{"type": "FeatureCollection", "features": [{"id": "10.1016/j.scitotenv.2021.152524", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:16:48Z", "type": "Journal Article", "created": "2021-12-23", "title": "Use of remote sensing to evaluate the effects of environmental factors on soil salinity in a semi-arid area", "description": "The global water crisis, driven by water scarcity and water quality deterioration, is expected to continue and intensify in dry and overpopulated areas, and will play a critical role in meeting future agricultural demands. Sustainability of agriculture irrigated with low quality water will require a comprehensive approach to soil, water, and crop management consisting of site- and situation-specific preventive measures and management strategies. Other problem related with water quality deterioration is soil salinization. Around 1Bha globally are salinized and soil salinization may be accelerating for several reasons including the changing climate. The consequences of climate change on soil salinization need to be monitored and mapped and, in this sense, remote sensing has been successfully applied to soil salinity monitoring. Although many issues remain to be resolved, some as important as the imbalance between ground-based measurements and satellite data. The main objective of this paper was to determine the influence of environmental factors on salinity from natural causes, and its effect on irrigated agriculture with degraded water. The study was developed on Campo de Cartagena, an intensive water-efficient irrigated area which main fruit tree is citrus (30%), a sensible crop to salinity. Nine representative citrus farms were selected, soil samples were analysed and different remote sensing indices and sets of environmental data were applied. Despite the heterogeneity between variables found by the descriptive analysis of the data, the relationship between farms, soil salinity and environmental data showed that applied salinity spectral indices were valid to detect soil salinity in citrus trees. Also, a set of environmental characterization provided useful information to determine the variables that most influence primary salinity in crops. Although the data extracted from spatial analysis indicated that to apply soil salinity predictive models, other variables related to agricultural management practices must be incorporated.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Agricultural", "Salinity", "550", "Degraded water", "Secondary soil salinization", "Crops", "Agriculture", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "6. Clean water", "12. Responsible consumption", "Soil", "13. Climate action", "Remote Sensing Technology", "11. Sustainability", "Irrigated agriculture", "0401 agriculture", " forestry", " and fisheries", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2021.152524"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2021.152524", "name": "item", "description": "10.1016/j.scitotenv.2021.152524", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2021.152524"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "10.1029/2021GB007285", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:17:35Z", "type": "Journal Article", "created": "2022-06-07", "title": "Differential Responses of Soil Extracellular Enzyme Activities to Salinization: Implications for Soil Carbon Cycling in Tidal Wetlands", "description": "Abstract

Rising sea levels are expected to cause salinization in many historically low\uffe2\uff80\uff90salinity tidal wetlands. However, the response of soil extracellular enzyme activities to salinization in tidal wetlands and their links to soil organic carbon (SOC) decomposition are largely unknown. Here, we conducted a global meta\uffe2\uff80\uff90analysis to examine the effect of salinization on hydrolytic and oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities and their relationships with SOC storage in tidal wetlands. The results showed that salinization reduced hydrolytic carbon\uffe2\uff80\uff90acquiring enzyme activities by 33% but increased oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities by 15%. Meanwhile, salinization decreased SOC storage by 14%, and the change in SOC storage was negatively associated with oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities. These results indicate an important role for oxidative carbon\uffe2\uff80\uff90acquiring enzymes in SOC loss in tidal wetlands. Moreover, the effect of salinization on oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities logarithmically declined with increasing salinization, implying that SOC loss was highly sensitive to even minor increases in salinity at the initial stage of salinization. Given increasing salinization over time with rising sea levels in most global tidal wetlands, our results suggest that SOC loss might be greater during early than later stages. Consequently, salinization\uffe2\uff80\uff90induced SOC loss may be overstated in the long term if extrapolations are merely based on a constant SOC loss rate determined from short\uffe2\uff80\uff90term studies. Future modeling frameworks should account for this changing sensitivity of microbially mediated SOC loss with increasing salinization over time.Rising sea levels are expected to cause salinization in many historically low\uffe2\uff80\uff90salinity tidal wetlands. However, the response of soil extracellular enzyme activities to salinization in tidal wetlands and their links to soil organic carbon (SOC) decomposition are largely unknown. Here, we conducted a global meta\uffe2\uff80\uff90analysis to examine the effect