Continental-scale mapping of soil pH with SAR-optical fusion based on long-term earth observation data in google earth engine

作者：Yajun Geng, Tao Zhou, Zhenhua Zhang, Buli Cui, Junna Sun, Lin Zeng, Runya Yang, Nan Wu, Tingting Liu, Jianjun Pan, Bingcheng Si, Angela Lausch · 发表于：Ecological Indicators · 年份：2024 · DOI：10.1016/j.ecolind.2024.112246 · 被引用次数：13 · 研究领域：Soil Geostatistics and Mapping、Geochemistry and Geologic Mapping、Soil Moisture and Remote Sensing

• The selection of satellite sensor and radar system parameters greatly affected the model output. • The model was improved when more polarizations, orbital directions and frequencies were involved. • Models built using multiband radar datasets achieved a comparable accuracy to models based on optical data. • The model built by fusing SAR-optical data achieved better results than the model without SAR data. • The development of this continental-scale DSM work largely benefits from GEE. The advent of cloud computing platforms (e.g., Google Earth Engine (GEE)) and the massive amounts of optical and radar Earth Observation (EO) data hosted by these platforms present new opportunities for mapping soil pH at large scales. However, existing studies generally lack consensus on the effects of satellite and radar sensor parameters on GEE-based soil pH prediction models. In this study, we assessed the suitability of long-term radar (C-band Sentinel-1 and L-band PALSAR-1/2) and optical (Sentinel-2) EO data on GEE for the digital mapping of soil pH on a continental (Europe) scale and determined the most appropriate radar sensor parameters. Thirteen scenarios with different data configurations were simulated and combined with the 2018 LUCAS soil database and two machine learners (boosted regression trees and extreme gradient boosting) to develop soil prediction models. Results showed that the selection of modeling techniques, satellite sensors and radar system parameters largely affected ...