{"type": "FeatureCollection", "features": [{"id": "10.1007/s00374-010-0462-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:14:26Z", "type": "Journal Article", "created": "2010-04-26", "title": "The Effect Of Different Tree Species On The Chemical And Microbial Properties Of Reclaimed Mine Soils", "description": "The chemical and microbial properties of afforested mine soils are likely to depend on the species composition of the introduced vegetation. This study compared the chemical and microbial properties of organic horizons and the uppermost mineral layers in mine soils under pure pine (Pinus sylvestris), birch (Betula pendula), larch (Larix decidua), alder (Alnus glutinosa), and mixed pine\u2013alder and birch\u2013alder forest stands. The studied properties included soil pH, content of organic C (Corg) and total N (Nt), microbial biomass (Cmic), basal respiration, nitrogen mineralization rate (Min-N), and the activities of dehydrogenase, acid phosphomonoesterase, and urease. Near-infrared spectroscopy (NIR) was used to detect differences in the chemical composition of soil organic matter under the studied forest stands. There were significant differences in Corg and Nt contents between stands in both O and mineral soil horizons and also in the chemical composition of the accumulated organic matter, as indicated by NIR spectra differences. Alder was associated with the largest Corg and Nt accumulation but also with a significant decrease of pH in the mineral soil. Microbial biomass, respiration, the percentage of Corg present as Cmic, Min-N, and dehydrogenase activity were the highest under the birch stand, indicating a positive effect of birch on soil microflora. Admixture of alder to coniferous stand increased basal respiration, Min-N, and activities of dehydrogenase and acid phosphomonoesterase as compared with the pure pine stand. In the O horizon, soil pH and Nt content had the most important effects on all microbial properties. In this horizon, the activities of urease and acid phosphomonoesterase did not depend on microbial biomass. In the mineral layer, however, the amount of accumulated C and microbial biomass were of primary importance for the enzyme activities.", "keywords": ["microbial biomass", "13. Climate action", "soil enzyme activities", "NIR spectroscopy", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "mine soils", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1007/s00374-010-0462-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biology%20and%20Fertility%20of%20Soils", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00374-010-0462-z", "name": "item", "description": "10.1007/s00374-010-0462-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00374-010-0462-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-04-27T00:00:00Z"}}, {"id": "10.1016/j.envpol.2021.118128", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:00Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/10.1016/j.envpol.2021.118128"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envpol.2021.118128", "name": "item", "description": "10.1016/j.envpol.2021.118128", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envpol.2021.118128"}, {"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": "10.1016/j.still.2008.11.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:00Z", "type": "Journal Article", "created": "2008-12-31", "title": "Carbon And Nitrogen Stocks In A Brazilian Clayey Oxisol: 13-Year Effects Of Integrated Crop-Livestock Management Systems", "description": "Abstract   Integrated crop\u2013livestock management systems (ICLS) have been increasingly recommended in Brazilian agroecosystems. However, knowledge of their effect on soil organic carbon (SOC) and total nitrogen (TN) concentrations and stocks is still limited. The study was undertaken to evaluate the effects of ICLS under two tillage and fertilization regimes on SOC and TN concentrations and stocks in the 0\u201330\u00a0cm soil layer, in comparison with continuous crops or pasture. The following soil management systems were studied: continuous pasture; continuous crop; 4 years\u2019 crop followed by 4 years\u2019 pasture and vice-versa. The adjacent native Cerrado area was used as a control. Under the rotation and continuous crop systems there were two levels of soil tillage (conventional and no-tillage) and fertility (maintenance and corrective fertility). The stock calculations were done using the equivalent soil mass approach. The land use systems had a significant effect on the concentrations of SOC and TN in the soil, but no effect was observed for the soil tillage and fertilizer regimes. For these two latter, some significant discrepancies appeared in the distribution of SOC and TN concentrations in the 0\u201330\u00a0cm layer. Carbon storage was 60.87\u00a0Mg\u00a0ha \u22121  under Cerrado, and ranged from 52.21\u00a0Mg\u00a0ha \u22121  under the ICLS rotation to 59.89\u00a0Mg\u00a0ha \u22121  with continuous cropping. The decrease in SOC stocks was approximately 8.5 and 7.5\u00a0Mg\u00a0ha \u22121 , or 14 and 12%, for continuous pasture and ICLS respectively. No-tillage for 10 years after the conversion of conventional tillage to no-tillage under the continuous crop system, and 13 years of conventional tillage in continuous cropping did not result in significant changes in SOC stocks. The SOC and TN stocks in surface layers, using the equivalent soil mass approach rather than the equivalent depth, stress the differences induced by the calculation method. As soil compaction is the principal feature of variability of stocks determinations, the thickness should be avoid in these