Abstract
Soil moisture is an important geophysical quantity for evaluating soil conditions. Traditional measurement methods such as remote sensing and field measurements cannot meet the requirements with high spatial and temporal resolution. Soil moisture retrieval based on Global Navigation Satellite System– Interferometry and Reflectometry (GNSS-IR) can be applied on the existing GNSS networks to solve these problems and has the great potential to complement existing soil moisture monitoring networks. Parameters (satellite elevation ranges, priori reflector height, and signal frequency) play a very important role in soil moisture retrieval using GNSS-IR and different selections could have great influence on the results. Previous research often relied on experience in the parameter selections, which was not conducive to the application and promotion of the technology. This study aims to investigate the effects of soil moisture retrieval using GNSS-IR based on different parameter selections. After describing the basic theory of soil moisture retrieval, the influence of the satellite elevation ranges, the priori reflector height, and the chosen signal frequency are investigated with National Science Foundation’s (NSF) Plate Boundary Observatory (PBO) data and experimental data obtained in Nanjing, China. The results show that using data within an elevation range of [5°, 30°] and arc lengths greater than 20° can reduce the mean RMSE and MAE by 1%-31.5% and 2%-31%. The selection of the priori reflector height does not have a significant influence. L2 signals (compared to L1 signals) can improve the accuracy of soil moisture retrieval and can reduce the mean RMSE and MAE by 20.9% and 20.4%.
Highlights
Soil moisture is a fundamental physical quantity used to characterize soil conditions and plays a very significant role in climate, hydrology, agriculture, and disaster warning [1][4]
This paper systematically analyses the various parameter selection strategies in soil moisture retrieval based on GNSS-IR
3) For L1 and L2 signals, the L2 signal is more suitable for soil moisture retrieval than the L1 signal
Summary
Soil moisture is a fundamental physical quantity used to characterize soil conditions and plays a very significant role in climate, hydrology, agriculture, and disaster warning [1][4]. Existing soil moisture monitoring networks cannot meet the needs of measurements with high spatial and temporal resolution. Remote sensing satellites such as the Soil Moisture and Ocean Salinity (SMOS) mission or the planned Soil Moisture Active Passive (SMAP) mission have large footprints (10 or 50 km resolution) and long periods (3 days) [5]-[8]. The merged active-passive soil moisture dataset from European Space Agency Climate Change Initiative (ESA CCI), that is representative among various of remotely sensed soil moisture products, is sensitive to the underlying surface and has low spatial and temporal resolution [9]. Global Navigation Satellite System – Interferometry and Reflectometry (GNSS-IR) is a method that uses ordinary
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