of salinization on hydrolytic and oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities and their relationships with SOC storage in tidal wetlands. The results showed that salinization reduced hydrolytic carbon\uffe2\uff80\uff90acquiring enzyme activities by 33% but increased oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities by 15%. Meanwhile, salinization decreased SOC storage by 14%, and the change in SOC storage was negatively associated with oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities. These results indicate an important role for oxidative carbon\uffe2\uff80\uff90acquiring enzymes in SOC loss in tidal wetlands. Moreover, the effect of salinization on oxidative carbon\uffe2\uff80\uff90acquiring enzyme activities logarithmically declined with increasing salinization, implying that SOC loss was highly sensitive to even minor increases in salinity at the initial stage of salinization. Given increasing salinization over time with rising sea levels in most global tidal wetlands, our results suggest that SOC loss might be greater during early than later stages. Consequently, salinization\uffe2\uff80\uff90induced SOC loss may be overstated in the long term if extrapolations are merely based on a constant SOC loss rate determined from short\uffe2\uff80\uff90term studies. Future modeling frameworks should account for this changing sensitivity of microbially mediated SOC loss with increasing salinization over time.Some microbes enhance stress tolerance in plants by minimizing plant ethylene levels via degradation of its immediate precursor, 1-aminocyclopropane-1-carboxylate (ACC), in the rhizosphere. In return, ACC is used by these microbes as a source of nitrogen. This mutualistic relationship between plants and microbes may be used to promote soil properties in stressful environments. In this study, we tested the hypothesis that amendments of ACC in soils reshape the structure of soil microbiome and alleviate the negative impacts of salinity on soil properties. We treated non-saline and artificially-developed saline soils with ACC in different concentrations for 14 days. The structure of soil microbiome, soil microbial properties and productivity were examined. Our results revealed that microbial composition of bacteria, archaea and fungi in saline soils was affected by ACC amendments; whereas community composition in non-saline soils was not affected. The amendments of ACC could not fully counteract the negative effects of salinity on soil microbial activities and productivity, but increased the abundance of ACC deaminase-encoding gene (acdS), enhanced soil microbial respiration, enzymatic activity, nitrogen and carbon cycling potentials and Arabidopsis biomass in saline soils. Collectively, our study indicates that ACC amendments in soils could efficiently ameliorate salinity impacts on soil properties and plant biomass production.

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": "10.5281/zenodo.10402591", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:56Z", "type": "Report", "title": "Currently available assessments of soil threats and ecosystem services: data, metadata, and methodologies - update", "description": "Deliverable of the EJPSoil project SERENA (Soil Ecosystem Services and soil threats modelling and mapping): Short descriptions of available assessments of selected soil threats and soil-based ecosystem services provided by the participating member states. The internal EJPSoil project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.", "keywords": ["Task 3.1", "Soil drought", "Salinization", "Habitat for biodiversity", "Pest and disease control", "15. Life on land", "Loss of diversity", "SERENA", "Environmental pollution control", "Soil contamination", "13. Climate action", "EJPSoil", "WP3", "D3.1.2", "Waterlogging", "Soil acidification"], "contacts": [{"organization": "Michel, Kerstin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10402591"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10402591", "name": "item", "description": "10.5281/zenodo.10402591", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10402591"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.10402592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:56Z", "type": "Report", "title": "Currently available assessments of soil threats and ecosystem services: data, metadata, and methodologies - update", "description": "Deliverable of the EJPSoil project SERENA (Soil Ecosystem Services and soil threats modelling and mapping): Short descriptions of available assessments of selected soil threats and soil-based ecosystem services provided by the participating member states. The internal EJPSoil project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.", "keywords": ["Soil-threat", "Task 3.1", "Soil drought", "Salinization", "Habitat for biodiversity", "Pest and disease control", "15. Life on land", "Loss of diversity", "Assessment", "SERENA", "Environmental pollution control", "Soil contamination", "13. Climate action", "EJPSoil", "WP3", "Soil-based ecosystem service", "D3.1.2", "Waterlogging", "Soil acidification"], "contacts": [{"organization": "Michel, Kerstin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10402592"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10402592", "name": "item", "description": "10.5281/zenodo.10402592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10402592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-18T00:00:00Z"}}, {"id": "10.5281/zenodo.13945384", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:22:22Z", "type": "Report", "title": "Evaluation of soil