{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T22:07:12Z","timestamp":1785967632090,"version":"3.56.0"},"reference-count":90,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,7]],"date-time":"2019-12-07T00:00:00Z","timestamp":1575676800000},"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 total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use\/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.<\/jats:p>","DOI":"10.3390\/rs11242934","type":"journal-article","created":{"date-parts":[[2019,12,9]],"date-time":"2019-12-09T05:54:51Z","timestamp":1575870891000},"page":"2934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1216-0709","authenticated-orcid":false,"given":"Tao","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany"},{"name":"Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research\u2013UFZ, Permoserstra\u00dfe 15, 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajun","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang 1, Nanjing 210095, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang 1, Nanjing 210095, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-9153","authenticated-orcid":false,"given":"Chuanliang","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang 1, Nanjing 210095, China"},{"name":"Jiangsu Academy of Agricultural Sciences, Zhongling Street 50, Nanjing 210014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dagmar","family":"Haase","sequence":"additional","affiliation":[{"name":"Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany"},{"name":"Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research\u2013UFZ, Permoserstra\u00dfe 15, 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4490-7232","authenticated-orcid":false,"given":"Angela","family":"Lausch","sequence":"additional","affiliation":[{"name":"Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany"},{"name":"Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research\u2013UFZ, Permoserstra\u00dfe 15, 04318 Leipzig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geoderma.2018.12.040","article-title":"Mapping soil total nitrogen in an estuarine area with high landscape fragmentation using a multiple-scale approach","volume":"339","author":"Chi","year":"2019","journal-title":"Geoderma"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.chemer.2016.04.002","article-title":"Greenhouse gas emissions from soils\u2014A review","volume":"76","author":"Oertel","year":"2016","journal-title":"Geochemistry"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/S0269-7491(98)80026-2","article-title":"Global air emission inventories for anthropogenic sources of NOx, NH3 and N2O in 1990","volume":"102","author":"Olivier","year":"1998","journal-title":"Environ. 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