{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T19:14:12Z","timestamp":1790709252689,"version":"4.1.0"},"reference-count":95,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.<\/jats:p>","DOI":"10.3390\/rs13071229","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T21:36:51Z","timestamp":1616621811000},"page":"1229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1\/-2 Data in a Small Watershed"],"prefix":"10.3390","volume":"13","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3892-1636","authenticated-orcid":false,"given":"Hongbin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Southwest University, Chongqing 400716, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1046\/j.1365-2486.2002.00511.x","article-title":"Interannual variability in global soil respiration, 1980\u20131994","volume":"8","author":"Raich","year":"2002","journal-title":"Glob. Chang. Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1038\/nature13604","article-title":"Temperature sensitivity of soil respiration rates enhanced by microbial community response","volume":"513","author":"Karhu","year":"2014","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hartemink, A.E., and McSweeney, K. (2014). Challenges for Soil Organic Carbon Research. Soil Carbon, Springer.","DOI":"10.1007\/978-3-319-04084-4"},{"key":"ref_4","first-page":"341","article-title":"Soil organic carbon and microbial community structure exhibit different responses to three land use types in the North China Plain","volume":"65","author":"Zhang","year":"2015","journal-title":"Agric. Scand. Sect. B Soil Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10584-004-2847-1","article-title":"Effects of land use change on the storage of soil organic carbon: A case study of the Qianyanzhou Forest Experimental Station in China","volume":"67","author":"Wang","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.geoderma.2016.04.012","article-title":"UAS-based soil carbon mapping using VIS-NIR (480\u20131000 nm) multi-spectral imaging: Potential and limitations","volume":"275","author":"Heckrath","year":"2016","journal-title":"Geoderma"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1071\/SR13077","article-title":"Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra","volume":"51","author":"Baldock","year":"2013","journal-title":"Soil Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.gexplo.2013.04.003","article-title":"Prediction of soil properties using laboratory VIS\u2013NIR spectroscopy and Hyperion imagery","volume":"132","author":"Lu","year":"2013","journal-title":"J. Geochem. Explor."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.saa.2018.06.018","article-title":"Prediction of soil organic carbon with different parent materials development using visible-near infrared spectroscopy","volume":"204","author":"Liu","year":"2018","journal-title":"Spectroc. Acta Pt. A Molec. Biomolec. Spectr."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Stevens, A., Nocita, M., Toth, G., Montanarella, L., and van Wesemael, B. (2013). Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0066409"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, L.F., Ji, M., Dong, Y.Y., Zhang, R.C., and Buchroithner, M. (2016). Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared (Vis-NIR-SWIR) Spectroscopy Using Fractal-Based Feature Extraction. Remote Sens., 8.","DOI":"10.3390\/rs8121035"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1111\/ejss.12875","article-title":"Data fusion of vis-NIR and PXRF spectra to predict soil physical and chemical properties","volume":"71","author":"Zhang","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.geoderma.2005.03.007","article-title":"Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties","volume":"131","author":"Rossel","year":"2006","journal-title":"Geoderma"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Peng, Y., Xiong, X., Adhikari, K., Knadel, M., Grunwald, S., and Greve, M.H. (2015). Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142295"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Castaldi, A., Chabrillat, S., Don, A., and van Wesemael, B. (2019). Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects. Remote Sens., 11.","DOI":"10.3390\/rs11182121"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2019.01.006","article-title":"Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems","volume":"223","author":"Vaudour","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Schmidt, K., Amirian-Chakan, A., Rentschler, T., Zeraatpisheh, M., Sarmadian, F., Valavi, R., Davatgar, N., Behrens, T., and Scholten, T. (2020). Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space. Remote Sens., 12.","DOI":"10.3390\/rs12071095"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.isprsjprs.2018.11.026","article-title":"Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands","volume":"147","author":"Castaldi","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2018.09.015","article-title":"Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging","volume":"218","author":"Gholizadeh","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"142661","DOI":"10.1016\/j.scitotenv.2020.142661","article-title":"Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images","volume":"755","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_21","first-page":"102277","article-title":"Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands","volume":"96","author":"Vaudour","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"186","DOI":"10.2136\/sssaj2017.04.0122","article-title":"Soil Property and Class Maps of the Conterminous United States at 100-Meter Spatial Resolution","volume":"82","author":"Ramcharan","year":"2018","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e00260","DOI":"10.1016\/j.geodrs.2020.e00260","article-title":"Spatial prediction of soil organic carbon using machine learning techniques in western Iran","volume":"21","author":"Mahmoudzadeh","year":"2020","journal-title":"Geoderma Reg."