{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.10060810", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-08-24T16:19:53Z", "type": "Dataset", "title": "SoilCompDB: Global soil compressive properties database. Version 1.0", "description": "Data collection and processing Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. \u00a0A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. \u00a0After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. \u00a0We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries. Time and place The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.\u00a0\u00a0 Instruments The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamand\u00e9 et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006). Resources Web of Science, Scopus \u2013 literature search Endnote Web \u2013 removal of duplicates Rayyan software \u2013 initial paper selection based on title and abstract WebPlotDigitizer \u2013 data extraction from figures Microsoft Access \u2013 database platform Description of the collected data (column, unit, and description) Sample ID-\u00a0\u00a0\u00a0 A unique identification number assigned to each individual sample within the database\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Study ID- Identification number assigned to each research study in the database Reference - Research paper reference Year - Year of research paper publication \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Language - Language of the research paper \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers Soil classification (original in paper) - Soil classification described in research paper\u00a0 Soil classification (convertion to Soil Taxonomy orders) -\u00a0 Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Location - Study location country\u00a0\u00a0\u00a0 Texture classification (USDA) -\u00a0Soil textural classification according USDA Texture\u00a0 classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)\u00a0 Sand (USDA) - % - Soil sand content (weight based)\u00a0 - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available Clay - g kg-1 - Soil clay content - original in the paper Clay class upper boundary - \u00b5m - The clay class upper boundary informed in source publication Silt - g kg-1 - Silt clay content - original in the paper Silt class upper boundary - \u00b5m - The silt class upper boundary informed in source publication Sand - Soil sand content - original in the paper Sand class upper boundary - \u00b5m - The sand class upper boundary informed in source publication Particle size data flag - =0 if no issues; =1 if there are issues (summing) Sum particle size- g kg-1 - Sum of clay, silt, and sand content Soil depth FROM \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil depth TO \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Depth \u2013 cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations Particle density - Mg m-3 - Soil particle density\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial matric potential \u2013 hPa - Soil water matric potential before loading log Initial matric potential - Soil water matric potential expressed by log\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Wetness (based on initial matric potential) - \u00a01=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported Initial water content data source -\u00a0Graph or table from where the data was collected, or explanation on calculation used Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial bulk density - Mg m-3 - Soil bulk density before loading\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density Precompression stress \u2013 kPa - Precompression stress \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress (SD) \u2013 kPa - Standard deviation for precompression stress values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used Compression index - Compression index \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index (SD) - Standard deviation of compression index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index data source - Graph or table from where the data was collected, or explanation on calculation used Swelling index - Swelling index\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index (SD) - Standard deviation of swelling index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used N -\u00a0Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported Land use (paper) -\u00a0Land use described in the research paper Land use (categories) -\u00a0Land use categorized Land use standardized -\u00a0Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna Land use (number code) -\u00a0Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation Tillage system -\u00a0Tillage system Tillage system (arable soils) - Tillage system for arable soils classified as 'conventional' and 'conservation' Coordinates -\u00a0\u00a0Geographical coordinates\u00a0 of study location Climate -\u00a0Climatic region classification: temperate, tropical, subtropical Climatecod -\u00a0\u00a0\u00a0 Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical Sampling position (paper) -\u00a0Field position where soil samples were collected with details described in the paper Sampling position -\u00a0Field position where soil samples were collected standardized Treatment -\u00a0Experimental treatment type where the soil samples were collected Stress rate - \u00a0kPa - Stress applied in compressive tests\u00a0 Minimum stress \u2013\u00a0kPa - Minimum stress applied in compressive tests Maximum stress \u2013\u00a0kPa - Maximum stress applied in compressive tests Number of stress rate steps -\u00a0Number of steps in stepwise stress application procedure Stess application type -\u00a01=Stepwise stress 2=one sample per stress 3=Strain controlled Stess application type \u2013\u00a0min - Time for stress application in each step in stepwise stress application procedure Degree of deformation at the end of loading -\u00a0% - Degree of deformation at the end of compressive test Sample diameter \u2013\u00a0cm - Diameter of the soil samples Sample height \u2013\u00a0cm - Height of the soil samples Ratio sample diameter and height\u00a0-\u00a0Ratio between diameter and height of the soil samples Sample volume -\u00a0cm3 -Sample volume when the sample diameter and height are nor presented Precompression stress calculation method -\u00a0Calculation method of precompression stress Precompression stress calculation method (number code) -\u00a0Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamand\u00e9 et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006) Description of precompression stress calculation -\u00a0Brief explanation of precompression stress calculation Soil compressive curve components -\u00a0Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain.\u00a0 Soil compressive curve components (number code) -\u00a0Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio Curve components source -\u00a0Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper Compressive curve available -\u00a0Original soil compressive curve available in the paper: 1= No 2=Yes Comments -\u00a0Brief comments on the paper Issues and remarks We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 \u03bcm, utilizing the k-nearest neighbor approach (referred to as 'similarity method' by Nemes et al. (1999). \u00a0 Reference Associa\u00e7\u00e3o Brasileira de Normas T\u00e9cnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990. Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60\u201364.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954\u2013960 10.1139/T2012-054. doi:10.1139/t2012-054 Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562\u2013567. Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139\u2013151. doi:10.1016/0933-3630(95)00015-8 Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012 Lamand\u00e9, M., Schj\u00f8nning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274. Nemes, A., \u00a0W\u00f6sten, J.H.M., Lilly, A., \u00a0Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database.