Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product

Abstract

Cyclone Global Navigation Satellite System (CYGNSS) soil moisture (SM) product is characterized by high temporal resolution, but the relative strengths and weaknesses of this new product are unknown. In this article, we analyze the performance of CYGNSS SM product across varied land covers and climates, using the triple collocation (TC) analysis and in situ validation. The Soil Moisture Active Passive, Advanced Microwave Scanning Radiometer 2 Land Parameter Retrieval Model, and European Space Agency Climate Change Initiative Active SM products were used as references as well as data alternatives to calculate TC-based standard deviation (SDTC), correlation (RTC), and in situ validation Pearson's correlation coefficient (R), unbiased root-mean-square error (ubRMSE). The TC analysis indicated that CYGNSS had a relatively low median SDTC of 0.024 m3/m3 and RTC of 0.419. Validation based on 251 in situ SM stations showed that CYGNSS obtained a relatively low median ubRMSE of 0.057 m3/m3 along with a low median R of 0.414. Both interproduct comparisons of triple collocation (TC) analysis and in situ validations revealed that the CYGNSS product was characterized by small TC-based standard deviation (SDTC) and unbiased root-mean-square error (ubRMSE) but performed poorly in capturing SM temporal variability. Additionally, the performance degradation for CYGNSS capturing the SM temporal variability over the barren areas including in Northern Africa, the Arabian Peninsula, and Central Australia with arid/semiarid climates, and forested regions including in eastern South America, the Indo-China Peninsula, and Southeastern China with temperate/tropical climates. This suggests that capturing SM temporal variations over barren and forests regions is a key priority to improve CYGNSS SM algorithms.