{"type": "FeatureCollection", "features": [{"id": "10.1016/j.scitotenv.2022.156582", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:25Z", "type": "Journal Article", "created": "2022-06-14", "title": "Potential of visible and near infrared spectroscopy coupled with machine learning for predicting soil metal concentrations at the regional scale", "description": "Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R2p = 0.76), Co (R2p = 0.77), Cd (R2p = 0.64) and Pb (R2p = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R2p = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R2p = 0.70), while it best predicted Cd with PLSR with an R2p value of 0.67, which is of value for regional survey.", "keywords": ["Spectroscopy", " Near-Infrared", "Support Vector Machine", "RANGE", "Machine", "Machine learning modelling", "learning modelling", "REFLECTANCE SPECTROSCOPY", "CONTAMINATION", "Soil", "Lead", "Soil contamination", "Nickel", "Metals", "Earth and Environmental Sciences", "Soil Pollutants", "Chemometrics", "Cadmium", "Near-infrared spectra"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2022.156582"}, {"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.2022.156582", "name": "item", "description": "10.1016/j.scitotenv.2022.156582", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2022.156582"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-01T00:00:00Z"}}, {"id": "10.1039/d1ra03337a", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:32Z", "type": "Journal Article", "created": "2021-09-10", "title": "Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease", "description": "

Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

", "keywords": ["electronic nose", "Linear discriminant analysis", "Principal component analysis", "Breath analysis", "02 engineering and technology", "sensors", "Supported Vectror Machine", "01 natural sciences", "nanotubes", "Ammonia; Biomarkers; Carbon nanotubes; Classification (of information); Clustering algorithms; Molecules; Nitrogen oxides; Principal component analysis; Sulfur compounds; Support vector machines", "0104 chemical sciences", "3. Good health", "breathomics", "Chemistry", "SWCNTs", "COPD", "ta318", "e-nose", "0210 nano-technology", "ta215"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/536855/1/RSC%20Adv._2021.pdf"}, {"href": "https://boa.unimib.it/bitstream/10281/517427/2/d1ra03337a.pdf%3b"}, {"href": "https://publicatt.unicatt.it/bitstream/10807/190102/1/d1ra03337a.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA03337A"}, {"href": "https://doi.org/10.1039/d1ra03337a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/RSC%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1039/d1ra03337a", "name": "item", "description": "10.1039/d1ra03337a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1039/d1ra03337a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.1111/wre.12255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:19:53Z", "type": "Journal Article", "created": "2017-05-25", "title": "Big Data for weed control and crop protection", "description": "Summary

Farmers have access to many data\uffe2\uff80\uff90intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide\uffe2\uff80\uff90resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

", "keywords": ["[SDE] Environmental Sciences", "support vector machine; vis-NIR; MIR; outer product analysis; soil classification", "2. Zero hunger", "Science", "Q", "vis-NIR", "MIR", "soil classification", "04 agricultural and veterinary sciences", "15. Life on land", "771", "630", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "outer product analysis"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://doi.org/10.3390/rs12091512"}, {"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/rs12091512", "name": "item", "description": "10.3390/rs12091512", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12091512"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-09T00:00:00Z"}}, {"id": "10.3390/rs13224615", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "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": "

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.

", "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": "3025754366", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:27:11Z", "type": "Journal Article", "created": "2020-05-11", "title": "Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion", "description": "

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

", "keywords": ["[SDE] Environmental Sciences", "support vector machine; vis-NIR; MIR; outer product analysis; soil classification", "2. Zero hunger", "Science", "Q", "vis-NIR", "MIR", "soil classification", "04 agricultural and veterinary sciences", "15. Life on land", "771", "630", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "outer product analysis"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://doi.org/3025754366"}, {"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": "3025754366", "name": "item", "description": "3025754366", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3025754366"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-09T00:00:00Z"}}, {"id": "10.5281/zenodo.8090575", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:42Z", "type": "Journal Article", "created": "2020-05-11", "title": "Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion", "description": "