\u00a0Geoderma 90: 187-202. 129\u00a0 O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 \u2013 4):161 \u2013 173.", "keywords": ["2. Zero hunger", "soil compression curve", "precompression stress", "15. Life on land", "soil mechanical properties", "compression index", "soil moisture", "uniaxial compression test", "swelling index"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10060810"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10060810", "name": "item", "description": "10.5281/zenodo.10060810", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10060810"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-06T00:00:00Z"}}, {"id": "10.5281/zenodo.14833053", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:20:28Z", "type": "Dataset", "created": "2025-02-06", "title": "Surface soil moisture for Europe 2014-2024 at 1 km annual and quarterly aggregates", "description": "Copernicus Land Monitoring Services provides Surface Soil Moisture 2014-present (raster 1 km), Europe, daily \u2013 version 1. Each day covers only 5 to 10% of European land mask and shows lines of scenes (obvious artifacts). This is the long-term aggregates of daily images of soil moisture (0\u2013100%) based on two types of aggregation:    Long-term quarterly (qr.1 - winter, qr.2 - spring, qr.3 - summer and qr.4 - autumn),  Annual quantiles P.05, P.50 and P.95,   The soil moisture rasters are based on Sentinel 1 and described in detail in:\u00a0    Bauer-Marschallinger, B. ; Freeman, V. ; Cao, S. ; Paulik, C. ; Schaufler, S. ; Stachl, T. ; Modanesi, S. ; Massari, C. ; Ciabatta, L. ; Brocca, L. ; Wagner, W.\u00a0Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles.\u00a0IEEE Transactions on Geoscience and Remote Sensing\u00a02019, 1 - 20. DOI\u00a010.1109/TGRS.2018.2858004   You can access and download the original data as .nc files from: https://globalland.vito.be/download/manifest/ssm_1km_v1_daily_netcdf/.  The files with pattern 'soil.moisture_s1.clms.qr.*.p0.*.gf_m_1km_20140101_20241231_eu_epsg4326_v20250211.tif' are the gap-filled soil moisture quarterly estimates. For gap filling I build a model using cca 250k random training points and relationship with CHELSA climate bioclimatic variables, ESA CCI snow cover probability, ESA CCI forest and bare areas percent cover and Global Water Pack long-term surface water fraction. The gap-filling model had an R-square of 0.96 and RMSE of 6.5% of soil moisture.  Aggregation has been generated using the terra package in R in combination with the matrixStats::rowQuantiles function. Tiling system and land mask for pan-EU is also available.  library(terra) library(matrixStats) g1 = terra::vect('/mnt/inca/EU_landmask/tilling_filter/eu_ard2_final_status.gpkg') ## 1254 tiles tile = g1[534] nc.lst = list.files('/mnt/landmark/SM1km/ssm_1km_v1_daily_netcdf/', pattern = glob2rx('*.nc$'), full.names=TRUE) ## 3726 ## test it #r = terra::rast(nc.lst[100:210])  agg_tile = function(r, tile, pv=c(0.05,0.5,0.95), out.year='2015.annual'){   bb = paste(as.vector(ext(tile)), collapse = '.')   out.tif = paste0('./eu_tmp/', out.year, '/sm1km_', pv, '_', out.year, '_', bb, '.tif')   if(any(!file.exists(out.tif))){     r.t = terra::crop(r, ext(tile))     r.t = as.data.frame(r.t, xy=TRUE, na.rm=FALSE)     sel.c = grep(glob2rx('ssm$'), colnames(r.t))     t1s = cbind(data.frame(matrixStats::rowQuantiles(as.matrix(r.t[,sel.c]), probs = pv, na.rm=TRUE)),  data.frame(x=r.t$x,  y=r.t$y))     ## write to GeoTIFFs     r.o = terra::rast(t1s[,c('x','y','X5.','X50.','X95.')], type='xyz', crs='+proj=longlat +datum=WGS84 +no_defs')     for(k in 1:length(pv)){        terra::writeRaster(r.o[[k]], filename=out.tif[k], gdal=c('COMPRESS=DEFLATE'), datatype='INT2U', NAflag=32768, overwrite=FALSE)     }     rm(r.t); gc()     tmpFiles(remove=TRUE)   } }  ## quarterly values: lA = data.frame(filename=nc.lst) library(lubridate) lA$Date = ymd(sapply(lA$filename, function(i){substr(strsplit(basename(i), '_')[[1]][4], 1, 8)})) #summary(is.na(lA$Date)) #hist(lA$Date, breaks=60) lA$quarter = quarter(lA$Date, fiscal_start = 11) summary(as.factor(lA$quarter))  for(qr in 1:4){   #qr=1   pth = paste0('A.q', qr)   rs = terra::rast(lA$filename[lA$quarter==qr])   x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(rs, tile=g1[i], out.year=pth) )}, mc.cores=20)   for(type in c(0.05,0.5,0.95)){     x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE)     out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt')     vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt')     cat(x, sep=' n', file=out.tmp)     system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp))     system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.qr.', qr, '.p', type, '_m_1km_20140101_20241231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27'))   } }  ## per year ---- for(year in 2015:2023){   l.lst = nc.lst[grep(year, basename(nc.lst))]   r = terra::rast(l.lst)   pth = paste0(year, '.annual')   x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(r, tile=g1[i], out.year=pth) )}, mc.cores=40)   ## Mosaics:   for(type in c(0.05,0.5,0.95)){     x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE)     out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt')     vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt')     cat(x, sep=' n', file=out.tmp)     system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp))     system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.annual.', type, '_m_1km_', year, '0101_', year, '1231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27'))   } }", "keywords": ["Soil", "Soil moisture"], "contacts": [{"organization": "Hengl, Tomislav", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14833053"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14833053", "name": "item", "description": "10.5281/zenodo.14833053", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14833053"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-07T00:00:00Z"}}, {"id": "10.1002/2017JD027346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:04Z", "type": "Journal Article", "created": "2017-12-28", "title": "Soil Moisture-Temperature Coupling in a Set of Land Surface Models", "description": "Abstract<p>The land surface controls the partitioning of water and energy fluxes and therefore plays a crucial role in the climate system. The coupling between soil moisture and air temperature, in particular, has been shown to affect the severity and occurrence of temperature extremes and heat waves. Here we study soil moisture\uffe2\uff80\uff90temperature coupling in five land surface models, focusing on the terrestrial segment of the coupling in the warm season. All models are run off\uffe2\uff80\uff90line over a common period with identical atmospheric forcing data, in order to allow differences in the results to be attributed to the models' partitioning of energy and water fluxes. Coupling is calculated according to two semiempirical metrics, and results are compared to observational flux tower data. Results show that the locations of the global hot spots of soil moisture\uffe2\uff80\uff90temperature coupling are similar across all models and for both metrics. In agreement with previous studies, these areas are located in transitional climate regimes. The magnitude and local patterns of model coupling, however, can vary considerably. Model coupling fields are compared to tower data, bearing in mind the limitations in the geographical distribution of flux towers and the differences in representative area of models and in situ data. Nevertheless, model coupling correlates in space with the tower\uffe2\uff80\uff90based results (r = 0.5\uffe2\uff80\uff930.7), with the multimodel mean performing similarly to the best\uffe2\uff80\uff90performing model. Intermodel differences are also found in the evaporative fractions and may relate to errors in model parameterizations and ancillary data of soil and vegetation characteristics.