{"type": "FeatureCollection", "features": [{"id": "10.1016/j.ecoleng.2017.08.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:17:18Z", "type": "Journal Article", "created": "2017-11-27", "title": "Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters", "description": "Open Access\u0625\u0646 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u063a\u0637\u0627\u0621 \u0627\u0644\u0646\u0628\u0627\u062a\u064a \u0639\u0644\u0649 \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u0644\u0645\u0646\u062d\u062f\u0631\u0627\u062a \u0645\u0641\u0647\u0648\u0645 \u062c\u064a\u062f\u064b\u0627 \u0639\u0644\u0649 \u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0645\u0646\u062d\u062f\u0631\u0627\u062a\u060c \u0644\u0643\u0646 \u0627\u0644\u0627\u0631\u062a\u0642\u0627\u0621 \u0625\u0644\u0649 \u0645\u0633\u062a\u0648\u0649 \u0645\u0633\u062a\u062c\u0645\u0639\u0627\u062a \u0627\u0644\u0645\u064a\u0627\u0647 \u0644\u0627 \u064a\u0632\u0627\u0644 \u064a\u0645\u062b\u0644 \u062a\u062d\u062f\u064a\u064b\u0627\u060c \u0648\u064a\u0631\u062c\u0639 \u0630\u0644\u0643 \u062c\u0632\u0626\u064a\u064b\u0627 \u0625\u0644\u0649 \u0646\u0642\u0635 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0645\u0646\u0627\u0633\u0628\u0629 \u0644\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0646\u0645\u0627\u0630\u062c. \u0627\u062e\u062a\u0628\u0631\u0646\u0627 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u0647\u064a\u0627\u0631\u0627\u062a \u0627\u0644\u0623\u0631\u0636\u064a\u0629 \u0627\u0644\u0645\u0627\u062f\u064a\u0629\u060c LAPSUS_LS\u060c \u0627\u0644\u0630\u064a \u064a\u0635\u0645\u0645 \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0639\u0644\u0649 \u0645\u0642\u064a\u0627\u0633 \u0645\u0633\u062a\u062c\u0645\u0639\u0627\u062a \u0627\u0644\u0645\u064a\u0627\u0647. \u062a\u062c\u0645\u0639 LAPSUS_LS \u0628\u064a\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0647\u064a\u062f\u0631\u0648\u0644\u0648\u062c\u064a \u0648\u0646\u0645\u0648\u0630\u062c \u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062a\u0648\u0627\u0632\u0646 \u0627\u0644\u062d\u062f\u064a\u060c \u0648\u062a\u062d\u0633\u0628 \u0639\u0627\u0645\u0644 \u0633\u0644\u0627\u0645\u0629 \u0627\u0644\u062e\u0644\u0627\u064a\u0627 \u0627\u0644\u0641\u0631\u062f\u064a\u0629 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u062e\u0635\u0627\u0626\u0635\u0647\u0627 \u0627\u0644\u0647\u064a\u062f\u0631\u0648\u0644\u0648\u062c\u064a\u0629 \u0648\u0627\u0644\u062c\u064a\u0648\u0645\u0648\u0631\u0641\u0648\u0644\u0648\u062c\u064a\u0629. \u0627\u062e\u062a\u0628\u0631\u0646\u0627 \u0646\u0648\u0639\u064a\u0646 \u0645\u0646 \u0627\u0644\u0646\u0628\u0627\u062a\u0627\u062a \u0639\u0644\u0649 \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u0644\u0645\u0646\u062d\u062f\u0631\u0627\u062a: (1) \u0632\u0631\u0627\u0639\u0629 \u0627\u0644\u0642\u0647\u0648\u0629 \u0627\u0644\u0623\u062d\u0627\u062f\u064a\u0629 (\u0627\u0644\u0642\u0647\u0648\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629) \u0648 (2) \u0632\u0631\u0627\u0639\u0629 \u0645\u062e\u062a\u0644\u0637\u0629 \u0644\u0644\u0628\u0646 \u0648\u062a\u062c\u0630\u064a\u0631 \u0639\u0645\u064a\u0642 \u0644\u0623\u0634\u062c\u0627\u0631 \u0627\u0644\u0625\u0631\u064a\u062b\u0631\u064a\u0646\u0627 (\u0627\u0644\u0625\u0631\u064a\u062b\u0631\u064a\u0646\u0627 \u0628\u0648\u0628\u064a\u062c\u064a\u0627\u0646\u0627). \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0631\u0628\u0629 \u0648\u0627\u0644\u062c\u0630\u0631 \u0645\u0646 \u0643\u0648\u0633\u062a\u0627\u0631\u064a\u0643\u0627\u060c \u0623\u062c\u0631\u064a\u0646\u0627 \u0639\u0645\u0644\u064a\u0627\u062a \u0645\u062d\u0627\u0643\u0627\u0629 \u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0633\u062a\u062c\u0627\u0628\u0629 LAPSUS_LS \u0644\u062a\u0642\u0648\u064a\u0629 \u0627\u0644\u062c\u0630\u0631 \u0648\u0643\u062b\u0627\u0641\u0629 \u0643\u062a\u0644\u0629 \u0627\u0644\u062a\u0631\u0628\u0629 \u0648\u0627\u0644\u0627\u0646\u062a\u0642\u0627\u0644 \u0648\u0632\u0627\u0648\u064a\u0629 \u0627\u0644\u0627\u062d\u062a\u0643\u0627\u0643 \u0627\u0644\u062f\u0627\u062e\u0644\u064a \u0648\u0639\u0645\u0642 \u0645\u0633\u062a\u0648\u0649 \u0627\u0644\u0642\u0635. \u0639\u0644\u0627\u0648\u0629 \u0639\u0644\u0649 \u0630\u0644\u0643\u060c \u0642\u0645\u0646\u0627 \u0628\u062a\u0639\u062f\u064a\u0644 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u064a\u0634\u0645\u0644 \u062a\u0623\u062b\u064a\u0631 \u0627\u0644\u0631\u0633\u0648\u0645 \u0627\u0644\u0625\u0636\u0627\u0641\u064a\u0629 \u0644\u0644\u0643\u062a\u0644\u0629 \u0627\u0644\u062d\u064a\u0648\u064a\u0629 \u0641\u064a \u0627\u0644\u062d\u0633\u0627\u0628\u0627\u062a. \u062a\u0638\u0647\u0631 \u0627\u0644\u0646\u062a\u0627\u0626\u062c \u0623\u0646 LAPSUS_LS 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\u0648\u0627\u0644\u0623\u0634\u062c\u0627\u0631 \u0639\u0644\u0649 \u0627\u0644\u0645\u0646\u062d\u062f\u0631\u0627\u062a\u060c \u0644\u0643\u0646 \u0627\u0644\u0632\u0631\u0627\u0639\u0629 \u0627\u0644\u0623\u062d\u0627\u062f\u064a\u0629 \u0644\u0644\u0628\u0646 \u0643\u0627\u0646\u062a \u063a\u064a\u0631 \u0645\u0633\u062a\u0642\u0631\u0629 \u0644\u0644\u063a\u0627\u064a\u0629\u060c \u0644\u0623\u0646 \u062a\u0642\u0648\u064a\u0629 \u0627\u0644\u062c\u0630\u0631 \u0643\u0627\u0646\u062a \u0645\u0646\u062e\u0641\u0636\u0629 \u0639\u0644\u0649 \u0639\u0645\u0642 1.5 \u0645\u062a\u0631. \u0643\u0627\u0646 \u0644\u0646\u0642\u0644 \u0627\u0644\u062a\u0631\u0628\u0629 \u062a\u0623\u062b\u064a\u0631 \u0645\u062d\u062f\u0648\u062f \u0639\u0644\u0649 \u0627\u0644\u0646\u062a\u0627\u0626\u062c \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0627\u0644\u0643\u062b\u0627\u0641\u0629 \u0627\u0644\u0633\u0627\u0626\u0628\u0629 \u0648\u0632\u0627\u0648\u064a\u0629 \u0627\u0644\u0627\u062d\u062a\u0643\u0627\u0643 \u0627\u0644\u062f\u0627\u062e\u0644\u064a. \u0644\u0645 \u064a\u0643\u0646 \u0644\u0644\u0631\u0633\u0648\u0645 \u0627\u0644\u0625\u0636\u0627\u0641\u064a\u0629 \u0644\u0644\u0643\u062a\u0644\u0629 \u0627\u0644\u062d\u064a\u0648\u064a\u0629 \u0623\u064a \u062a\u0623\u062b\u064a\u0631 \u0643\u0628\u064a\u0631 \u0639\u0644\u0649 \u0639\u0645\u0644\u064a\u0627\u062a \u0627\u0644\u0645\u062d\u0627\u0643\u0627\u0629. \u0641\u064a \u0627\u0644\u062e\u062a\u0627\u0645\u060c \u0627\u0633\u062a\u062c\u0627\u0628\u062a LAPSUS_LS \u0628\u0634\u0643\u0644 \u062c\u064a\u062f \u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0645\u062f\u062e\u0644\u0627\u062a \u0627\u0644\u062a\u0631\u0628\u0629 \u0648\u0627\u0644\u063a\u0637\u0627\u0621 \u0627\u0644\u0646\u0628\u0627\u062a\u064a\u060c \u0648\u0647\u064a \u0645\u0631\u0634\u062d \u0645\u0646\u0627\u0633\u0628 \u0644\u0646\u0645\u0630\u062c\u0629 \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0627\u0644\u0645\u0646\u062d\u062f\u0631\u0627\u062a \u0627\u0644\u0646\u0628\u0627\u062a\u064a\u0629 \u0639\u0644\u0649 \u0645\u0633\u062a\u0648\u0649 \u0645\u0633\u062a\u062c\u0645\u0639\u0627\u062a \u0627\u0644\u0645\u064a\u0627\u0647.", "keywords": ["Cohesion (chemistry)", "http://aims.fao.org/aos/agrovoc/c_27199", "http://aims.fao.org/aos/agrovoc/c_4915", "F08 - Syst\u00e8mes et modes de culture", "[SDV]Life Sciences [q-bio]", "culture associ\u00e9e", "http://aims.fao.org/aos/agrovoc/c_1920", "FOS: Mechanical engineering", "Organic chemistry", "Plant Science", "02 engineering and technology", "Erythrina poeppigiana", "01 natural sciences", "630", "Mechanical Effects of Plant Roots on Slope Stability", "stabilisation du sol", "Agricultural and Biological Sciences", "Soil", "monoculture", "Engineering", "enracinement", "couverture du sol", "m\u00e9thode statistique", "Pathology", "Monoculture", "http://aims.fao.org/aos/agrovoc/c_1721", "http://aims.fao.org/aos/agrovoc/c_2018", "http://aims.fao.org/aos/agrovoc/c_24199", "http://aims.fao.org/aos/agrovoc/c_35927", "U10 - Informatique", " math\u00e9matiques et statistiques", "Susceptibility Mapping", "Life Sciences", "Hydrology (agriculture)", "Geology", "Coffea arabica", "[SDV] Life Sciences [q-bio]", "Chemistry", "Landslide", "Plant Responses to Flooding Stress", "Slope Stability", "Physical Sciences", "http://aims.fao.org/aos/agrovoc/c_6649", "Medicine", "Vegetation (pathology)", "http://aims.fao.org/aos/agrovoc/c_7377", "http://aims.fao.org/aos/agrovoc/c_7171", "0207 environmental engineering", "Soil Science", "Management", " Monitoring", " Policy and