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

", "keywords": ["[SDE] Environmental Sciences", "support vector machine; vis-NIR; MIR; outer product analysis; soil classification", "2. Zero hunger", "Science", "Q", "vis-NIR", "MIR", "soil classification", "04 agricultural and veterinary sciences", "15. Life on land", "771", "630", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "outer product analysis"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/9/1512/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090575"}, {"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.8090575", "name": "item", "description": "10.5281/zenodo.8090575", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090575"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-09T00:00:00Z"}}, {"id": "10.3390/rs12010072", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:50Z", "type": "Journal Article", "created": "2019-12-24", "title": "Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data", "description": "

The main objective of this work was to retrieve surface soil moisture (SSM) by using scattering models and a support vector machine (SVM) technique driven by backscattering coefficients obtained from Sentinel-1 satellite images acquired over bare agricultural soil in the Tensfit basin of Morocco. Two backscattering models were selected in this study due to their wide use in inversion procedures: the theoretical integral equation model (IEM) and the semi-empirical model (Oh). To this end, the sensitivity of the SAR backscattering coefficients at V V ( \uffcf\uff83 v v \uffe2\uff88\uff98 ) and V H ( \uffcf\uff83 v h \uffe2\uff88\uff98 ) polarizations to in situ soil moisture data were analyzed first. As expected, the results showed that over bare soil the \uffcf\uff83 v v \uffe2\uff88\uff98 was well correlated with SSM compared to the \uffcf\uff83 v h \uffe2\uff88\uff98 , which showed more dispersion with correlation coefficients values (r) of about 0.84 and 0.61 for the V V and V H polarizations, respectively. Afterwards, these values of \uffcf\uff83 v v \uffe2\uff88\uff98 were compared to those simulated by the backscatter models. It was found that IEM driven by the measured length correlation L slightly underestimated SAR backscatter coefficients compared to the Oh model with a bias of about \uffe2\uff88\uff92 0.7 dB and \uffe2\uff88\uff92 1.2 dB and a root mean square (RMSE) of about 1.1 dB and 1.5 dB for Oh and IEM models, respectively. However, the use of an optimal value of L significantly improved the bias of IEM, which became near to zero, and the RMSE decreased to 0.9 dB. Then, a classical inversion approach of \uffcf\uff83 v v \uffe2\uff88\uff98 observations based on backscattering model is compared to a data driven retrieval technic (SVM). By comparing the retrieved soil moisture against ground truth measurements, it was found that results of SVM were very encouraging and were close to those obtained by IEM model. The bias and RMSE were about 0.28 vol.% and 2.77 vol.% and \uffe2\uff88\uff92 0.13 vol.% and 2.71 vol.% for SVM and IEM, respectively. However, by taking into account the difficultly of obtaining roughness parameter at large scale, it was concluded that SVM is still a useful tool to retrieve soil moisture, and therefore, can be fairly used to generate maps at such scales.

", "keywords": ["[SDE] Environmental Sciences", "soil moisture; synthetic aperture radar (SAR); Sentinel-1; semi-empirical and theoretical backscatter models; support vector machine; bare soil", "550", "Science", "sentinel-1", "Q", "0211 other engineering and technologies", "0207 environmental engineering", "support vector", "02 engineering and technology", "synthetic aperture radar (SAR)", "15. Life on land", "543", "bare soil", "[SDE]Environmental Sciences", "Sentinel-1", "support vector machine", "soil moisture", "synthetic aperture radar (sar)", "semi-empirical and theoretical backscatter models", "machine"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://www.mdpi.com/2072-4292/12/1/72/pdf"}, {"href": "https://doi.org/10.3390/rs12010072"}, {"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/rs12010072", "name": "item", "description": "10.3390/rs12010072", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12010072"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-24T00:00:00Z"}}, {"id": "10.3390/rs14030541", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2022-01-24", "title": "Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data", "description": "

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data.