</p>", "keywords": ["ENVIRONMENT SIMULATOR JULES", "FLUXES", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "CO2 EXCHANGE", "models", "WATER", "SCALE", "Research Articles", "0105 earth and related environmental sciences", "land surface", "CARBON-DIOXIDE EXCHANGE", "eartH2Observe", "temperature", "15. Life on land", "DECIDUOUS FOREST", "CLIMATE", "EVAPORATION", "VARIABILITY", "13. Climate action", "Earth and Environmental Sciences", "BALANCE", "land surface models", "SENSIBLE HEAT", "land-atmosphere interactions", "soil moisture"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JD027346"}, {"href": "https://doi.org/10.1002/2017JD027346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2017JD027346", "name": "item", "description": "10.1002/2017JD027346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2017JD027346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-01T00:00:00Z"}}, {"id": "10.1016/j.envexpbot.2020.104095", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:15:27Z", "type": "Journal Article", "created": "2020-04-25", "title": "Alternation of wet and dry sides during partial rootzone drying irrigation enhances leaf ethylene evolution", "description": "Soil drying increases endogenous ABA and ACC concentrations in planta, but how these compounds interact to regulate stomatal responses to soil drying and re-watering is still unclear. To determine the temporal dynamics and physiological significance of root, xylem and leaf ABA and ACC concentrations in response to deficit irrigation (DI) or partial rootzone drying (PRD-F) and re-watering, these variables were measured in plants exposed to similar whole pot soil water contents. Both DI and PRD-F plants received only a fraction of the irrigation supplied to well-watered (WW) plants, either to all (DI) or part (PRD-F) of the rootzone of plants grown in split-pots. Both DI and PRD-F induced partial stomatal closure, increased root ABA and ACC accumulation consistent with local soil water content, but did not affect xylem or leaf concentrations of these compounds compared to WW plants. Two hours after re-watering all (DI-RW) or part of the rootzone (PRD-A) to the same soil water content, stomatal conductance returned to WW values or further decreased respectively. Re-watering the whole rootzone had no effect on xylem and leaf ABA and ACC concentrations, while re-watering the dry side of the pot in PRD plants had no effect on xylem and leaf ABA concentrations but increased xylem and leaf ACC concentrations and leaf ethylene evolution. Leaf water potential was similar between all irrigation treatments, with stomatal conductance declining as xylem ABA concentrations and leaf ACC concentrations increased. Prior to re-watering PRD plants, accounting for the spatial differences in soil water uptake best explained variation in xylem ACC concentration suggesting root-to-shoot ACC signalling, but this model did not account for variation in xylem ACC concentration after re-watering the dry side of PRD plants. Thus local (foliar) and long-distance (root-to-shoot) variation in ACC status both seem important in regulating the temporal dynamics of foliar ethylene evolution in plants exposed to PRD.", "keywords": ["0106 biological sciences", "Irrigation", "Stomatal conductance", "Root-to-shoot signalling", "Ethylene", "Physiological significance", "Deficit irrigation", "Plant Science", "Leaf water", "F06 Irrigation", "01 natural sciences", "ACC", "Ecology", " Evolution", " Behavior and Systematics", "580", "2. Zero hunger", "Xylem", "15. Life on land", "F60 Plant physiology and biochemistry", "6. Clean water", "Horticulture", "13. Climate action", "Soil water", "Agronomy and Crop Science", "Soil moisture heterogeneity", "Partial rootzone drying"]}, "links": [{"href": "https://eprints.lancs.ac.uk/id/eprint/144510/1/Juan_EEB_Manuscript_final.pdf"}, {"href": "https://doi.org/10.1016/j.envexpbot.2020.104095"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20and%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envexpbot.2020.104095", "name": "item", "description": "10.1016/j.envexpbot.2020.104095", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envexpbot.2020.104095"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-01T00:00:00Z"}}, {"id": "/publisher", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:13:57Z", "type": "Dataset", "title": "Soil moisture and temperature datasets in LIDO orchard", "description": "Soil moisture and temperature timeserie datasets of the apple orchard located in the Laimburg Integrated Digital Orchard in Bolzano province. The timeseries collects data about soil moisture and temperature at 20 cm, 30 cm and 40 cm under the soil surface in 3 different position of the orchard. Acquisition frequency is 30 minutes for all the parameters.", "keywords": ["agricultural-and-aquaculture-facilities", "cct", "eu", "sensor", "soil", "soil-moisture", "temperature", "water"]}, "links": [{"href": "https://doi.org/10.48784/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"href": "http://data.europa.eu/88u/dataset/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"rel": "self", "type": "application/geo+json", "title": "/publisher", "name": "item", "description": "/publisher", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//publisher"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "/", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:13:57Z", "type": "Dataset", "title": "DrSch\u00e4r mais field monitoring timeseries", "description": "Timeseries of the DrSch\u00e4r field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.", "keywords": ["capacitive", "environmental-monitoring-facilities", "eu", "humidity", "irrigation", "lorawan", "mais", "sensor", "soil", "soil-moisture", "tdr", "temperature", "water", "zigbee"]}, "links": [{"href": "http://data.europa.eu/88u/dataset/17af841d-1329-4c5a-a8b8-c4326f0614f9"}, {"rel": "self", "type": "application/geo+json", "title": "/", "name": "item", "description": "/", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "00370eef-d266-4f5d-9c79-b93488983b54", "type": "Feature", "geometry": null, "properties": {"license": "https://spdx.org/licenses/CC-BY-4.0.html", "updated": "2025-09-16T06:24:05.968528", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-09-15 ore 12 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). Corsa del 2025-09-15 ore 12 UTC - Valido dalle ore 12 UTC del 2025-09-15 alle ore 00 UTC del 2025-09-19. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-09-15", "20250915t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-umidita-del-suolo-volumetrica-2025-09-15-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/00370eef-d266-4f5d-9c79-b93488983b54"}, {"rel": "self", "type": "application/geo+json", "title": "00370eef-d266-4f5d-9c79-b93488983b54", "name": "item", "description": "00370eef-d266-4f5d-9c79-b93488983b54", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/00370eef-d266-4f5d-9c79-b93488983b54"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "00682004-c6b9-4c1d-8b40-3afff8bbec69", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.16, 47.52], [11.16, 47.52], [11.16, 47.52], [11.16, 47.52], [11.16, 47.52]]]}, "properties": {"themes": [{"concepts": [{"id": "climatologyMeteorologyAtmosphere"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "environmental factors"}, {"id": "water"}, {"id": "Soil analysis"}, {"id": "Soil"}, {"id": "soil amendments"}, {"id": "Soil biology"}, {"id": "Temperature profile"}, {"id": "moisture content"}, {"id": "Temperature"}, {"id": "Soil temperature"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "soil profile"}, {"id": "soil moisture"}, {"id": "temperature"}], "scheme": "GEMET - Concepts, version 2.4"}, {"concepts": [{"id": "farming systems"}, {"id": "Grassland management"}, {"id": "Grassland soils"}, {"id": "grasslands"}, {"id": "permanent grasslands"}, {"id": "agriculture"}, {"id": "agricultural practices"}, {"id": "Climatic change"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "Boden"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "opendata"}], "scheme": "Individual"}], "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. (e.g. Reports, articles, papers, scientific and non-scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \u201cData re-used from the BonaRes Data Centre www.bonares.de. This data were created as part of BonaRes Module A-Project - SUSALPS's research activities.