{"type": "FeatureCollection", "features": [{"id": "10.1002/2016rg000543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:13:58Z", "type": "Journal Article", "created": "2017-03-23", "title": "A review of spatial downscaling of satellite remotely sensed soil moisture", "description": "Abstract<p>Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.</p>", "keywords": ["TIME-DOMAIN REFLECTOMETRY", "550", "IN-SITU", "downscaling", "MODIS TOA RADIANCES", "AMSR-E", "15. Life on land", "551", "01 natural sciences", "LAND-SURFACE TEMPERATURE", "REMEDHUS NETWORK SPAIN", "6. Clean water", "3. Good health", "[SDU] Sciences of the Universe [physics]", "L-BAND RADIOMETER", "remote sensing", "EVAPORATIVE FRACTION", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth and Environmental Sciences", "soil moisture", "SOUTHERN GREAT-PLAINS", "spatial resolution", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016RG000543"}, {"href": "https://doi.org/10.1002/2016rg000543"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Reviews%20of%20Geophysics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2016rg000543", "name": "item", "description": "10.1002/2016rg000543", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2016rg000543"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-18T00:00:00Z"}}, {"id": "10.1016/j.rse.2016.02.046", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:35Z", "type": "Journal Article", "created": "2016-03-05", "title": "Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes", "description": "AbstractThis paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas.", "keywords": ["2. Zero hunger", "0207 environmental engineering", "Soil Science", "Agriculture", "Geology", "AMSR-E", "02 engineering and technology", "15. Life on land", "16. Peace & justice", "01 natural sciences", "6. Clean water", "Water management", "ASCAT", "13. Climate action", "Regional scale", "LSM", "Soil moisture", "Computers in Earth Sciences", "Irrigation", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2016.02.046"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2016.02.046", "name": "item", "description": "10.1016/j.rse.2016.02.046", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2016.02.046"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-07-01T00:00:00Z"}}, {"id": "2288664970", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:16Z", "type": "Journal Article", "created": "2016-03-05", "title": "Comparison of remote sensing and simulated soil moisture datasets in Mediterranean landscapes", "description": "AbstractThis paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas.", "keywords": ["2. Zero hunger", "0207 environmental engineering", "Soil Science", "Agriculture", "Geology", "AMSR-E", "02 engineering and technology", "15. Life on land", "16. Peace & justice", "01 natural sciences", "6. Clean water", "Water management", "ASCAT", "13. Climate action", "Regional scale", "LSM", "Soil moisture", "Computers in Earth Sciences", "Irrigation", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2288664970"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2288664970", "name": "item", "description": "2288664970", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2288664970"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-07-01T00:00:00Z"}}, {"id": "10.5194/hess-25-17-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:21:35Z", "type": "Journal Article", "created": "2021-01-04", "title": "Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors", "description": "<p>Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as \uffe2\uff80\uff9copen-loop\uffe2\uff80\uff9d models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byr\uffc3\uffa5ns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5\uffe2\uff80\uff89cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50\uffe2\uff80\uff89% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of\uffc2\uffa00.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.                     </p>", "keywords": ["Technology", "CLIMATE-CHANGE", "550", "GLOBAL-SCALE EVALUATION", "NEAR-SURFACE", "RADIOFREQUENCY INTERFERENCE", "T", "AMSR-E", "4 DECADES", "15. Life on land", "Environmental technology. Sanitary engineering", "01 natural sciences", "6. Clean water", "HEIHE RIVER-BASIN", "AGRICULTURAL SITES", "G", "Environmental sciences", "DATA ASSIMILATION", "13. Climate action", "Earth and Environmental Sciences", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "SMOS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://eprints.soton.ac.uk/471538/1/hess_25_17_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-17-2021"}, {"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-25-17-2021", "name": "item", "description": "10.5194/hess-25-17-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-17-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-19T00:00:00Z"}}, {"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": "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": "1854/LU-8528923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:49Z", "type": "Journal Article", "created": "2017-03-23", "title": "A review of spatial downscaling of satellite remotely sensed soil moisture", "description": "Abstract<p>Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.</p", "keywords": ["TIME-DOMAIN REFLECTOMETRY", "550", "IN-SITU", "downscaling", "MODIS TOA RADIANCES", "AMSR-E", "15. Life on land", "551", "01 natural sciences", "LAND-SURFACE TEMPERATURE", "REMEDHUS NETWORK SPAIN", "6. Clean water", "3. Good health", "[SDU] Sciences of the Universe [physics]", "L-BAND RADIOMETER", "remote sensing", "EVAPORATIVE FRACTION", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth and Environmental Sciences", "soil moisture", "SOUTHERN GREAT-PLAINS", "spatial resolution", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016RG000543"}, {"href": "https://doi.org/1854/LU-8528923"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Reviews%20of%20Geophysics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8528923", "name": "item", "description": "1854/LU-8528923", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8528923"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-18T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=AMSR-E&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=AMSR-E&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=AMSR-E&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=AMSR-E&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-05-25T14:20:44.464081Z"}