", "keywords": ["LUCAS 2018", "crop type classification", "crop type classification; random forest; support vector machine; LUCAS 2018", "Science", "Q", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://doi.org/10.3390/rs14030541"}, {"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/rs14030541", "name": "item", "description": "10.3390/rs14030541", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030541"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-23T00:00:00Z"}}, {"id": "10.3390/rs16091510", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:21:52Z", "type": "Journal Article", "created": "2024-04-25", "title": "Remote Quantification of Soil Organic Carbon: Role of Topography in the Intra-Field Distribution", "description": "

Soil organic carbon (SOC) measurements are an indicator of soil health and an important parameter for the study of land-atmosphere carbon fluxes. Field sampling provides precise measurements at the sample location but entails high costs and cannot provide detailed maps unless the sampling density is very high. Remote sensing offers the possibility to quantify SOC over large areas in a cost-effective way. As a result, numerous studies have sought to quantify SOC using Earth observation data with a focus on inter-field or regional distributions. This study took a different angle and aimed to map the spatial distribution of SOC at the intra-field scale, since this distribution provides important insights into the biophysiochemical processes involved in the retention of SOC. Instead of solely using spectral measurements to quantify SOC, topographic and spectral features act as predictor variables. The necessary data on study fields in South-East England was acquired through a detailed SOC sampling campaign, including a LiDAR survey flight. Multi-spectral Sentinel-2 data of the study fields were acquired for the exact day of the sampling campaign, and for an interval of 18 months before and after this date. Random Forest (RF) and Support Vector Regression (SVR) models were trained and tested on the spectral and topographical data of the fields to predict the observed SOC values. Five different sets of model predictors were assessed, by using independently and in combination, single and multidate spectral data, and topographical features for the SOC sampling points. Both, RF and SVR models performed best when trained on multi-temporal Sentinel-2 data together with topographic features, achieving validation root-mean-square errors (RMSEs) of 0.29% and 0.23% SOC, respectively. These RMSEs are competitive when compared with those found in the literature for similar models. The topographic wetness index (TWI) exhibited the highest permutation importance for virtually all models. Given that farming practices within each field are the same, this result suggests an important role of soil moisture in SOC retention. Contrary to findings in dryer climates or in studies encompassing larger areas, TWI was negatively related to SOC levels in the study fields, suggesting a different role of soil wetness in the SOC storage in climates characterized by excess rainfall and poorly drained soils.

", "keywords": ["2. Zero hunger", "Science", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "soil organic carbon", "topographic wetness index", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "Sentinel-2", "random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/16/9/1510/pdf"}, {"href": "https://doi.org/10.3390/rs16091510"}, {"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/rs16091510", "name": "item", "description": "10.3390/rs16091510", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs16091510"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8091863", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:24:42Z", "type": "Journal Article", "created": "2022-01-23", "title": "Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data", "description": "

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data.

", "keywords": ["LUCAS 2018", "crop type classification", "crop type classification; random forest; support vector machine; LUCAS 2018", "Science", "Q", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091863"}, {"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.8091863", "name": "item", "description": "10.5281/zenodo.8091863", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091863"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-23T00:00:00Z"}}, {"id": "10807/190102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:25:53Z", "type": "Journal Article", "created": "2021-09-10", "title": "Exploring the performance of a functionalized CNT-based sensor array for breathomics through clustering and classification algorithms: from gas sensing of selective biomarkers to discrimination of chronic obstructive pulmonary disease", "description": "

Extensive application of clustering and classification algorithms shows the potential of a CNT-based sensor array in breathomics.

