IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Jan 2021)
Evaluation of SMAP, SMOS, and AMSR2 Soil Moisture Products Based on Distributed Ground Observation Network in Cold and Arid Regions of China
Abstract
Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of ∼0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ∼0.10 m3/m3 as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique–Centre d'Etudes Spatiales de la BIOsphère product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ∼0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.
Keywords
