{"type": "FeatureCollection", "features": [{"id": "10.1016/j.still.2008.01.009", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:46Z", "type": "Journal Article", "created": "2008-03-11", "title": "Effect Of Water Erosion And Cultivation On The Soil Carbon Stock In A Semiarid Area Of South-East Spain", "description": "Open AccessAn experiment to evaluate the impact of water erosion and cultivation on the soil carbon dynamic and carbon stock in a semiarid area of South-East Spain was carried out. The study was performed under three different land use scenarios: (1) forest; (2) abandoned agricultural field; and (3) non-irrigated olive grove. Experimental erosion plots (in olive grove and forest) and sediment traps (in the abandoned area) were used to determine the carbon pools associated with sediments and runoff after each event occurring between September 2005 and November 2006. Change in land use from forest to cultivated enhanced the risk of erosion (total soil loss in olive cropland seven-fold higher than in the forest area) and reduced the soil carbon stock (in the top 5 cm) by about 50%. Mineral-associated organic carbon (MOC) represented the main C pool in the three study areas although its contribution to soil organic carbon (SOC) was significantly higher in the disturbed areas (78.91 \u00b1 1.81% and 77.29 \u00b1 1.21% for abandoned and olive area, respectively) than in the forest area (66.05 \u00b1 3.11%). In both, the olive and abandoned soils, the reduction in particulate organic carbon (POC) was proportionally greater than the decline in MOC. The higher degree of sediment production in the olive cropland had an important consequence in terms of the carbon losses induced by erosion compared to the abandoned and forest plots. Thus, the total OC lost by erosion in the sediments was around three times higher in the cultivated (5.12 g C m\u22122) than the forest plot (1.77 g C m\u22122). The abandoned area displayed similar OC losses as a result of erosion as the forest plot (in the measurement period: 2.07 g C m\u22122, 0.63 g C m\u22122 and 0.65 g C m\u22122 for olive, forest and abandoned area, respectively). MOC represented the highest percentage of contribution to total sediment OC for all the events analysed and in all uses being, in general these values higher in Olive (74\u201390%) than in the other two areas (55\u201380%). The organic carbon lost was basically linked to the solid phase in the three land uses, although the contribution of DOC to total carbon loss by erosion varied widely with each event. Data from this study show that the more labile OC fraction (POC) lost in soil in the cultivated area was mainly due to the effect of cultivation (low overall biomass production and residue return together with high C mineralization) rather than to water erosion, given that the major part of the OC lost in sediments was in the form of MOC.", "keywords": ["2. Zero hunger", "Erosion", "Soil organic carbon", "13. Climate action", "Semi-arid areas", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Particulate organic carbon", "Eroded organic carbon"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2008.01.009"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2008.01.009", "name": "item", "description": "10.1016/j.still.2008.01.009", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2008.01.009"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-04-01T00:00:00Z"}}, {"id": "10.1016/j.still.2009.05.007", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:47Z", "type": "Journal Article", "created": "2009-06-18", "title": "Effect Of Long-Term Conservation Tillage On Soil Biochemical Properties In Mediterranean Spanish Areas", "description": "Open AccessPeer reviewed", "keywords": ["Soil microbial biomass carbon", "2. Zero hunger", "Soil organic carbon", "Semi-arid areas", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Soil enzymatic activities", "15. Life on land", "Soil tillage", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2009.05.007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2009.05.007", "name": "item", "description": "10.1016/j.still.2009.05.007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2009.05.007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-09-01T00:00:00Z"}}, {"id": "10.3390/rs13020305", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard\u2013Stone (K\u2013S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21\u20130.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07\u201379.6 dS m\u22121), the spectral reflectance of salinized soil in the MSI data ranged from 0.09\u20130.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m\u22121, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.3390/rs13020305"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13020305", "name": "item", "description": "10.3390/rs13020305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13020305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.3390/d12060234", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:41Z", "type": "Journal Article", "created": "2020-06-12", "title": "Does Arbuscular Mycorrhiza Determine Soil Microbial Functionality in Nutrient-Limited Mediterranean Arid Ecosystems?", "description": "