threats and ecosystem service evolution under climate, land use or management changes.", "description": "The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. Based on an intensive literature review and results from previous experiences in member states a scenario framework was developed (climate, land use, and management changes) and common methodologies (statistical methods, simple and/or more sophisticated models) were identified, used or validated to forecast how selected soil ecosystem services (SES) and soil threats (ST) will change according to climate, land-use and management changes. In contrast to WP5 we focus in WP3/Task 3 on forecasts of changes of various soil indicators on site, regional or national scale, and could rely on soil maps with high resolution that are maintained by several member states. Three countries out of 6 were able to give predictions for changes on the SES \u201cGHG and climate regulation\u201d. Two countries were working on the SES \u201cPrimary biomass production\u201d and could predict changes in \u201cErosion control\u201d on a national scale. \u201cHydrological control\u201d and \u201cEnvironmental pollution control\u201d was predicted in one country in 2 regions. Changes in climate, land management or land use change and their effects on ST could be predicted less often. Three countries could predict the effects ofchanges on \u201cSoil organic carbon loss\u201d and on \u201cSoil compaction\u201d, two countries estimated the loss ofsoil via erosion. Only one country each could predict effects of changes on \u201cSoil nutrient imbalance\u201dand \u201cSoil acidification\u201d and \u201cSoil sealing\u201d. Either no appropriate model or no experience was availablefor the SES \u201cHabitat for biodiversity\u201d and \u201cPest and disease control\u201d and for the ST\u2019s \u201cWaterlogging\u201d,\u201cSoil contamination\u201d, \u201cLoss of diversity\u201d and \u201cSalinization\u201d.", "keywords": ["Estonia", "land use change", "Task 3.3", "soil nutrient imbalance", "salinization", "management change", "D3.4", "soil", "Environmental pollution control", "loss of diversity", "soil compaction", "soil sealing", "Erosion control", "Soil threats", "habitat for biodiversity", "loss of soil", "Primary biomass production", "Czech Republic", "agriculture", "GHG and climate regulation", "Hydrological control", "scenario analysis", "Grant n. 862695", "Soil ecosystem services", "waterlogging", "soil organic carbon loss", "climate change", "SERENA EJPSOIL", "WP3", "Austria", "pest and disease control", "France", "Poland", "soil acidification", "Ireland", "soil contamination"], "contacts": [{"organization": "Kitzler, Barbara", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13945384"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13945384", "name": "item", "description": "10.5281/zenodo.13945384", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13945384"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-17T00:00:00Z"}}, {"id": "10.5281/zenodo.13945383", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:22:22Z", "type": "Report", "created": "2024-10-22", "title": "Evaluation of soil threats and ecosystem service evolution under climate, land use or management changes.", "description": "The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. Based on an intensive literature review and results from previous experiences in member states a scenario framework was developed (climate, land use, and management changes) and common methodologies (statistical methods, simple and/or more sophisticated models) were identified, used or validated to forecast how selected soil ecosystem services (SES) and soil threats (ST) will change according to climate, land-use and management changes. In contrast to WP5 we focus in WP3/Task 3 on forecasts of changes of various soil indicators on site, regional or national scale, and could rely on soil maps with high resolution that are maintained by several member states. Three countries out of 6 were able to give predictions for changes on the SES \u201cGHG and climate regulation\u201d. Two countries were working on the SES \u201cPrimary biomass production\u201d and could predict changes in \u201cErosion control\u201d on a national scale. \u201cHydrological control\u201d and \u201cEnvironmental pollution control\u201d was predicted in one country in 2 regions. Changes in climate, land management or land use change and their effects on ST could be predicted less often. Three countries could predict the effects ofchanges on \u201cSoil organic carbon loss\u201d and on \u201cSoil compaction\u201d, two countries estimated the loss ofsoil via erosion. Only one country each could predict effects of changes on \u201cSoil nutrient imbalance\u201dand \u201cSoil acidification\u201d and \u201cSoil sealing\u201d. Either no appropriate model or no experience was availablefor the SES \u201cHabitat for biodiversity\u201d and \u201cPest and disease control\u201d and for the ST\u2019s \u201cWaterlogging\u201d,\u201cSoil contamination\u201d, \u201cLoss of diversity\u201d and \u201cSalinization\u201d.", "keywords": ["Estonia", "land use change", "Task 3.3", "soil nutrient imbalance", "salinization", "management change", "D3.4", "soil", "Environmental pollution control", "loss of diversity", "soil compaction", "soil sealing", "Erosion control", "Soil threats", "habitat for biodiversity", "loss of soil", "Primary biomass production", "Czech Republic", "agriculture", "GHG and climate regulation", "Hydrological control", "scenario analysis", "Grant n. 862695", "Soil ecosystem services", "waterlogging", "soil