Details

Autor(en) / Beteiligte

• Guo, Tianhao
• Zheng, Jia
• Wang, Chunmei
• Tao, Zui
• Zheng, Xingming
• Wang, Qi
• Li, Lei
• Feng, Zhuangzhuang
• Wang, Xigang
• Li, Xinbiao
• Ke, Liwei

Titel

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Ist Teil von

• Chinese geographical science, 2023-08, Vol.33 (4), p.649-663

Ort / Verlag

Heidelberg: Science Press

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Soil moisture plays an important role in crop yield estimation, irrigation management, etc. Remote sensing technology has potential for large-scale and high spatial soil moisture mapping. However, offline remote sensing data processing is time-consuming and resource-intensive, and significantly hampers the efficiency and timeliness of soil moisture mapping. Due to the high-speed computing capabilities of remote sensing cloud platforms, a High Spatial Resolution Soil Moisture Estimation Framework (HSRSMEF) based on the Google Earth Engine (GEE) platform was developed in this study. The functions of the HSRSMEF include research area and input datasets customization, radar speckle noise filtering, optical-radar image spatio-temporal matching, soil moisture retrieving, soil moisture visualization and exporting. This paper tested the performance of HSRSMEF by combining Sentinel-1, Sentinel-2 images and in-situ soil moisture data in the central farmland area of Jilin Province, China. Reconstructed Normalized Difference Vegetation Index (NDVI) based on the Savitzky-Golay algorithm conforms to the crop growth cycle, and its correlation with the original NDVI is about 0.99 ( P < 0.001). The soil moisture accuracy of the random forest model ( R 2 = 0.942, RMSE = 0.013 m 3 /m 3 ) is better than that of the water cloud model ( R 2 = 0.334, RMSE = 0.091 m 3 /m 3 ). HSRSMEF transfers time -consuming offline operations to cloud computing platforms, achieving rapid and simplified high spatial resolution soil moisture mapping.