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Emadi, M., Taghizadeh-Mehrjardi, R., Cherati, A., Danesh, M., Mosavi, A., and Scholten, T. (2020). Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran. Remote Sens., 12.","DOI":"10.3390\/rs12142234"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.geoderma.2018.09.006","article-title":"Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran","volume":"338","author":"Zeraatpisheh","year":"2019","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Safanelli, J.L., Chabrillat, S., Ben-Dor, E., and Dematte, J.A.M. (2020). Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe. Remote Sens., 12.","DOI":"10.3390\/rs12091369"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104392","DOI":"10.1016\/j.cageo.2019.104392","article-title":"Mapping soil organic carbon and total nitrogen in croplands of the Corn Belt of Northeast China based on geographically weighted regression kriging model","volume":"135","author":"Li","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1002\/saj2.20056","article-title":"Estimating soil organic carbon and pH in Jilin Province using Landsat and ancillary data","volume":"84","author":"Zhang","year":"2020","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10705-017-9870-x","article-title":"Soil nutrient maps of Sub-Saharan Africa: Assessment of soil nutrient content at 250 m spatial resolution using machine learning","volume":"109","author":"Hengl","year":"2017","journal-title":"Nutr. Cycl. Agroecosyst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hengl, T., Mendes de Jesus, J., Heuvelink, G.B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotic, A., Shangguan, W., Wright, M.N., Geng, X., and Bauer-Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169748"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.scitotenv.2019.05.332","article-title":"High-resolution three-dimensional mapping of soil organic carbon in China: Effects of SoilGrids products on national modeling","volume":"685","author":"Liang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cai, H.K., Feng, X., Chen, Q.L., Sun, Y., Wu, Z.M., and Tie, X. (2017). Spatial and Temporal Features of the Frequency of Cloud Occurrence over China Based on CALIOP. Adv. Meteorol., 2017.","DOI":"10.1155\/2017\/4548357"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"138244","DOI":"10.1016\/j.scitotenv.2020.138244","article-title":"High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms","volume":"729","author":"Zhou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1016\/j.scitotenv.2016.11.078","article-title":"Assimilation of optical and radar remote sensing data in 3D mapping of soil properties over large areas","volume":"579","author":"Poggio","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_35","first-page":"102182","article-title":"Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images","volume":"92","author":"Wang","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106288","DOI":"10.1016\/j.ecolind.2020.106288","article-title":"Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China","volume":"114","author":"Zhou","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_37","first-page":"101906","article-title":"Modelling of soil organic carbon and bulk density in invaded coastal wetlands using Sentinel-1 imagery","volume":"82","author":"Yang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s10661-019-7580-3","article-title":"Using time-series Sentinel-1 data for soil prediction on invaded coastal wetlands","volume":"191","author":"Yang","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.geodrs.2017.06.002","article-title":"Mapping key soil properties to support agricultural production in Eastern China","volume":"10","author":"Ma","year":"2017","journal-title":"Geoderma Reg."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11769-010-0139-4","article-title":"Changes of Soil Labile Organic Carbon in Different Land Uses in Sanjiang Plain, Heilongjiang Province","volume":"20","author":"Zhang","year":"2010","journal-title":"Chin. Geogr. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.catena.2011.07.012","article-title":"Soil organic carbon distribution in relation to land use and its storage in a small watershed of the Loess Plateau, China","volume":"88","author":"Fang","year":"2012","journal-title":"Catena"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.landusepol.2008.06.001","article-title":"Effect of land-use change on soil fertility characteristics within water-stable aggregates of two cultivated soils in northern Iran","volume":"26","author":"Emadi","year":"2009","journal-title":"Land Use Pol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1016\/j.scitotenv.2017.02.146","article-title":"Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: A comparison of multiple linear regressions and the random forest model","volume":"592","author":"Zhang","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"114177","DOI":"10.1016\/j.geoderma.2020.114177","article-title":"Mapping soil organic carbon and clay using remote sensing to predict soil workability for enhanced climate change adaptation","volume":"363","author":"Paul","year":"2020","journal-title":"Geoderma"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Biney, J.K.M., Saberioon, M., Boruvka, L., Houska, J., Vasat, R., Agyeman, P.C., Coblinski, J.A., and Klement, A. (2021). Exploring the Suitability of UAS-Based Multispectral Images for Estimating Soil Organic Carbon: Comparison with Proximal Soil Sensing and