\u201d Although every care has been taken in preparing and testing the data, BonaRes Module A-Project- SUSALPS and BonaRes Data Centre cannot guarantee that the data are correct; neither does BonaRes Module A-Project-SUSALPS and BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The BonaRes Module A-Project-SUSALPS and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data. The access to this data is restricted during embargo time. If prior access is requested, contact the data owner/author.)", "updated": "2020-02-14", "type": "Dataset", "created": "2018-12-05", "language": "eng", "title": "SUSALPS temperature and volumetric soil water content Esterberg Subplot 3 in Esterberg intensiv", "description": "Grassland is a precious good. Grassland contributes to food security by providing fodder for dairy and beef farming, storing nutrients and increasing biodiversity. These functions that secure the fertility and yields of soil are jeopardized by climate change, especially in monane and alpine areas.\nIn SUSALPS, scientists, authorities and farmers work together to investigate the influence of climate change on i) plant biodiversity, ii) C and N storage, iii) greenhouse gas exchange, iv) socio economic conditions that influence decision making of farmers.\nA central experimental aspect is the translocation of soil mesocosms from higher elevation to lower elevation (Esterberg site at 1200m, Graswang site at 860m, Fendt at 600m, Bayreuth at 300m). To reflect the spatial heterogeneity of soils, mesocosms from three different subplots approx. 100-300m apart from each other are translocated. 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Corsa del 2025-06-21 ore 12 UTC - Valido dalle ore 12 UTC del 2025-06-21 alle ore 00 UTC del 2025-06-25. 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The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02\uffe2\uff80\uff930.56, a sand fraction range of 0.05\uffe2\uff80\uff930.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (\uffce\uffb8) and two resistance parameters   and \uffce\uffb8efolding. The data\uffe2\uff80\uff90driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut\uffe2\uff80\uff90off soil moisture value   at which SEE=0.5, and first derivative of SEE at  , named  . An analytical relationship between   and   is first built by running a soil energy balance model for two extreme conditions with rss\uffe2\uff80\uff89=\uffe2\uff80\uff890 and   using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair   either from the time series of SEE and \uffce\uffb8 observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear   relationships and potentially large random deviations of observed SEE from the mean observed  . The second method parameterizes   as a multi\uffe2\uff80\uff90linear regression of clay and sand percentages, and sets   to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol\uffe2\uff80\uff90Biosph\uffc3\uffa8re\uffe2\uff80\uff90Atmosph\uffc3\uffa8re), H\uffe2\uff80\uff90TESSEL (Hydrology\uffe2\uff80\uff90Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land\uffe2\uff80\uff90surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.</p", "keywords": ["550", "0207 environmental engineering", "modeling", "02 engineering and technology", "15. Life on land", "551", "01 natural sciences", "evaporation", "soil", "moisture", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "texture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015WR018233"}, {"href": "https://doi.org/10.1002/2015wr018233"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water%20Resources%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2015wr018233", "name": "item", "description": "10.1002/2015wr018233", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2015wr018233"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-05-01T00:00:00Z"}}, {"id": "0ea79f25-efe5-4ad4-aaff-d707b059faf4", "type": "Feature", "geometry": null, "properties": {"updated": "2025-07-11T06:23:09.695616", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-07-10 ore 12 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). 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Corsa del 2025-04-29 ore 12 UTC - Valido dalle ore 12 UTC del 2025-04-29 alle ore 00 UTC del 2025-05-03. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-04-29", "20250429t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-umidita-del-suolo-volumetrica-2025-04-29-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250429T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0fc7bb44-b2a2-4650-82e6-9effae4a8607"}, {"rel": "self", "type": "application/geo+json", "title": "0fc7bb44-b2a2-4650-82e6-9effae4a8607", "name": "item", "description": "0fc7bb44-b2a2-4650-82e6-9effae4a8607", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0fc7bb44-b2a2-4650-82e6-9effae4a8607"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "0ff8b540-2236-4288-86a3-dabac9b44d83", "type": "Feature", "geometry": null, "properties": {"updated": "2025-08-31T11:22:41.479631", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-08-31 ore 00 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). Corsa del 2025-08-31 ore 00 UTC - Valido dalle ore 00 UTC del 2025-08-31 alle ore 00 UTC del 2025-09-03. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["000000", "2025-08-31", "20250831t000000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN00/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-umidita-del-suolo-volumetrica-2025-08-31-ore-00-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250831T000000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0ff8b540-2236-4288-86a3-dabac9b44d83"}, {"rel": "self", "type": "application/geo+json", "title": "0ff8b540-2236-4288-86a3-dabac9b44d83", "name": "item", "description": "0ff8b540-2236-4288-86a3-dabac9b44d83", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0ff8b540-2236-4288-86a3-dabac9b44d83"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.1002/2016JD026099", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:04Z", "type": "Journal Article", "created": "2017-04-07", "title": "Global soil moisture bimodality in satellite observations and climate models", "description": "Abstract<p>A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land\uffe2\uff80\uff90atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land\uffe2\uff80\uff90atmospheric feedback may be overestimated in current climate models.</p>", "keywords": ["PREFERENTIAL STATES", "IMPACT", "MIXTURE", "SCHEME", "0207 environmental engineering", "NORMAL-DISTRIBUTIONS", "02 engineering and technology", "15. Life on land", "01 natural sciences", "PART I", "satellite soil moisture", "climate models", "13. Climate action", "Earth and Environmental Sciences", "LAND-SURFACE MODEL", "PRECIPITATION", "SDG 13 - Climate Action", "CMIP5", "ATMOSPHERE COUPLING EXPERIMENT", "land-atmosphere interactions", "soil moisture", "bimodality", "SYSTEM", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026099"}, {"href": "https://doi.org/10.1002/2016JD026099"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2016JD026099", "name": "item", "description": "10.1002/2016JD026099", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2016JD026099"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-27T00:00:00Z"}}, {"id": "10.1002/2016rg000543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:04Z", "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": "0f31be4f-5547-4e42-8216-e51a08b32539", "type": "Feature", "geometry": null, "properties": {"updated": "2025-04-27T08:23:33.452659Z", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-04-26 ore 12 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). Corsa del 2025-04-26 ore 12 UTC - Valido dalle ore 12 UTC del 2025-04-26 alle ore 00 UTC del 2025-04-30. