Understanding discrepancies in soil moisture from SMAP and AMSR2: insights into performance and dry-down behavior

Abstract

Understanding the dynamic changes in soil moisture (SM) is crucial for studying land–atmosphere interactions in hydrometeorology. While numerous SM datasets have been developed for passive microwave remote sensing systems operating at various frequencies, the consistency of SM dry-down patterns observed by different sensors remains uncertain. Additionally, the use of distinct algorithms across SM products complicates direct comparisons. This study addresses these issues by producing two new enhanced-resolution (approximately 10-km) SM datasets using the same multi-channel collaborative algorithm (MCCA). These datasets are retrieved from the L-band brightness temperature (Tb) from Soil Moisture Active Passive (SMAP) and the C/X/Ku-band Tb from Advanced Microwave Scanning Radiometer 2 (AMSR2), referred to as MCCA SMAP and MCCA AMSR2. The satellite-derived SM data are evaluated and compared using 40 globally distributed SM observation networks at both regional (dense network) and grid scales. The results indicate that the Pearson correlation coefficients (R) of MCCA SMAP and MCCA AMSR2 SM in 23 dense networks are 0.795 and 0.664, respectively, with unbiased root-mean-square deviation (ubRMSD) of 0.041 m3/m3 and 0.048 m3/m3, respectively. Both datasets perform better at the regional scale than at the grid scale. The analysis shows that SMAP outperforms AMSR2 overall, although the sensing capabilities of both payloads decline with increasing vegetation water content (VWC). Further analysis of global SM dry-down patterns reveals minimal differences in the magnitude of SM dry-down between the two payloads, but slightly larger differences in the effective wilting point. A notable disparity was observed in SM memory, with SMAP exhibiting a significantly longer memory than AMSR2. Analysis of SM loss rates shows that SMAP has a lower loss rate compared to AMSR2, consistent with the theoretical expectation that lower-frequency observations, such as those from SMAP, observes deeper soil layers. These results highlight the importance of considering differences in payload configurations when using remote sensing SM products for studies of land–atmosphere interactions in hydrometeorology and for improving land surface models (LSMs).

Keywords

• MCCA
• SMAP
• AMSR2
• soil moisture
• dry-down