Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain

文献类型： 外文期刊

第一作者： Zhou, Tao

作者： Zhou, Tao;Geng, Yajun;Lv, Wenhao;Si, Bingcheng;Zhou, Tao;Lausch, Angela;Zhou, Tao;Lausch, Angela;Xiao, Shancai;Zhang, Peiyu;Xu, Xiangrui;Chen, Jie;Wu, Zhen;Pan, Jianjun;Si, Bingcheng

作者机构：

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.

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