Law", "Transmissivity", "Environmental science", "mod\u00e8le math\u00e9matique", "FOS: Mathematics", "http://aims.fao.org/aos/agrovoc/c_12676", "http://aims.fao.org/aos/agrovoc/c_37897", "Landslide Hazards and Risk Assessment", "pratique culturale", "Biology", "0105 earth and related environmental sciences", "P36 - \u00c9rosion", " conservation et r\u00e9cup\u00e9ration des sols", "Soil science", "montagne", "Mechanical Engineering", "Slope stability", "Modeling", "Botany", "FOS: Earth and related environmental sciences", "15. Life on land", "Roots", "Bulk density", "Agronomy", "Geotechnical engineering", "13. Climate action", "Environmental Science", "Cohesion", "Mathematics"]}, "links": [{"href": "https://doi.org/10.1016/j.ecoleng.2017.08.010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecological%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ecoleng.2017.08.010", "name": "item", "description": "10.1016/j.ecoleng.2017.08.010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ecoleng.2017.08.010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "10.1029/2022je007190", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:19:14Z", "type": "Journal Article", "created": "2022-01-25", "title": "InSight Pressure Data Recalibration, and Its Application to the Study of Long-Term Pressure Changes on Mars", "description": "Abstract<p>Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40\uffc2\uffa0years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than \uffc2\uffb18\uffc2\uffa0Pa in the CO2 cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40\uffc2\uffa0year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.</p>", "keywords": ["Atmospheric sciences", "550", "Astronomy", "Atmosphere (unit)", "FOS: Mechanical engineering", "Library science", "Oceanography", "01 natural sciences", "CO<SUB>2</SUB> ice", "pressure", "Mars Exploration Program", "Engineering", "Surface pressure", "Storm", "Martian Climate", "Space Suit Design and Ergonomics for EVA", "Martian Atmosphere", "Earth and Planetary Astrophysics (astro-ph.EP)", "Climatology", "Global and Planetary Change", "Geography", "Martian Surface", "Physics", "Geology", "Impact crater", "Condensed matter physics", "Anomaly (physics)", "World Wide Web", "Algorithm", "Satellite Observations", "Residual", "Physical Sciences", "Exploration and Study of Mars", "Astrophysics - Instrumentation and Methods for Astrophysics", "Research Article", "FOS: Physical sciences", "Mars", "Aerospace Engineering", "Pressure gradient", "Environmental science", "[SDU] Sciences of the Universe [physics]", "atmospheric mass", "Meteorology", "Orbiter", "0103 physical sciences", "Instrumentation and Methods for Astrophysics (astro-ph.IM)", "Formation and Evolution of the Solar System", "0105 earth and related environmental sciences", "Pressure system", "CO 2 ice", "Astronomy and Astrophysics", "FOS: Earth and related environmental sciences", "Astrobiology", "Computer science", "Physics and Astronomy", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Global Methane Emissions and Impacts", "Environmental Science", "cap sublimation", "Water on Mars", "Astrophysics - Earth and Planetary Astrophysics"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2022JE007190"}, {"href": "https://doi.org/10.1029/2022je007190"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Planets", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2022je007190", "name": "item", "description": "10.1029/2022je007190", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2022je007190"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-25T00:00:00Z"}}, {"id": "10.3390/rs9111155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:23:36Z", "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/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/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:30Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10459.1/60556", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:28Z", "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": "2767588274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:45Z", "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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