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-04-26", "20250426t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-umidita-del-suolo-volumetrica-2025-04-26-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250426T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0f31be4f-5547-4e42-8216-e51a08b32539"}, {"rel": "self", "type": "application/geo+json", "title": "0f31be4f-5547-4e42-8216-e51a08b32539", "name": "item", "description": "0f31be4f-5547-4e42-8216-e51a08b32539", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0f31be4f-5547-4e42-8216-e51a08b32539"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.1002/hyp.14451", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:07Z", "type": "Journal Article", "created": "2021-12-11", "title": "Hydrological responses to rainfall events including the extratropical cyclone Gloria in two contrasting Mediterranean headwaters in Spain; the perennial font del Reg\u00e0s and the intermittent Fuirosos", "description": "Abstract<p>Catchment hydrological responses to precipitation inputs, particularly during exceptionally large storms, are complex and variable, and our understanding of the associated runoff generation processes during those events is limited. Hydrological monitoring of climatically and hydrologically distinct catchments can help to improve this understanding by shedding light on the interplay between antecedent soil moisture conditions, hydrological connectivity, and rainfall event characteristics. This knowledge is urgently needed considering that both the frequency and magnitude of extreme precipitation events are increasing worldwide as a consequence of climate change. In autumn 2018, we installed water level sensors to monitor stream water and near\uffe2\uff80\uff90stream groundwater levels at two Mediterranean forest headwater catchments with contrasting hydrological regimes: Font del Reg\uffc3\uffa0s (sub\uffe2\uff80\uff90humid climate, perennial flow regime) and Fuirosos (semi\uffe2\uff80\uff90arid climate, intermittent flow regime). Both catchments are located in northeastern Spain, where the extratropical cyclone Gloria hit in January 2020 and left in ca. 65\uffe2\uff80\uff89h outstanding accumulated rainfalls of 424\uffe2\uff80\uff89mm in Font del Reg\uffc3\uffa0s and 230\uffe2\uff80\uff89mm in Fuirosos. During rainfall events of low mean intensity, hydrological responses to precipitation inputs at the semi\uffe2\uff80\uff90arid Fuirosos were more delayed and more variable than at the sub\uffe2\uff80\uff90humid Font del Reg\uffc3\uffa0s. We explain these divergences by differences in antecedent soil moisture conditions and associated differences in catchment hydrological connectivity between the two catchments, which in this case are likely driven by differences in local climate rather than by differences in local topography. In contrast, during events of moderate and high mean rainfall intensities, including the storm Gloria, precipitation inputs and hydrological responses correlated similarly in the two catchments. We explain this convergence by rapid development of hydrological connectivity independently of antecedent soil moisture conditions. The data set presented here is unique and contributes to our mechanistic understanding on how streams respond to rainfall events and exceptionally large storms in catchments with contrasting flow regimes.</p>", "keywords": ["info:eu-repo/classification/ddc/550", "550", "ddc:550", "rainfall intensity", "climate extreme", "15. Life on land", "551", "extreme hydrological event", "01 natural sciences", "6. Clean water", "antecedent soil moisture conditions", "Earth sciences", "13. Climate action", "heavy rainfall", "Mediterranean climate", "catchment hydrological connectivity", "environmental monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14451"}, {"href": "https://doi.org/10.1002/hyp.14451"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrological%20Processes", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/hyp.14451", "name": "item", "description": "10.1002/hyp.14451", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/hyp.14451"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "10.1002/hyp.14667", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:14:07Z", "type": "Journal Article", "created": "2022-08-09", "title": "Non\u2010linearity in event runoff generation in a small agricultural catchment", "description": "Abstract<p>Understanding the role of soil moisture and other controls in runoff generation is important for predicting runoff across scales. This paper aims to identify the degree of non\uffe2\uff80\uff90linearity of the relationship between event peak runoff and potential controls for different runoff generation mechanisms in a small agricultural catchment. The study is set in the 66\uffe2\uff80\uff89ha Hydrological Open Air Laboratory, Austria, where discharge was measured at the catchment outlet and for 11 sub\uffe2\uff80\uff90catchments or hillslopes with different runoff generation mechanisms. Peak runoff of 73 events was related to three potential controls: event precipitation, soil moisture and groundwater levels. The results suggest that the hillslopes dominated by ephemeral overland flow exhibit the most non\uffe2\uff80\uff90linear runoff generation behaviour for its controls; runoff is only generated above a threshold of 95% of the maximum soil moisture. Runoff generation through tile drains and in wetlands is more linear. The largest winter and spring events at the catchment outlet are caused by runoff from hillslopes with shallow flow paths (ephemeral overland flow and tile drainage mechanisms), while the largest summer events are caused by other hillslopes, those with deeper flow paths or with saturation areas throughout the year. Therefore, the response of the entire catchment is a mix of the various mechanisms, and the groundwater contribution makes the response more linear. The implications for hydrological modelling are discussed.</p", "keywords": ["13. Climate action", "0208 environmental biotechnology", "0207 environmental engineering", "connectivity; flow paths; groundwater; non\u2010linearity; precipitation; runoff generation; scaling; seasonality; soil moisture", "02 engineering and technology", "15. Life on land", "Research Articles", "6. Clean water"]}, "links": [{"href": "https://cris.unibo.it/bitstream/11585/1012878/1/2022_Vreugdenhil_HydrologicalProcesses.pdf"}, {"href": "https://doi.org/10.1002/hyp.14667"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrological%20Processes", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/hyp.14667", "name": "item", "description": "10.1002/hyp.14667", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/hyp.14667"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-01T00:00:00Z"}}, {"id": "10.1007/s00442-012-2484-8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:27Z", "type": "Journal Article", "created": "2012-12-27", "title": "Herbivore Trampling As An Alternative Pathway For Explaining Differences In Nitrogen Mineralization In Moist Grasslands", "description": "Studies addressing the role of large herbivores on nitrogen cycling in grasslands have suggested that the direction of effects depends on soil fertility. Via selection for high quality plant species and input of dung and urine, large herbivores have been shown to speed up nitrogen cycling in fertile grassland soils while slowing down nitrogen cycling in unfertile soils. However, recent studies show that large herbivores can reduce nitrogen mineralization in some temperate fertile soils, but not in others. To explain this, we hypothesize that large herbivores can reduce nitrogen mineralization in loamy or clay soils through soil compaction, but not in sandy soils. Especially under wet conditions, strong compaction in clay soils can lead to periods of soil anoxia, which reduces decomposition of soil organic matter and, hence, N mineralization. In this study, we