Arbuscular mycorrhizal fungi (AMF) are determinant for the performance of plant communities and for the functionality of terrestrial ecosystems. In natural ecosystems, grazing can have a major impact on mycorrhizal fungi and consequently on plant growth. The objective of this study was to evaluate the statements referred above in Mediterranean arid areas in Tunisia. Root samples and rhizosphere soils of five dominant herbaceous plants were studied at six distinct arid sites differing on soil proprieties and grazing intensity. At each site, chemical and dynamic properties of the soil were characterized as well as the AMF colonization intensity and the soil functionality. Results showed that the mycorrhizal frequency and intensity and spore density, varied between plants in the same site and, for each plant, between sites and evidenced a positive effect of mycorrhized plants on soil microbial activity. Grazing and soil properties strongly affected AMF composition and the soil microbial and biochemical dynamics, which presented the lowest values at the sites with the highest grazing intensities. In conclusion, these results demonstrate that AMF improve soil biological properties, supporting the hypothesis that mycorrhiza and grazing compete for plant photosynthates, and highlight the importance of mycorrhizal symbiosis towards soil functionality under arid conditions.

", "keywords": ["2. Zero hunger", "arid areas", "conserved areas", "QH301-705.5", "mycorrhiza", "0401 agriculture", " forestry", " and fisheries", "arbuscular mycorrhizal fungi", "grazing", "04 agricultural and veterinary sciences", "14. Life underwater", "Biology (General)", "15. Life on land", "biological properties"]}, "links": [{"href": "http://www.mdpi.com/1424-2818/12/6/234/pdf"}, {"href": "https://www.mdpi.com/1424-2818/12/6/234/pdf"}, {"href": "https://doi.org/10.3390/d12060234"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Diversity", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/d12060234", "name": "item", "description": "10.3390/d12060234", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/d12060234"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-10T00:00:00Z"}}, {"id": "10.3390/rs13204063", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:51Z", "type": "Journal Article", "created": "2021-10-12", "title": "Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades", "description": "

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land\u2013climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

", "keywords": ["2. Zero hunger", "arid areas", "Science", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "climate change", "13. Climate action", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "MOD13A2", "arid areas; vegetation variation; climate change; MOD13A2; Google Earth Engine", "Google Earth Engine", "vegetation variation", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/20/4063/pdf"}, {"href": "https://doi.org/10.3390/rs13204063"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13204063", "name": "item", "description": "10.3390/rs13204063", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13204063"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-11T00:00:00Z"}}, {"id": "10.5281/zenodo.8090887", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:42Z", "type": "Journal Article", "created": "2021-10-11", "title": "Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades", "description": "

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land\u2013climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

", "keywords": ["2. Zero hunger", "arid areas", "Science", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "climate change", "13. Climate action", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "MOD13A2", "arid areas; vegetation variation; climate change; MOD13A2; Google Earth Engine", "Google Earth Engine", "vegetation variation", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/20/4063/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090887"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090887", "name": "item", "description": "10.5281/zenodo.8090887", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090887"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-11T00:00:00Z"}}, {"id": "10.5281/zenodo.8091638", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:42Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard\u2013Stone (K\u2013S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21\u20130.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07\u201379.6 dS m\u22121), the spectral reflectance of salinized soil in the MSI data ranged from 0.09\u20130.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m\u22121, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091638"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091638", "name": "item", "description": "10.5281/zenodo.8091638", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091638"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}, {"id": "10.5281/zenodo.8194045", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:45Z", "type": "Dataset", "title": "Supplementary material/Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, and mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid climate. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", " soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Fatiha, Faraoun", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194045"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194045", "name": "item", "description": "10.5281/zenodo.8194045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8194083", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:45Z", "type": "Dataset", "title": "Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid area. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", "soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Bekhit, Nadia, Faraoun, Fatiha, Bennabi, Faiza, Abbassia Ayache, Toumi, Fawzia, Mlih, Rawan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194083"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194083", "name": "item", "description": "10.5281/zenodo.8194083", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194083"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10451/49481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:25:48Z", "type": "Journal Article", "created": "2020-06-12", "title": "Does Arbuscular Mycorrhiza Determine Soil Microbial Functionality in Nutrient-Limited Mediterranean Arid Ecosystems?", "description": "

