{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:55:16Z","timestamp":1776444916690,"version":"3.51.2"},"reference-count":73,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31971466"],"award-info":[{"award-number":["31971466"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32001214"],"award-info":[{"award-number":["32001214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The North China agro\u2013pastoral zone is a large, ecologically fragile zone in the arid and semi-arid regions. Quantitative remote sensing inversion of soil organic carbon (SOC) in this region can facilitate understanding of the current status of degraded land restoration and provide data support for carbon cycling research in the region. Deep learning (DNN) for SOC inversion has been W.a hot topic over the past decade, but there have been few studies at the regional scale in the arid and semi-arid zones. In this study, a DNN model with five hidden layers and five skip connections was established using 644 spatially distributed SOC samples and Landsat 8 OLI imagery. The model was compared with the random forest algorithm in terms of generalization ability. The main conclusions were as follows: 1. The DNN algorithm can establish a high-precision SOC inversion model (R2 = 0.52, RMSE = 0.7), with 90% of errors concentrated in the range of \u22122.5 to 2.5 kg\u00b7C\/m2; 2. the Boruta variable-screening algorithm can effectively improve the model accuracy of the random forest algorithm, but due to the DNN\u2019s better ability to mine hidden information in the data, the improvement effect on the DNN model accuracy is limited; 3. the SOC samples in arid and semi-arid areas are highly positively skewed, with a significant impact on the modeling accuracy of DNN, and conversion is required to obtain a model with better generalization ability; and 4. in arid and semi-arid regions, SOC has a weak correlation with vegetation indices but a stronger correlation with temperature, elevation, and aridity. This study established a reliable deep learning model for SOC density in a large arid and semi-arid region, providing a reference and framework for the establishment of SOC inversion models in other regions.<\/jats:p>","DOI":"10.3390\/rs15153846","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T10:57:33Z","timestamp":1690973853000},"page":"3846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Remote Sensing of Soil Organic Carbon at Regional Scale Based on Deep Learning: A Case Study of Agro-Pastoral Ecotone in Northern China"],"prefix":"10.3390","volume":"15","author":[{"given":"Zichen","family":"Guo","sequence":"first","affiliation":[{"name":"Northwest Institute of Eco\u2013Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China"}]},{"given":"Yuqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco\u2013Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Strategic Mineral Resources of the Upper Yellow River, Ministry of Natural Resources, Lanzhou 730000, China"}]},{"given":"Xuyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco\u2013Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China"}]},{"given":"Xiangwen","family":"Gong","sequence":"additional","affiliation":[{"name":"Wansheng Mining of Chongqing Conservation and Repair of Ecological Environment Observation and Research Station, Chongqing Institute of Geology and Mineral Resources, Chongqing 400042, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1712-4555","authenticated-orcid":false,"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco\u2013Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China"}]},{"given":"Wenjie","family":"Cao","sequence":"additional","affiliation":[{"name":"Northwest Institute of Eco\u2013Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China"},{"name":"Naiman Desertification Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Tongliao 028300, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"ref_1","first-page":"310","article-title":"The global carbon cycle","volume":"78","author":"Post","year":"1990","journal-title":"Am. 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