use a long-term (37-year) field experiment on a salt marsh to investigate the hypothesis that the effect of large herbivores on nitrogen mineralization depends on soil texture. Our results confirm that the presence of large herbivores decreased nitrogen mineralization rate in a clay soil, but not in a sandy soil. By comparing a hand-mown treatment with a herbivore-grazed treatment, we show that these differences can be attributed to herbivore-induced changes in soil physical properties rather than to above-ground biomass removal. On clay soil, we find that large herbivores increase the soil water-filled porosity, induce more negative soil redox potentials, reduce soil macrofauna abundance, and reduce decomposition activity. On sandy soil, we observe no changes in these variables in response to grazing. We conclude that effects of large herbivores on nitrogen mineralization cannot be understood without taking soil texture, soil moisture, and feedbacks through soil macrofauna into account.", "keywords": ["0106 biological sciences", "IMPACT", "Nitrogen", "01 natural sciences", "Soil fauna", "COMPACTION", "Soil", "SOIL PHYSICAL-PROPERTIES", "SALT-MARSH", "Large herbivores", "Soil texture", "Animals", "Biomass", "Herbivory", "Soil compaction", "Ecosystem", "2. Zero hunger", "UNGULATE", "national", "Water", "DENITRIFICATION", "Nitrogen Cycle", "15. Life on land", "N cycling", "YELLOWSTONE-NATIONAL-PARK", "PLANT-GROWTH", "13. Climate action", "ECOSYSTEM", "Clay", "Aluminum Silicates", "Soil moisture", "BAIT-LAMINA TEST"]}, "links": [{"href": "https://doi.org/10.1007/s00442-012-2484-8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Oecologia", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00442-012-2484-8", "name": "item", "description": "10.1007/s00442-012-2484-8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00442-012-2484-8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-12-28T00:00:00Z"}}, {"id": "10.1007/s10021-008-9198-0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:29Z", "type": "Journal Article", "created": "2008-10-14", "title": "Soil Respiration In European Grasslands In Relation To Climate And Assimilate Supply", "description": "Soil respiration constitutes the second largest flux of carbon (C) between terrestrial ecosystems and the atmosphere. This study provides a synthesis of soil respiration (R(s)) in 20 European grasslands across a climatic transect, including ten meadows, eight pastures and two unmanaged grasslands. Maximum rates of R(s) (R(s(max) )), R(s) at a reference soil temperature (10\u00b0C; R(s(10) )) and annual R(s) (estimated for 13 sites) ranged from 1.9 to 15.9 \u03bcmol CO(2) m(-2) s(-1), 0.3 to 5.5 \u03bcmol CO(2) m(-2) s(-1) and 58 to 1988 g C m(-2) y(-1), respectively. Values obtained for Central European mountain meadows are amongst the highest so far reported for any type of ecosystem. Across all sites R(s(max) ) was closely related to R(s(10) ).Assimilate supply affected R(s) at timescales from daily (but not necessarily diurnal) to annual. Reductions of assimilate supply by removal of aboveground biomass through grazing and cutting resulted in a rapid and a significant decrease of R(s). Temperature-independent seasonal fluctuations of R(s) of an intensively managed pasture were closely related to changes in leaf area index (LAI). Across sites R(s(10) ) increased with mean annual soil temperature (MAT), LAI and gross primary productivity (GPP), indicating that assimilate supply overrides potential acclimation to prevailing temperatures. Also annual R(s) was closely related to LAI and GPP. Because the latter two parameters were coupled to MAT, temperature was a suitable surrogate for deriving estimates of annual R(s) across the grasslands studied. These findings contribute to our understanding of regional patterns of soil C fluxes and highlight the importance of assimilate supply for soil CO(2) emissions at various timescales.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "leaf area index", "577", "temperature", "land use", "04 agricultural and veterinary sciences", "15. Life on land", "soil CO2 efflux", "13. Climate action", "Settore BIO/07 - ECOLOGIA", "moisture", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "soil carbon", "gross primary productivity", "Soil CO2 efflux"]}, "links": [{"href": "https://doi.org/10.1007/s10021-008-9198-0"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10021-008-9198-0", "name": "item", "description": "10.1007/s10021-008-9198-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10021-008-9198-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-10-15T00:00:00Z"}}, {"id": "10.1007/s100210000025", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:14:31Z", "type": "Journal Article", "created": "2002-07-25", "title": "Controls On Soil Carbon Dioxide And Methane Fluxes In A Variety Of Taiga Forest Stands In Interior Alaska", "description": "CO2 and CH4 fluxes were monitored over 4 years in a range of taiga forests along the Tanana River in interior Alaska. Floodplain alder and white spruce sites and upland birch/aspen and white spruce sites were examined. Each site had control, fertilized, and sawdust amended plots; flux measurements began during the second treatment year. CO2 emissions decreased with successional age across the sites (alder, birch/aspen, and white spruce, in order of succession) regardless of landscape position. Although CO2 fluxes showed an exponential relationship with soil temperature, the response of CO2 production to moisture fit an asymptotic model. Of the manipulations, only N fertilization had an effect on CO2 flux, decreasing flux in the floodplain sites but increasing it in the birch/aspen site. Landscape position was the best predictor of CH4 flux. The two upland sites consumed CH4 at similar rates (approximately 0.5 mg C m\u22122 d\u22121), whereas the floodplain sites had lower consumption rates (0\u20130.3 mg C m\u22122 d\u22121). N fertilization and sawdust both inhibited CH4 consumption in the upland birch/aspen and floodplain spruce sites but not in the upland spruce site. The biological processes driving CO2 fluxes were sensitive to temperature, moisture, and vegetation, whereas CH4 fluxes were sensitive primarily to landscape position and biogeochemical disturbances. Hence, climate change effects on C-gas flux in taiga forest soils will depend on the relationship between soil temperature and moisture and the concomitant changes in soil nutrient pools and cycles.", "keywords": ["landscape-ecology", "Betulaceae-: Dicotyledones-", "flux-", "soil-nutrient-pools", "Coniferopsida-: Gymnospermae-", "Vascular-Plants", "forests-", "Environmental-Sciences)", "carbon-dioxide", "nitrogen-fertilizers", "01 natural sciences", "carbon-dioxide: emissions-", "nitrogen-: fertilization-", "vegetation-", "birch- (Betulaceae-)", "124-38-9: CARBON DIOXIDE", "Spermatophytes-", "Spermatophyta-", "74-82-8: METHANE", "Plantae-", "white-spruce (Coniferopsida-)", "successional-age", "boreal-forests", "environmental-temperature", "0105 earth and related environmental sciences", "taiga-forest-stands", "Angiosperms-", "Gymnosperms-", "Angiospermae-", "Plants-", "sawdust-", "methane-", "15. Life on land", "North-America", "Nearctic-region)", "floodplains-", "mathematical-models", "13. Climate action", "alder- (Betulaceae-)", "upland-sites", "Alaska- (USA-", "climate-change", "Terrestrial-Ecology (Ecology-", "7727-37-9: NITROGEN", "Dicots-", "methane-: consumption-", "moisture-", "climatic-change", "temperature-"]}, "links": [{"href": "https://doi.org/10.1007/s100210000025"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s100210000025", "name": "item", "description": "10.1007/s100210000025", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s100210000025"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2000-05-10T00:00:00Z"}}, {"id": "10.1016/j.agwat.2021.107290", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:15:07Z", "type": "Journal Article", "created": "2021-11-22", "title": "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions", "description": "Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ET c act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET c act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK c. Surface SM observations were assimilated into the soil evaporation (E s) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T c act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K s) as a proxy of LST (LST proxy). