "Science", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "antecedent precipitation index", "SDG 13 - Climate Action", "Global scale", "Antecedent precipitation index; Feature importance; Global scale; In situ constrained; Random forest; Soil moisture", "soil moisture; random forest; global scale; in situ constrained; feature importance; antecedent precipitation index", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "Antecedent precipitation index", "Q", "In situ constrained", "15. Life on land", "Feature importance", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Soil moisture", "soil moisture", "ITC-GOLD", "in situ constrained", "random forest", "Random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/938135/1/2021_Ljie_Zeng_et_al_remotesensing.pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://doi.org/10.3390/rs13234893"}, {"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/rs13234893", "name": "item", "description": "10.3390/rs13234893", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13234893"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:19Z", "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": "11585/910145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:10Z", "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": "11588/938135", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:11Z", "type": "Journal Article", "created": "2021-12-06", "title": "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\u22123 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\u22123 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.</p></article>", "keywords": ["feature importance", "Antecedent precipitation index", "Science", "Q", "0207 environmental engineering", "In situ constrained", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Feature importance", "antecedent precipitation index", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Global scale", "Antecedent precipitation index; Feature importance; Global scale; In situ constrained; Random forest; Soil moisture", "Soil moisture", "soil moisture", "ITC-GOLD", "in situ constrained", "soil moisture; random forest; global scale; in situ constrained; feature importance; antecedent precipitation index", "random forest", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/938135/1/2021_Ljie_Zeng_et_al_remotesensing.pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://doi.org/11588/938135"}, {"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": "11588/938135", "name": "item", "description": "11588/938135", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11588/938135"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "20.500.11850/688246", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:31Z", "type": "Journal Article", "created": "2024-07-29", "title": "Hydro-pedotransfer functions: a roadmap for future development", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Hydro-pedotransfer functions\u00a0(PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties\u00a0(SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards\u2013Richardson equation are mostly limited to predicting parameters of the van\u00a0Genuchten\u2013Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium\u00a0(ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.</p></article>", "keywords": ["Technology", "550", "Bodenanalyse", "Modell", "SPHAGNUM MOSS", "Environmental technology. Sanitary engineering", "630", "Ing\u00e9nierie", " informatique & technologie", "Biogeochemical process", "Earth and Planetary Sciences (miscellaneous)", "Geography. Anthropology. Recreation", "GE1-350", "SATURATED HYDRAULIC CONDUCTIVITY", "Geosciences", " Multidisciplinary", "TD1-1066", "Water Science and Technology", "2. Zero hunger", "T", "Geology", "Hydraulics effects", "Agriculture & agronomy", "Life sciences", "Daten", "Pedo-transfer functions", "6. Clean water", "Soil hydraulics", "REFLECTANCE SPECTROSCOPY", "Roadmap", "Physical Sciences", "Sciences du vivant", "Water Resources", "SOIL-WATER-RETENTION", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience", "Process-based modeling", "Environmental Engineering", "Physique", " chimie", " math\u00e9matiques & sciences de la terre", "PHYSICAL-PROPERTIES", "SENSITIVITY-ANALYSIS", "Soil hydraulic properties", "0905 Civil Engineering", "333", "G", "Physical", " chemical", " mathematical & earth Sciences", "Empirical model", "Agriculture & agronomie", "Life Science", "UNSATURATED CONDUCTIVITY", "SEASONAL-CHANGES", "Pedotransfer functions", "HYSTERETIC MOISTURE PROPERTIES", "info:eu-repo/classification/ddc/550", "Science & Technology", "3707 Hydrology", "Physikochemische Bodeneigenschaft", "500", "15. Life on land", "Engineering", " computing & technology", "Sciences de la terre & g\u00e9ographie physique", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "Earth sciences & physical geography", "HETEROGENEOUS SOILS", "4013 Geomatic engineering", "ITC-GOLD", "Hydrological process"]}, "links": [{"href": "https://orbi.uliege.be/bitstream/2268/321088/1/hess-28-3391-2024.pdf"}, {"href": "https://hess.copernicus.org/articles/28/3391/2024/hess-28-3391-2024.pdf"}, {"href": "https://doi.org/20.500.11850/688246"}, {"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": "20.500.11850/688246", "name": "item", "description": "20.500.11850/688246", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/688246"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-29T00:00:00Z"}}, {"id": "3216678516", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:19Z", "type": "Journal Article", "created": "2021-12-06", "title": "In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m\u22123 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m\u22123 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally.</p></article>", "keywords": ["feature importance", "Science", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "antecedent precipitation index", "SDG 13 - Climate Action", "Global scale", "Antecedent precipitation index; Feature importance; Global scale; In situ constrained; Random forest; Soil moisture", "soil moisture; random forest; global scale; in situ constrained; feature importance; antecedent precipitation index", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "Antecedent precipitation index", "Q", "In situ constrained", "15. Life on land", "Feature importance", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Soil moisture", "soil moisture", "ITC-GOLD", "in situ constrained", "random forest", "Random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/938135/1/2021_Ljie_Zeng_et_al_remotesensing.pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/23/4893/pdf"}, {"href": "https://doi.org/3216678516"}, {"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": "3216678516", "name": "item", "description": "3216678516", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3216678516"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "PMC11085199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:27:06Z", "type": "Journal Article", "created": "2024-04-29", "title": "Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 \u00b1 0.12; NDVI RMSE: 0.43 \u00b1 0.095) and power (NDWI RMSE: 0.44 \u00b1 0.116; NDVI RMSE: 0.44 \u00b1 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.</p></article>", "keywords": ["2. Zero hunger", "Botany", "04 agricultural and veterinary sciences", "vegetation index-based K<sub>c</sub>", "15. Life on land", "01 natural sciences", "Article", "6. Clean water", "maize water demand", "ITC-ISI-JOURNAL-ARTICLE", "QK1-989", "vegetation index-based crop evapotranspiration", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "ITC-GOLD", "SDG 2 - Zero Hunger", "SDG 6 - Clean Water and Sanitation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2223-7747/13/9/1212/pdf"}, {"href": "https://doi.org/PMC11085199"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plants", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC11085199", "name": "item", "description": "PMC11085199", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC11085199"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-27T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ITC-GOLD&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=ITC-GOLD&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=ITC-GOLD&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=ITC-GOLD&offset=8", "hreflang": "en-US"}], "numberMatched": 8, "numberReturned": 8, "distributedFeatures": [], "timeStamp": "2026-05-25T14:21:31.031507Z"}