types of studies.", "keywords": ["Carbon and nitrogen sequestration", "Crop-pasture rotation", "2. Zero hunger", "Brazilian Cerrado", "No-tillage", "NIR spectroscopy", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "630"], "contacts": [{"organization": "Marchao, R. L., /Becquer, Thierry, /Brunet, Didier, Balbino, L. C., Vilela, L., /Brossard, Michel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.still.2008.11.002"}, {"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.2008.11.002", "name": "item", "description": "10.1016/j.still.2008.11.002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2008.11.002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-05-01T00:00:00Z"}}, {"id": "10.3390/rs13224615", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:50Z", "type": "Journal Article", "created": "2021-11-17", "title": "Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years.</p></article>", "keywords": ["PROVINCE", "Landsat 7", "analysis", "Science", "random forest (RF)", "MOISTURE", "01 natural sciences", "NIR SPECTROSCOPY", "spatiotemporal analysis", "AGRICULTURAL SOILS", "spatiotemporal", "0105 earth and related environmental sciences", "2. Zero hunger", "RANGE", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "3. Good health", "MULTIVARIATE", "TOPSOILS", "13. Climate action", "Earth and Environmental Sciences", "soil heavy metal; Landsat 7; partial least squares regression (PLSR); random forest (RF); support vector machine (SVM); spatiotemporal analysis", "0401 agriculture", " forestry", " and fisheries", "support vector machine (SVM)", "soil heavy metal", "partial least squares regression (PLSR)"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/22/4615/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/22/4615/pdf"}, {"href": "https://doi.org/10.3390/rs13224615"}, {"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/rs13224615", "name": "item", "description": "10.3390/rs13224615", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13224615"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-16T00:00:00Z"}}, {"id": "1854/LU-01GM39MMFY2YP4FTDY102R50HB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:48Z", "type": "Journal Article", "created": "2021-11-17", "title": "Spatiotemporal Prediction and Mapping of Heavy Metals at Regional Scale Using Regression Methods and Landsat 7", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years.</p></article>", "keywords": ["Technology", "PROVINCE", "Landsat 7", "analysis", "Science", "Environmental Sciences & Ecology", "random forest (RF)", "MOISTURE", "01 natural sciences", "NIR SPECTROSCOPY", "0203 Classical Physics", "Remote Sensing", "0909 Geomatic Engineering", "spatiotemporal analysis", "AGRICULTURAL SOILS", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "spatiotemporal", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "RANGE", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "3. Good health", "MULTIVARIATE", "TOPSOILS", "13. Climate action", "Earth and Environmental Sciences", "Physical Sciences", "soil heavy metal; Landsat 7; partial least squares regression (PLSR); random forest (RF); support vector machine (SVM); spatiotemporal analysis", "0401 agriculture", " forestry", " and fisheries", "support vector machine (SVM)", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "soil heavy metal", "partial least squares regression (PLSR)", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/22/4615/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/22/4615/pdf"}, {"href": "https://doi.org/1854/LU-01GM39MMFY2YP4FTDY102R50HB"}, {"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": "1854/LU-01GM39MMFY2YP4FTDY102R50HB", "name": "item", "description": "1854/LU-01GM39MMFY2YP4FTDY102R50HB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01GM39MMFY2YP4FTDY102R50HB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-16T00:00:00Z"}}, {"id": "1854/LU-8720112", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:49Z", "type": "Journal Article", "created": "2021-09-09", "title": "Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy", "description": "Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.", "keywords": ["DIFFUSE-REFLECTANCE SPECTROSCOPY", "HUMAN HEALTH", "PREDICTION", "POTENTIALLY TOXIC ELEMENTS", "Boruta algorithm", "01 natural sciences", "Visible-to-near-infrared spectroscopy", "NIR SPECTROSCOPY", "Soil", "ORGANIC-CARBON", "Machine learning", "11. Sustainability", "Soil Pollutants", "Least-Squares Analysis", "0105 earth and related environmental sciences", "Spectroscopy", " Near-Infrared", "RANDOM FOREST", "Urban and suburban soil Cd contamination", "04 agricultural and veterinary sciences", "15. Life on land", "QUANTITATIVE-ANALYSIS", "6. Clean water", "RIVER DELTA", "13. Climate action", "Earth and Environmental Sciences", "Synthetic minority over-sampling technique", "0401 agriculture", " forestry", " and fisheries", "HEAVY-METAL CONCENTRATIONS", "Cadmium"]}, "links": [{"href": "https://doi.org/1854/LU-8720112"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8720112", "name": "item", "description": "1854/LU-8720112", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8720112"}, {"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"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NIR+SPECTROSCOPY&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=NIR+SPECTROSCOPY&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=NIR+SPECTROSCOPY&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=NIR+SPECTROSCOPY&offset=6", "hreflang": "en-US"}], "numberMatched": 6, "numberReturned": 6, "distributedFeatures": [], "timeStamp": "2026-05-25T07:20:10.768354Z"}