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K s estimates from FAO-56 model and thermal-derived K s. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET c act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK c using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET c act measurements.", "keywords": ["2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "550", "Evapotranspiration Data assimilation FAO-dualK c Soil moisture Land surface temperature", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "FAO-dualK(c)", "13. Climate action", "Data assimilation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Land surface temperature"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.107290"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2021.107290", "name": "item", "description": "10.1016/j.agwat.2021.107290", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.107290"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2018.04.013", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:15:54Z", "type": "Journal Article", "created": "2018-04-24", "title": "Retrieving surface soil moisture at high spatio-temporal resolution from a synergy between Sentinel-1 radar and Landsat thermal data: A study case over bare soil", "description": "Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015\u20132016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39\u00b0\u201343\u00b0). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03\u202fm3\u202fm\u22123, which is far smaller than 0.16\u202fm3\u202fm\u22123 when using S1 (VV) only.", "keywords": ["550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "Sentinel-1 (A/B)", "near surface soil moisture", "Bare soil", "0211 other engineering and technologies", "Sentinel-1 (AB)", "02 engineering and technology", "15. Life on land", "Landsat-78", "01 natural sciences", "Energy balance modelling", "Near surface soil moisture", "Landsat-7/8", "bare soil", "13. Climate action", "energy balance modelling", "soil evaporation", "[SDE.IE] Environmental Sciences/Environmental Engineering", "Soil evaporation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://hal.archives-ouvertes.fr/hal-01912888/file/Amazirh%20et%20al_2018%20%281%29.pdf"}, {"href": "https://doi.org/10.1016/j.rse.2018.04.013"}, {"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.2018.04.013", "name": "item", "description": "10.1016/j.rse.2018.04.013", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2018.04.013"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "10.1016/j.rse.2019.111627", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:15:54Z", "type": "Journal Article", "created": "2020-01-10", "title": "Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region", "description": "Abstract   Monitoring irrigation is essential for an efficient management of water resources in arid and semi-arid regions. We propose to estimate the timing and the amount of irrigation throughout the agricultural season using optical and thermal Landsat-7/8 data. The approach is implemented in four steps: i) partitioning the Landsat land surface temperature (LST) to derive the crop water stress coefficient (Ks), ii) estimating the daily root zone soil moisture (RZSM) from the integration of Landsat-derived Ks into a crop water balance model, iii) retrieving irrigation at the Landsat pixel scale and iv) aggregating pixel-scale irrigation estimates at the crop field scale. The new irrigation retrieval method is tested over three agricultural areas during four seasons and is evaluated over five winter wheat fields under different irrigation techniques (drip, flood and no-irrigation). The model is very accurate for the seasonal accumulated amounts (R ~ 0.95 and RMSE ~ 44\u00a0mm). However, lower agreements with observed irrigations are obtained at the daily scale. To assess the performance of the irrigation retrieval method over a range of time periods, the daily predicted and observed irrigations are cumulated from 1 to 90\u00a0days. Generally, acceptable errors (R\u00a0=\u00a00.52 and RMSE\u00a0=\u00a027\u00a0mm) are obtained for irrigations cumulated over 15\u00a0days and the performance gradually improves by increasing the accumulation period, depicting a strong link to the frequency of Landsat overpasses (16\u00a0days or 8\u00a0days by combining Landsat-7 and -8). Despite the uncertainties in retrieved irrigations at daily to weekly scales, the daily RZSM and evapotranspiration simulated from the retrieved daily irrigations are estimated accurately and are very close to those estimated from actual irrigations. This research demonstrates the utility of high spatial resolution optical and thermal data for estimating irrigation and consequently for better closing the water budget over agricultural areas. We also show that significant improvements can be expected at daily to weekly time scales by reducing the revisit time of high-spatial resolution thermal data, as included in the TRISHNA future mission requirements.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "550", "Evapotranspiration", "0208 environmental biotechnology", "Root-zone soil moisture", "0207 environmental engineering", "FAO-56 model", "02 engineering and technology", "15. Life on land", "630", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "[SDE]Environmental Sciences", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Irrigation", "Landsat", "Land surface temperature"], "contacts": [{"organization": "Olivera-Guerra, Luis Enrique, Merlin, Olivier, Er-Raki, Salah,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2019.111627"}, {"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.2019.111627", "name": "item", "description": "10.1016/j.rse.2019.111627", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2019.111627"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.1016/j.isprsjprs.2019.02.004", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:15:44Z", "type": "Journal Article", "created": "2019-02-15", "title": "Including Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data", "description": "Abstract   The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100\u202fm resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in addition to the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites \u2013 an 8\u202fkm by 8\u202fkm irrigated crop area named \u201cR3\u201d and a 12\u202fkm by 12\u202fkm rainfed area named \u201cSidi Rahal\u201d in central Morocco (Marrakech) \u2013 on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015\u20132016 growing season. The downscaling methods are applied to the 1\u202fkm resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100\u202fm disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60\u202f\u00b0C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35\u202f\u00b0C and a mean R increasing to 0.75.", "keywords": ["LST", "2. Zero hunger", "550", "0211 other engineering and technologies", "02 engineering and technology", "15. Life on land", "01 natural sciences", "333", "6. Clean water", "MODIS/Terra", "Disaggregation", "disaggregation", "[SDE.ES] Environmental Sciences/Environment and Society", "MODIS/Terra Landsat", "MODISTerra Landsat", "Sentinel-1", "Soil moisture", "soil moisture", "[SDE.ES]Environmental Sciences/Environment and Society", "Landsat", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.isprsjprs.2019.02.004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20Journal%20of%20Photogrammetry%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.isprsjprs.2019.02.004", "name": "item", "description": "10.1016/j.isprsjprs.2019.02.004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.isprsjprs.2019.02.004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-01T00:00:00Z"}}, {"id": "10.1007/s13595-016-0547-4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:55Z", "type": "Journal