文献类型： 外文期刊

作者： Zhou, Tao ¹ ; Geng, Yajun ¹ ; Lv, Wenhao ¹ ; Xiao, Shancai ⁴ ; Zhang, Peiyu ⁵ ; Xu, Xiangrui ⁶ ; Chen, Jie ⁷ ; Wu, Zhen ⁸ ; Pan, Jianjun ⁸ ; Si, Bingcheng ¹ ; Lausch, Angela ² ;

作者机构： 1.Ludong Univ, Sch Resources & Environm Engn, Middle Hongqi Rd 186, Yantai 264025, Peoples R China

2.Humboldt Univ, Dept Geog, Unter Linden 6, D-10099 Berlin, Germany

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

4.Peking Univ, Coll Urban & Environm Sci, Yiheyuan Rd 5, Beijing 100871, Peoples R China

5.Hunan Normal Univ, Coll Geog Sci, Lushan Rd 36, Changsha 410081, Peoples R China

6.Zhejiang Univ City Coll, Sch Spatial Planning & Design, Huzhou St 51, Hangzhou 31000, Peoples R China

7.Hunan Acad Agr Sci, Yuanda 2nd Rd 560, Changsha 410125, Peoples R China

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

9.Univ Saskatchewan, Dept Soil Sci, Saskatoon, SK S7N 5AS, Canada

关键词： Google earth engine; Multisensor; Sentinel; Soil organic carbon; Digital soil mapping; Synthetic aperture radar

期刊名称：JOURNAL OF ENVIRONMENTAL MANAGEMENT （ 影响因子：8.7； 五年影响因子：8.4 ）

ISSN： 0301-4797

年卷期： 2023 年 338 卷

页码：

收录情况： SCI

摘要： The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band fre-quency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining infor-mation from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/ Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.