Evaluation of remote sensing soil moisture data products with a new approach to analyse footprint mismatch with in-situ measurements

Abstract

Global-scale surface soil moisture (SSM) products (e.g. SMAP L3.0, ASCAT V3.0, ESA/CCI V7.1 and GLDAS V2.2) are vital for applications in hydrology, climate variability, and agriculture. This study uses a new SSM evaluation approach by combining temporal evolution, Coefficient of Variation (CV), Cumulative Distribution Function (CDF), evaluation metrics, and Triple Collocation Analysis (TCA) to assess SSM accuracy and spatial–temporal variability, particularly the impact of footprint mismatch when comparing retrieved SSM with in-situ measurements. Results revealed significant spatial variability and seasonal patterns in SSM, as indicated by the CV values and temporal evaluations at different resampling scales. The variability captured by in-situ measurements was comparable to that of SSM products. The impact of footprint mismatch between in-situ measurements and data products, particularly for SMAP and ASCAT SSM, was more significant and led to substantial differences in evaluation metrics between smaller and larger spatial scales. TCA alone cannot reliably assess the accuracy of global-scale SSM products without in-situ SSM measurements. Overall, our findings highlight the critical role of footprint mismatch on the estimated accuracy of SSM products and underscore the need to combine multiple evaluations into an overall scoring indicator, as proposed in this study.