{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:10:24Z","timestamp":1767337824043,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"],"award-info":[{"award-number":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"],"award-info":[{"award-number":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"]}]},{"name":"Natural Science Foundation of Guangdong Province, China","award":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"],"award-info":[{"award-number":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"]}]},{"name":"Guangdong Province Agricultural Science and Technology Innovation and Promotion Project","award":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"],"award-info":[{"award-number":["U1901601","2020YFD1100204","2021A1515011643","2022KJ102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R2) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R2 of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields.<\/jats:p>","DOI":"10.3390\/rs14143311","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Yiping","family":"Peng","sequence":"first","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5334-2202","authenticated-orcid":false,"given":"Zhenhua","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Chenjie","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Yueming","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China"},{"name":"College of Tropical Crops, Hainan University, Haikou 570228, China"},{"name":"South China Academy of Natural Resources Science and Technology, Guangzhou 510642, China"}]},{"given":"Li","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"South China Academy of Natural Resources Science and Technology, Guangzhou 510642, China"}]},{"given":"Runyan","family":"Zou","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"},{"name":"South China Academy of Natural Resources Science and Technology, Guangzhou 510642, China"}]},{"given":"Ya","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Xiaoyun","family":"Mao","sequence":"additional","affiliation":[{"name":"College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1475-2743.2002.tb00272.x","article-title":"Soil fertility in organic farming systems\u2014Fundamentally different?","volume":"18","author":"Stockdale","year":"2006","journal-title":"Soil Use Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1002\/agj2.20338","article-title":"Long-term fertilization effects on crop yield and desalinized soil properties","volume":"112","author":"Li","year":"2020","journal-title":"Agron. J."},{"key":"ref_3","first-page":"710","article-title":"Application of Rough Set Theory to Determine Weights of Soil Fertility Factor","volume":"47","author":"Ye","year":"2014","journal-title":"Sci. Agric. Sin."},{"key":"ref_4","first-page":"1855","article-title":"Characteristics of soil fertility quality and minimum dataset for yellow-mud paddy fields in Fujian Province","volume":"26","author":"Wang","year":"2018","journal-title":"Chin. J. Eco-Agric."},{"key":"ref_5","unstructured":"Huang, J., Han, T.F., Shen, Z., Liu, K.L., Ma, C.B., Wang, H.Y., Qu, X.L., Yu, Z.K., Xie, J.H., and Zhang, H.M. (2022). Spatiotemporal Variation of Fertility Quality of Chinese Paddy Soil Based on Fuzzy Method in Recent 30 Years. Acta Pedol. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.2136\/sssaj2009.0130","article-title":"Spatial modeling of a soil fertility index using Visible\u2013Near-Infrared spectra and terrain attributes","volume":"74","author":"Rossel","year":"2010","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106341","DOI":"10.1016\/j.compag.2021.106341","article-title":"Development of a soil fertility index using on-line Vis-NIR spectroscopy","volume":"188","author":"Munnaf","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1111\/ejss.12907","article-title":"Assessment of a soil fertility index using visible and near-infrared spectroscopy in the rice paddy region of southern China","volume":"71","author":"Yang","year":"2020","journal-title":"Eur. J. Soil Sci."},{"key":"ref_9","first-page":"240","article-title":"Study on Farmland Soil Fertility Model Based on Multi-Angle Polarized Hyper-Spectrum","volume":"38","author":"Wang","year":"2018","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_10","first-page":"1000182","article-title":"Correlation of Spatial Variability of Soil Macronutrients with Crop Performance by Using Satellite and Remote Sensing Indices for Site Specific Agriculture: Chakwal Region","volume":"5","author":"Zeeshan","year":"2017","journal-title":"Rice Res."},{"key":"ref_11","first-page":"148","article-title":"Study on Evaluation Methods for Soil Fertility in Oasis Cotton Field Based on the Nor-malized Difference Vegetation Index (NDVI)","volume":"25","author":"Wang","year":"2013","journal-title":"Cotton Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duan, D.D., Sun, X., Liang, S.F., Sun, J., Fan, L.L., Chen, H., Xia, L., Zhao, F., Yang, W.Q., and Yang, P. (2022). Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China. Remote Sens., 14.","DOI":"10.3390\/rs14051250"},{"key":"ref_13","unstructured":"Lu, R.K. (2000). Methods of Soil Agrochemical Analysis, China Agricultural Science and Technology Press."},{"key":"ref_14","first-page":"779","article-title":"Research on the inversion model of cultivated land quality based on normalized difference vegetation index","volume":"49","author":"Guan","year":"2018","journal-title":"Chin. J. Soil Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1080\/0143116042000274015","article-title":"MTCI: The meris terrestrial chlorophyll index","volume":"25","author":"Dash","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"640","DOI":"10.2134\/agronj1968.00021962006000060016x","article-title":"Measuring the color of growing turf with a reflectance spectrophotometer","volume":"60","author":"Birth","year":"1968","journal-title":"Agron. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/1011-1344(93)06963-4","article-title":"Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves","volume":"22","author":"Gitelson","year":"1994","journal-title":"J. Photochem. Photobiol. B Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"663","DOI":"10.2307\/1936256","article-title":"Derivation of leaf-area index from quality of light on the forest floor","volume":"50","author":"Jordan","year":"1969","journal-title":"Ecology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e1602244","DOI":"10.1126\/sciadv.1602244","article-title":"Canopy near-infrared reflectance and terrestrial photosynthesis","volume":"3","author":"Badgley","year":"2017","journal-title":"Sci. Adv."