Spaceborne Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13020308"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.still.2009.01.001","article-title":"The effect of plant hedgerows on the spatial distribution of soil erosion and soil fertility on sloping farmland in the purple-soil area of China","volume":"105","author":"Lin","year":"2009","journal-title":"Soil Tillage Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.still.2008.08.010","article-title":"Factors determining soil nutrient distribution in a small-scaled watershed in the purple soil region of Sichuan Province, China","volume":"105","author":"Wang","year":"2009","journal-title":"Soil Tillage Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.gexplo.2013.06.002","article-title":"National multi-purpose regional geochemical survey in China","volume":"139","author":"Li","year":"2014","journal-title":"J. Geochem. Explor."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.apgeog.2013.07.010","article-title":"Ecosystem health assessment based on analysis of a land use database","volume":"44","author":"Yu","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhou, J., Li, E.M., Wang, M.Z., Chen, X., Shi, X.Z., and Jiang, L.S. (2019). Feasibility of Stochastic Gradient Boosting Approach for Evaluating Seismic Liquefaction Potential Based on SPT and CPT Case Histories. J. Perform. Constr. Facil., 33.","DOI":"10.1061\/(ASCE)CF.1943-5509.0001292"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"124293","DOI":"10.1016\/j.jhydrol.2019.124293","article-title":"Comparison of support vector regression and extreme gradient boosting for decomposition-based data-driven 10-day streamflow forecasting","volume":"582","author":"Yu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zheng, H.T., Yuan, J.B., and Chen, L. (2017). Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation. Energies, 10.","DOI":"10.3390\/en10081168"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jia, Y., Jin, S.G., Savi, P., Gao, Y., Tang, J., Chen, Y.X., and Li, W.M. (2019). GNSS-R Soil Moisture Retrieval Based on a XGboost Machine Learning Aided Method: Performance and Validation. Remote Sens., 11.","DOI":"10.3390\/rs11141655"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wei, L., Yuan, Z., Wang, Z., Zhao, L., Zhang, Y., Lu, X., and Cao, L. (2020). Hyperspectral Inversion of Soil Organic Matter Content Based on a Combined Spectral Index Model. Sensors, 20.","DOI":"10.3390\/s20102777"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.enconman.2013.03.004","article-title":"General models for estimating daily global solar radiation for different solar radiation zones in mainland China","volume":"70","author":"Li","year":"2013","journal-title":"Energy Convers. Manag."},{"key":"ref_57","first-page":"13","article-title":"Impacts of Various Agricultural Land Use Patterns on the Content of Organic Carbon in Soil in Jiangxi Province","volume":"24","author":"Hua","year":"2010","journal-title":"China Land Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.ecoleng.2016.06.101","article-title":"Variation of soil organic carbon and land use in a dry valley in Sichuan province, Southwestern China","volume":"95","author":"Liu","year":"2016","journal-title":"Ecol. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1111\/j.1475-2743.2002.tb00255.x","article-title":"Effects of reforestation and deforestation on soil properties in humid mountainous areas: A case study in Wolong Nature Reserve, Sichuan province, China","volume":"18","author":"Liu","year":"2002","journal-title":"Soil Use Manag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.geoderma.2019.06.005","article-title":"Factor contribution to soil organic and inorganic carbon accumulation in the Loess Plateau: Structural equation modeling","volume":"352","author":"Zhao","year":"2019","journal-title":"Geoderma"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1111\/j.1747-0765.2010.00457.x","article-title":"Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China","volume":"56","author":"Zhang","year":"2010","journal-title":"Soil Sci. Plant Nutr."},{"key":"ref_62","unstructured":"Sparks, D.L. (2010). Chapter Five\u2014Visible and Near Infrared Spectroscopy in Soil Science. Advances in Agronomy, Academic Press."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.catena.2012.11.012","article-title":"Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach","volume":"104","author":"Li","year":"2013","journal-title":"Catena"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1139\/cjss-2017-0128","article-title":"Importance of terrain attributes in relation to the spatial distribution of soil properties at the micro scale: A case study","volume":"98","author":"Kokulan","year":"2018","journal-title":"Can. J. Soil Sci."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ceddia, M.B., Gomes, A.S., Vasques, G.M., and Pinheiro, E.F.M. (2017). Soil Carbon Stock and Particle Size Fractions in the Central Amazon Predicted from Remotely Sensed Relief, Multispectral and Radar Data. Remote Sens., 9.","DOI":"10.3390\/rs9020124"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1007\/s10661-017-6197-7","article-title":"Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran","volume":"189","author":"Mahmoudabadi","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0034-4257(96)00148-4","article-title":"The use of imaging radars for ecological applications\u2014A review","volume":"59","author":"Kasischke","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.rse.2017.07.015","article-title":"Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications","volume":"199","author":"Veloso","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.1109\/TGRS.2003.814132","article-title":"High-resolution measurements of scattering in wheat canopies\u2014Implications for crop parameter retrieval","volume":"41","author":"Brown","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s10661-017-6249-z","article-title":"Soil color indicates carbon and wetlands: Developing a color-proxy for soil organic carbon and wetland boundaries on sandy coastal plains in South Africa","volume":"189","author":"Pretorius","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s12524-017-0738-y","article-title":"Geospatial Mapping of Soil Organic Carbon Using Regression Kriging and Remote Sensing","volume":"46","author":"Kumar","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1080\/00103624.2016.1194990","article-title":"Near-Infrared-Spectroscopy for Determination of Carbon and Nitrogen in Indian Soils","volume":"47","author":"Dinakaran","year":"2016","journal-title":"Commun. Soil Sci. Plant Anal."