Arbuscular mycorrhizal fungi (AMF) are determinant for the performance of plant communities and for the functionality of terrestrial ecosystems. In natural ecosystems, grazing can have a major impact on mycorrhizal fungi and consequently on plant growth. The objective of this study was to evaluate the statements referred above in Mediterranean arid areas in Tunisia. Root samples and rhizosphere soils of five dominant herbaceous plants were studied at six distinct arid sites differing on soil proprieties and grazing intensity. At each site, chemical and dynamic properties of the soil were characterized as well as the AMF colonization intensity and the soil functionality. Results showed that the mycorrhizal frequency and intensity and spore density, varied between plants in the same site and, for each plant, between sites and evidenced a positive effect of mycorrhized plants on soil microbial activity. Grazing and soil properties strongly affected AMF composition and the soil microbial and biochemical dynamics, which presented the lowest values at the sites with the highest grazing intensities. In conclusion, these results demonstrate that AMF improve soil biological properties, supporting the hypothesis that mycorrhiza and grazing compete for plant photosynthates, and highlight the importance of mycorrhizal symbiosis towards soil functionality under arid conditions.

", "keywords": ["2. Zero hunger", "arid areas", "conserved areas", "QH301-705.5", "mycorrhiza", "0401 agriculture", " forestry", " and fisheries", "arbuscular mycorrhizal fungi", "grazing", "14. Life underwater", "04 agricultural and veterinary sciences", "Biology (General)", "15. Life on land", "biological properties"]}, "links": [{"href": "http://www.mdpi.com/1424-2818/12/6/234/pdf"}, {"href": "https://repositorio.ulisboa.pt/bitstream/10451/49481/1/Mahmoudi%20et%20al%202020%20-%20Does%20Arbuscular%20Mycorrhiza%20Determine%20Soil%20Microbial%20Functionality%20in%20Nutrient-Limited%20Mediterranean%20Arid%20Ecosystems.pdf"}, {"href": "https://www.mdpi.com/1424-2818/12/6/234/pdf"}, {"href": "https://doi.org/10451/49481"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Diversity", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10451/49481", "name": "item", "description": "10451/49481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10451/49481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-10T00:00:00Z"}}, {"id": "3206556723", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:27:25Z", "type": "Journal Article", "created": "2021-10-12", "title": "Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades", "description": "

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land\u2013climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

", "keywords": ["2. Zero hunger", "arid areas", "Science", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "climate change", "13. Climate action", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "MOD13A2", "arid areas; vegetation variation; climate change; MOD13A2; Google Earth Engine", "Google Earth Engine", "vegetation variation", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/20/4063/pdf"}, {"href": "https://doi.org/3206556723"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3206556723", "name": "item", "description": "3206556723", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3206556723"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-11T00:00:00Z"}}, {"id": "3122338430", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:18Z", "type": "Journal Article", "created": "2021-01-20", "title": "Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China", "description": "

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard\u2013Stone (K\u2013S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21\u20130.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07\u201379.6 dS m\u22121), the spectral reflectance of salinized soil in the MSI data ranged from 0.09\u20130.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m\u22121, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.

", "keywords": ["2. Zero hunger", "soil salinization; Sentinel-2 MSI; remote sensing; machine learning; arid area", "Science", "soil salinization", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2 MSI", "6. Clean water", "remote sensing", "machine learning", "arid area", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/2/305/pdf"}, {"href": "https://doi.org/3122338430"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3122338430", "name": "item", "description": "3122338430", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3122338430"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-17T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=arid+area&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=arid+area&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=arid+area&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=arid+area&offset=12", "hreflang": "en-US"}], "numberMatched": 12, "numberReturned": 12, "distributedFeatures": [], "timeStamp": "2026-06-24T13:20:33.009177Z"}