", "keywords": ["electronic nose", "Linear discriminant analysis", "Chemistry", " Multidisciplinary", "Principal component analysis", "02 engineering and technology", "VOLATILE ORGANIC-COMPOUNDS", "sensors", "Supported Vectror Machine", "01 natural sciences", "nanotubes", "E-NOSE", "breathomics", "THIN-FILMS", "SWCNTs", "RANDOM NETWORKS", "COPD", "ta318", "e-nose", "ta215", "WALLED CARBON NANOTUBES", "Science & Technology", "Breath analysis", "SWCNT SENSOR", "34 Chemical sciences", "Ammonia; Biomarkers; Carbon nanotubes; Classification (of information); Clustering algorithms; Molecules; Nitrogen oxides; Principal component analysis; Sulfur compounds; Support vector machines", "0104 chemical sciences", "3. Good health", "Chemistry", "ROOM-TEMPERATURE", "AMMONIA SENSOR", "Physical Sciences", "NO2 DETECTION", "03 Chemical Sciences", "0210 nano-technology", "RESISTIVE SENSORS"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/536855/1/RSC%20Adv._2021.pdf"}, {"href": "https://boa.unimib.it/bitstream/10281/517427/2/d1ra03337a.pdf%3b"}, {"href": "https://publicatt.unicatt.it/bitstream/10807/190102/1/d1ra03337a.pdf"}, {"href": "http://pubs.rsc.org/en/content/articlepdf/2021/RA/D1RA03337A"}, {"href": "https://doi.org/10807/190102"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/RSC%20Advances", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10807/190102", "name": "item", "description": "10807/190102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10807/190102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:13Z", "type": "Journal Article", "created": "2022-06-14", "title": "Potential of visible and near infrared spectroscopy coupled with machine learning for predicting soil metal concentrations at the regional scale", "description": "Chemical analytical methods for metal analysis in soils are laborious, time-consuming and costly. This paper aims to evaluate the potential of short-range (SR) and full-range (FR) visible and infrared spectroscopy (vis-NIR) combined with linear and nonlinear calibration methods to estimate concentrations of nickel (Ni), cobalt (Co), cadmium (Cd), lead (Pb) and copper (Cu) in soils. A total of 435 soil samples were collected over agricultural sites, forest (7 %), pasture (5 %) and fallow land across a region in the northern part of Belgium. Generally, better predictions were obtained when using partial least squares regression (PLSR) and nonlinear calibration method [i.e., random forest (RF)] for processing of the spectral data, than when using support vector machine (SVM). FR generally outperformed SR and provided the best prediction results for Ni (R2p = 0.76), Co (R2p = 0.77), Cd (R2p = 0.64) and Pb (R2p = 0.65), when using PLSR and RF. SVM produced the best prediction result only for Pb (R2p = 0.57) using the SR spectra. The metals Ni, Co, Cd and Pb can be predicted successfully (good accuracy) from the FR vis-NIR spectra using PLSR for Co, and RF for Ni, Cd, Pb and Cu. Compared to the FR spectrophotometer, improvement in accuracy was obtained for Cd and Co, using the SR spectra when combined with PLSR and RF, respectively. It is concluded that the SR spectrometer can be used successfully for the prediction of Co with RF (R2p = 0.70), while it best predicted Cd with PLSR with an R2p value of 0.67, which is of value for regional survey.", "keywords": ["Spectroscopy", " Near-Infrared", "Support Vector Machine", "RANGE", "Machine", "Machine learning modelling", "learning modelling", "REFLECTANCE SPECTROSCOPY", "CONTAMINATION", "Soil", "Lead", "Soil contamination", "Nickel", "Metals", "Earth and Environmental Sciences", "Soil Pollutants", "Chemometrics", "Cadmium", "Near-infrared spectra"]}, "links": [{"href": "https://doi.org/1854/LU-01GM39KW0F5ENNMCF40YD35GFY"}, {"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": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "name": "item", "description": "1854/LU-01GM39KW0F5ENNMCF40YD35GFY", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01GM39KW0F5ENNMCF40YD35GFY"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-01T00:00:00Z"}}, {"id": "1854/LU-01GM39MMFY2YP4FTDY102R50HB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:26:13Z", "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": "

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.

", "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": "2620227646", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:26:52Z", "type": "Journal Article", "created": "2017-05-25", "title": "Big Data for weed control and crop protection", "description": "Summary

Farmers have access to many data\uffe2\uff80\uff90intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide\uffe2\uff80\uff90resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.