文献类型： 外文期刊

作者： Zhou, Tao ¹ ; Geng, Yajun ³ ; Ji, Cheng ⁴ ; Xu, Xiangrui ³ ; Wang, Hong ⁵ ; Pan, Jianjun ³ ; Bumberger, Jan ⁶ ; Haase, Da ¹ ;

作者机构： 1.Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany

2.UFZ Helmholtz Ctr Environm Res, Dept Computat Landscape Ecol, Permoserstr 15, D-04318 Leipzig, Germany

3.Nanjing Agr Univ, Coll Resources & Environm Sci, Weigang 1, Nanjing 210095, Peoples R China

4.Jiangsu Acad Agr Sci, Inst Agr Resource & Environm Sci, Zhongling St 50, Nanjing 210014, Peoples R China

5.Anhui Sci & Technol Univ, Coll Resource & Environm, Donghua Rd 9, Chuzhou 233100, Peoples R China

6.UFZ Helmholtz Ctr Environm Res, Dept Monitoring & Explorat Technol, Permoserstr 15, D-04318 Leipzig, Germany

关键词： Soil organic carbon; C:N ratio; Sentinel; Landsat; Machine learning; Digital soil mapping

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：7.963； 五年影响因子：7.842 ）

ISSN： 0048-9697

年卷期： 2021 年 755 卷

页码：

收录情况： SCI

摘要： Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery. (C) 2020 Elsevier B.V. All rights reserved.