organic carbon loss", "climate change", "SERENA EJPSOIL", "WP3", "Austria", "pest and disease control", "France", "Poland", "soil acidification", "Ireland", "soil contamination"], "contacts": [{"organization": "Kitzler, Barbara", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13945383"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13945383", "name": "item", "description": "10.5281/zenodo.13945383", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13945383"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-17T00:00:00Z"}}, {"id": "10.5281/zenodo.8091638", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-01T16:23:33Z", "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": "11586/391721", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:52Z", "type": "Journal Article", "created": "2021-12-23", "title": "Use of remote sensing to evaluate the effects of environmental factors on soil salinity in a semi-arid area", "description": "The global water crisis, driven by water scarcity and water quality deterioration, is expected to continue and intensify in dry and overpopulated areas, and will play a critical role in meeting future agricultural demands. Sustainability of agriculture irrigated with low quality water will require a comprehensive approach to soil, water, and crop management consisting of site- and situation-specific preventive measures and management strategies. Other problem related with water quality deterioration is soil salinization. Around 1Bha globally are salinized and soil salinization may be accelerating for several reasons including the changing climate. The consequences of climate change on soil salinization need to be monitored and mapped and, in this sense, remote sensing has been successfully applied to soil salinity monitoring. Although many issues remain to be resolved, some as important as the imbalance between ground-based measurements and satellite data. The main objective of this paper was to determine the influence of environmental factors on salinity from natural causes, and its effect on irrigated agriculture with degraded water. The study was developed on Campo de Cartagena, an intensive water-efficient irrigated area which main fruit tree is citrus (30%), a sensible crop to salinity. Nine representative citrus farms were selected, soil samples were analysed and different remote sensing indices and sets of environmental data were applied. Despite the heterogeneity between variables found by the descriptive analysis of the data, the relationship between farms, soil salinity and environmental data showed that applied salinity spectral indices were valid to detect soil salinity in citrus trees. Also, a set of environmental characterization provided useful information to determine the variables that most influence primary salinity in crops. Although the data extracted from spatial analysis indicated that to apply soil salinity predictive models, other variables related to agricultural management practices must be incorporated.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Agricultural", "Salinity", "550", "Degraded water", "Secondary soil salinization", "Crops", "Agriculture", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "6. Clean water", "12. Responsible consumption", "Soil", "13. Climate action", "Remote Sensing Technology", "11. Sustainability", "Irrigated agriculture", "0401 agriculture", " forestry", " and fisheries", "Environmental Monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/11586/391721"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11586/391721", "name": "item", "description": "11586/391721", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11586/391721"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-01T00:00:00Z"}}, {"id": "1959.7/uws:51687", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:25:05Z", "type": "Journal Article", "created": "2019-05-03", "title": "Soil amendments with ethylene precursor alleviate negative impacts of salinity on soil microbial properties and productivity", "description": "Abstract

Some microbes enhance stress tolerance in plants by minimizing plant ethylene levels via degradation of its immediate precursor, 1-aminocyclopropane-1-carboxylate (ACC), in the rhizosphere. In return, ACC is used by these microbes as a source of nitrogen. This mutualistic relationship between plants and microbes may be used to promote soil properties in stressful environments. In this study, we tested the hypothesis that amendments of ACC in soils reshape the structure of soil microbiome and alleviate the negative impacts of salinity on soil properties. We treated non-saline and artificially-developed saline soils with ACC in different concentrations for 14 days. The structure of soil microbiome, soil microbial properties and productivity were examined. Our results revealed that microbial composition of bacteria, archaea and fungi in saline soils was affected by ACC amendments; whereas community composition in non-saline soils was not affected. The amendments of ACC could not fully counteract the negative effects of salinity on soil microbial activities and productivity, but increased the abundance of ACC deaminase-encoding gene (acdS), enhanced soil microbial respiration, enzymatic activity, nitrogen and carbon cycling potentials and Arabidopsis biomass in saline soils. Collectively, our study indicates that ACC amendments in soils could efficiently ameliorate salinity impacts on soil properties and plant biomass production.

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.

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