Article", "created": "2016-03-24", "title": "Effects Of Experimental Warming On Soil Respiration And Biomass In Quercus Variabilis Blume And Pinus Densiflora Sieb. Et Zucc. Seedlings", "description": "AbstractKey messageIn the open-field warming experiment using infrared heaters, 3\u00a0\u00b0C warming affected soil respiration more in the deciduousQuercus variabilisBlume plot than in the evergreenPinus densifloraSieb. et Zucc. plot, but did not affect the plant biomass in either species.ContextUnderstanding the species-specific responses of belowground carbon processes to warming is essential for the accurate prediction of forest carbon cycles in ecosystems affected by future climate change.AimsThis study aimed to investigate the effect of experimental warming on soil CO2 efflux, soil-air CO2 concentration, and plant biomass for two taxonomically different temperate tree species.MethodsExperimental warming was conducted in an open-field planted with Q. variabilis and P. densiflora seedlings. Infrared heaters increased the air temperature by 3\u00a0\u00b0C in the warmed plots compared with the air temperature in the control plots over a 2-year period.ResultsThe increase in air and soil temperature stimulated soil CO2 efflux by 29 and 22\u00a0% for the Q. variabilis and P. densiflora plots, respectively. Seasonal variation in the warming effect on soil CO2 efflux was species-specific. Soil CO2 efflux was also positively related to both soil temperature and soil water content. The soil moisture deficit decreased the difference in soil CO2 efflux between the control and warmed plots. Warming did not affect soil CO2 concentration and plant biomass in either species; however, the mean soil CO2 concentration was positively correlated with root and total biomass.ConclusionWarming increased soil CO2 efflux in both Q. variabilis and P. densiflora plots, while the increase showed remarkable seasonal variations and different magnitudes for the two species.", "keywords": ["0106 biological sciences", "soil temperature", "evergreen tree", "soil water", "Red pine", "seedling", "soil respiration", "01 natural sciences", "experimental study", "Pinus resinosa", "Climate change", "Pinus densiflora", "seasonal variation", "concentration (composition)", "Quercus variabilis", "Oriental oak", "carbon dioxide", "Soil respiration", "04 agricultural and veterinary sciences", "15. Life on land", "air temperature", "carbon flux", "[SDV] Life Sciences [q-bio]", "climate change", "13. Climate action", "coniferous tree", "phytomass", "0401 agriculture", " forestry", " and fisheries", "Experimental warming", "soil moisture", "deciduous tree"]}, "links": [{"href": "https://doi.org/10.1007/s13595-016-0547-4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Annals%20of%20Forest%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s13595-016-0547-4", "name": "item", "description": "10.1007/s13595-016-0547-4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s13595-016-0547-4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-03-24T00:00:00Z"}}, {"id": "10.1016/j.agrformet.2018.02.033", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:15:05Z", "type": "Journal Article", "created": "2018-03-20", "title": "Calibrating an evapotranspiration model using radiometric surface temperature, vegetation cover fraction and near-surface soil moisture data", "description": "An accurate representation of the partitioning between soil evaporation and plant transpiration is an asset for modeling crop evapotranspiration (ET) along the agricultural season. The Two-Surface energy Balance (TSEB) model operates the ET partitioning by using the land surface temperature (LST), vegetation cover fraction (fc), and the Priestley Taylor (PT) assumption that relates transpiration to net radiation via a fixed PT coefficient (\u03b1PT). To help constrain the evaporation/transpiration partition of TSEB, a new model (named TSEB-SM) is developed by using, in addition to LST and fc data, the near-surface soil moisture (SM) as an extra constraint on soil evaporation. An innovative calibration procedure is proposed to retrieve three key parameters: \u03b1PT and the parameters (arss and brss) of a soil resistance formulation. Specifically, arss and brss are retrieved at the seasonal time scale from SM and LST data with fc\u202f \u202f0.5. The new ET model named TSEB-SM is tested over 1 flood- and 2 drip-irrigated wheat fields using in situ data collected during two field experiments in 2002\u20132003 and 2016\u20132017. The calibration algorithm is found to be remarkably stable as \u03b1PT, arss and brss parameters converge rapidly in few (2\u20133) iterations. Retrieved values of \u03b1PT, arss and brss are in the range 0.0\u20131.4, 5.7\u20139.5, and 1.4\u20136.9, respectively. Calibrated daily \u03b1PT mainly follows the phenology of winter wheat crop with a maximum value coincident with the full development of green biomass and a minimum value reached at harvest. The temporal variations of \u03b1PT before senescence are attributed to the dynamics of both root-zone soil moisture. Moreover, the overall (for the three sites) root mean square difference between the ET simulated by TSEB-SM and eddy-covariance measurements is 67\u202fW\u202fm\u22122 (24% relative error), compared to 108\u202fW\u202fm\u22122 (38% relative error) for the original version of TSEB using default parameterization (\u03b1PT\u202f=\u202f1.26). Such a calibration strategy has great potential for applications at multiple scales using remote sensing data including thermal-derived LST, solar reflectance-derived fc and microwave-derived SM.", "keywords": ["Priestley-taylor coefficient", "2. Zero hunger", "550", "TSEB modifid", "[SDE.IE]Environmental Sciences/Environmental Engineering", "0207 environmental engineering", "02 engineering and technology", "Vegetation cover fraction", "15. Life on land", "01 natural sciences", "630", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Turbulent heat fluxes", "Soil moisture", "[SDE.IE] Environmental Sciences/Environmental Engineering", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Land surface temperature", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2018.02.033"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2018.02.033", "name": "item", "description": "10.1016/j.agrformet.2018.02.033", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2018.02.033"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "10.1016/j.agwat.2004.09.030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:15:06Z", "type": "Journal Article", "created": "2005-03-11", "title": "Effects Of Different Management Practices On Soil Conservation And Soil Water In A Rainfed Olive Orchard", "description": "Open Access16 pages, figures, and tables statistics.", "keywords": ["0106 biological sciences", "2. Zero hunger", "Sustainable agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Legumes", "Soil fertility", "01 natural sciences", "6. Clean water", "0401 agriculture", " forestry", " and fisheries", "Plant covers", "Weeds", "Soil moisture"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2004.09.030"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2004.09.030", "name": "item", "description": "10.1016/j.agwat.2004.09.030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2004.09.030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2005-08-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=moisture&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=moisture&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=moisture&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=moisture&offset=50", "hreflang": "en-US"}], "numberMatched": 539, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-08-24T19:13:51.460679Z"}