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N., and Segui, P.Q. (2018). Irrigation Mapping Using Sentinel-1 Time Series at Field Scale. Remote Sens., 10.","DOI":"10.3390\/rs10091495"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0034-4257(89)90035-7","article-title":"Munsell Soil Color and Soil Reflectance in the Visible Spectral Bands of Landsat Mss and Tm Data","volume":"27","author":"Escadafal","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_75","unstructured":"Pouget, M., Madeira, J., Le Floch, E., and Kamal, S. (1990, January 4\u20136). Caracteristiques Spectrales des Surfaces Sableuses de La Region Cotiere Nord-Ouest de L\u2019Egypte: Application Aux Donnees Satellitaires SPOT. Proceedings of the 2eme JoumCes de T&detection: Caracterisation et Suivi des Milieux Terrestres En Regions Arides et Tropicales, Paris, France."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A Soil-Adjusted Vegetation Index (Savi)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/0034-4257(91)90009-U","article-title":"Potentials and Limits of Vegetation Indexes for Lai and Apar Assessment","volume":"35","author":"Baret","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A Modified Soil Adjusted Vegetation Index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_79","unstructured":"Qi, J., Kerr, Y., and Chehbouni, A. (1994, January 17\u201322). External Factor Consideration in Vegetation Index Development. Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, Val D\u2019Isere, France."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/S0034-4257(95)00098-4","article-title":"A new approach for remote sensing of canopy absorbed photosynthetically active radiation. 2. Proportion of canopy absorption","volume":"55","author":"Moreau","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/01431169308904321","article-title":"Comparative-Study of Avhrr Ratio Vegetation Index and Normalized Difference Vegetation Index in District Level Agricultural Monitoring","volume":"14","author":"Gupta","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","first-page":"1541","article-title":"Distinguishing Vegetation from Soil Background Information","volume":"43","author":"Richardson","year":"1977","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/0034-4257(90)90085-Z","article-title":"Calculating the Vegetation Index Faster","volume":"34","author":"Crippen","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0034-4257(88)90041-7","article-title":"The Derivation of a Simplified Reflectance Model for the Estimation of Leaf-Area Index","volume":"25","author":"Clevers","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0001-706X(02)00011-6","article-title":"Use of landsat TM satellite surveillance data to measure the impact of the 1998 flood on snail intermediate host dispersal in the lower Yangtze River Basin","volume":"82","author":"Zhou","year":"2002","journal-title":"Acta Trop."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0034-4257(96)00072-7","article-title":"Use of a green channel in remote sensing of global vegetation from EOS-MODIS","volume":"58","author":"Gitelson","year":"1996","journal-title":"Remote. Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2549","DOI":"10.1080\/01431169608949092","article-title":"Critical assessment of vegetation indices from AVHRR in a semi-arid environment","volume":"17","author":"Leprieur","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically Resistant Vegetation Index (Arvi) for Eos-Modis","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Valerio, F., Ferreira, E., Godinho, S., Pita, R., Mira, A., Fernandes, N., and Santos, S.M. (2020). Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data. Remote Sens., 12.","DOI":"10.3390\/rs12030562"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5403","DOI":"10.1080\/0143116042000274015","article-title":"The MERIS terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0034-4257(00)00113-9","article-title":"Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance","volume":"74","author":"Daughtry","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1080\/01431169308953986","article-title":"Red Edge Spectral Measurements from Sugar Maple Leaves","volume":"14","author":"Vogelmann","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.isprsjprs.2013.04.007","article-title":"Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation","volume":"82","author":"Frampton","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0034-4257(98)00059-5","article-title":"Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches","volume":"66","author":"Blackburn","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:12Z","timestamp":1760161212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":95,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071229"],"URL":"https:\/\/doi.org\/10.3390\/rs13071229","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}