Estimation of Soil Moisture Using Multi-Source Remote Sensing and Machine Learning Algorithms in Farming Land of Northern China

作者：Quanshan Liu, Zongjun Wu, Ningbo Cui, Xiuliang Jin, Shidan Zhu, Shouzheng Jiang, Lu Zhao, Daozhi Gong · 发表于：Remote Sensing · 年份：2023 · DOI：10.3390/rs15174214 · 被引用次数：44 · 研究领域：Remote Sensing in Agriculture、Soil Moisture and Remote Sensing、Plant Water Relations and Carbon Dynamics

Soil moisture is a key parameter for the circulation of water and energy exchange between surface and the atmosphere, playing an important role in hydrology, agriculture, and meteorology. Traditional methods for monitoring soil moisture suffer from spatial discontinuity, time-consuming processes, and high costs. Remote sensing technology enables the non-destructive and efficient retrieval of land information, allowing rapid soil moisture monitoring to schedule crop irrigation and evaluate the irrigation efficiency. Satellite data with different resolutions provide different observation scales. Evaluating the accuracy of estimating soil moisture based on open and free satellite data, as well as exploring the comprehensiveness and adaptability of different satellites for soil moisture temporal and spatial observations, are important research contents of current soil moisture monitoring. The study utilized three types of satellite data, namely GF-1, Landsat-8, and GF-4, with respective temporal and spatial resolutions of 16 m (every 4 days), 30 m (every 16 days), and 50 m (daily). The gray relational analysis (GRA) was employed to identify vegetation indices that selected sensitivity to soil moisture at varying depths (3 cm, 10 cm, and 20 cm). Then, this study employed random forest (RF), Extra Tree (ETr), and linear regression (LR) algorithms to estimate soil moisture at different depths with optical satellite data sources. The results showed that the accuracy of soil moisture ...