文献类型：期刊文献

英文题名：High-resolution annual desertification mapping in northern China using a novel comprehensive desertification index and unsupervised algorithm

作者：Dou, Yaqing[1,2] Zhang, Huaiqing[3] Sun, Hua[1,2] Lin, Hui[1,2] Liu, Yang[3] Zhang, Meng[1,2]

第一作者：Dou, Yaqing

通信作者：Zhang, M[1]

机构：[1]Cent South Univ Forestry & Technol, Hunan Prov Key Lab Forestry Remote Sensing Based B, Changsha 410004, Peoples R China;[2]Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2026

卷号：334

外文期刊名：REMOTE SENSING OF ENVIRONMENT

收录：;EI(收录号：20260419949673);Scopus(收录号：2-s2.0-105028015498);WOS:【SCI-EXPANDED(收录号:WOS:001665587400001)】；

基金：This study was funded by National Key Research and Development Program of China (Grant No. 2023YFF1303701) , the Furong Plan for Science and Technology Innovation Project (2025RC3184) and the National Natural Science Foundation of China (41901385) .

语种：英文

外文关键词：Desertification; Sentinel data; Comprehensive desertification index (CDI); Gaussian mixture model (GMM); Northern China

摘要：Desertification is a global ecological and environmental problem, dynamic monitoring and accurate assessment of desertification are essential for restoring regional ecology and achieving sustainable development. Current desertification monitoring methods face dual challenges including unclear remote sensing mechanisms, nonrobust extraction methods and the absence of high-resolution large-scale desertification products. This study constructs a comprehensive desertification index (CDI) by integrating multisource remote sensing data (Sentinel1/2), incorporating three features -phenomenal indices (vegetation cover), cause indices (soil moisture) and essence indices (soil roughness)-on the basis of the multidimensional driving mechanisms of desertification physical processes. The Gaussian mixture model (GMM) was then applied to the CDI to automate desertification mapping for yielding the first 10 m-resolution annual desertification dataset in northern China (NCDMD, 2016-2024). The results demonstrated that the CDI-GMM based method achieves superior performance in mapping desertification scope across northern China in 2019, with an overall accuracy of 93.5 % and an overall accuracy of 86.4 % for desertification degree according to field survey data. In comparison, traditional approaches showed significantly lower accuracy, with the pixel dichotomy model (FVC-based) achieving 82.2 % in scope extraction and 50.3 % in degree classification, while the DDI feature space method reached 86.1 % and 64.2 %, respectively. Comparative experiments with five unsupervised classification methods (GMM, K-Means, MiniBatch K-Means, Jenks natural breaks, and Weka LVQ algorithms) indicated that the CDI combined with the GMM clustering algorithm can optimize the extraction of desertification and maintain stable performance, with an overall classification accuracy of over 93 %. Moreover, the NCDMD achieved consistent desertification mapping accuracies above 83 % throughout the 2016-2024 period, further demonstrating the robust spatiotemporal reliability of the proposed product. In summary, as a nationally significant high-resolution base dataset, the NCDMD not only fills the gap in high-precision desertification monitoring in China but also provides scientific support for ecological restoration assessment and land management policy-making.