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Pasqualotto, N., Delegido, J., Wittenberghe, S.V., Rinaldi, M., and Moreno, J. (2019). Multi-crop green LAI estimation with a new simple Sentinel-2 LAI index (SeLI). Sensors, 19.","DOI":"10.3390\/s19040904"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Anatoly","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TGRS.2007.904836","article-title":"Remote estimation of crop chlorophyll content using spectral indices derived from Hyperspectral data","volume":"46","author":"Haboudane","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s11119-014-9368-3","article-title":"Optimizing soybean harvest date using HJ-1 satellite imagery","volume":"16","author":"Meng","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_33","first-page":"103","article-title":"A visible band index for remote sensing leaf chlorophyll content at the canopy scale","volume":"21","author":"Hunt","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/S0034-4257(00)00197-8","article-title":"Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density","volume":"76","author":"Broge","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_35","first-page":"1","article-title":"The key techniques and future vision of feature selection in machine learning","volume":"41","author":"Cui","year":"2018","journal-title":"J. Beijing Univ. Posts Telecommun."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"118955","DOI":"10.1016\/j.jclepro.2019.118955","article-title":"Identification of high impact factors of air quality on a national scale using big data and machine learning techniques","volume":"244","author":"Ma","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108378","DOI":"10.1016\/j.ecolind.2021.108378","article-title":"A new AG-AGB estimation model based on MODIS and SRTM data in Qinghai Province, China","volume":"133","author":"Zhao","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.catena.2016.01.001","article-title":"Spatial prediction of soil surface texture in a semiarid region using random forest and multiple linear regressions","volume":"139","author":"Chagas","year":"2016","journal-title":"Catena"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.geoderma.2006.03.050","article-title":"High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures","volume":"136","author":"Selige","year":"2006","journal-title":"Geoderma"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"105250","DOI":"10.1016\/j.still.2021.105250","article-title":"Laser-Induced Breakdown Spectroscopy (LIBS) for tropical soil fertility analysis","volume":"216","author":"Tavares","year":"2022","journal-title":"Soil Tillage Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Peng, Y.P., Zhao, L., Hu, Y.M., Wang, G.X., Wang, L., and Liu, Z.H. (2019). Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8100437"},{"key":"ref_43","unstructured":"Nielsen, R.H. (1987, January 21\u201324). Kolmogorov\u2019s mapping neural network existence theorem. Proceedings of the IEEE 1st International Conference on Neural Networks, San Diego, CA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"111793","DOI":"10.1016\/j.rse.2020.111793","article-title":"An integrated methodology using open soil spectral libraries and Earth Observation data for soil organic carbon estimations in support of soil-related SDGs","volume":"244","author":"Tziolas","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"115159","DOI":"10.1016\/j.geoderma.2021.115159","article-title":"Evaluating validation strategies on the performance of soil property prediction from regional to continental spectral data","volume":"400","author":"Chen","year":"2021","journal-title":"Geoderma"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/JSTARS.2012.2211863","article-title":"Stem Volume and Above-Ground Biomass Estimation of Individual Pine Trees from LiDAR Data: Contribution of Full-Waveform Signals","volume":"6","author":"Allouis","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3894","DOI":"10.1002\/app.39656","article-title":"ANN modeling in Pb(II) removal from water by clay-polymer composites fabricated via the melt-blending","volume":"130","author":"Dlamini","year":"2013","journal-title":"J. Appl. Polym. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geoderma.2010.03.001","article-title":"Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy","volume":"158","author":"Mouazen","year":"2010","journal-title":"Geoderma"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"425740","DOI":"10.1155\/2013\/425740","article-title":"Review on methods to fix number of hidden neurons in neural networks","volume":"2013","author":"Sheela","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_51","first-page":"71","article-title":"Cultivated Land Quality Assessment Based on SPOT Multispectral Remote Sensing Image: A Case Study in Jimo City of Shandong Province","volume":"27","author":"Fang","year":"2008","journal-title":"Prog. Geogr."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, S.S., Peng, Y.P., Xia, Z.Q., Hu, Y.M., Wang, G.X., Zhu, A.X., and Liu, Z.H. (2019). The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data. Sensors, 19.","DOI":"10.3390\/s19235127"},{"key":"ref_53","first-page":"81","article-title":"Method of comprehensive evaluation on soil fertility on the basis of weight analysis","volume":"35","author":"Zhou","year":"2016","journal-title":"J. Irrig. Drain."},{"key":"ref_54","first-page":"1168","article-title":"Crop recognition and evaluation using red edge features of GF-6 satellite","volume":"24","author":"Liang","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Weksler, S., Rozenstein, O., Haish, N., Moshelion, M., Wallach, R., and Ben-Dor, E. (2021). Pepper Plants Leaf Spectral Reflectance Changes as a Result of Root Rot Damage. Remote Sens., 13.","DOI":"10.3390\/rs13050980"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3311\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:47:22Z","timestamp":1760140042000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3311"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,9]]},"references-count":55,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143311"],"URL":"https:\/\/doi.org\/10.3390\/rs14143311","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,7,9]]}}}