Evaluation of SMAP Level 2, 3, and 4 Soil Moisture Datasets over the Great Lakes Region

Global long term daily 1 km surface soil moisture dataset with physics informed machine learning

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0-5 cm) at 1 km spatial and daily temporal resolution over the period 2000-2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region

The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network deployed in Millbrook, New York, between 2019 and 2021 are used. The network comprises 25 stations distributed across a 33-km SMAP pixel with a predominantly forest land cover. The in-situ soil moisture observations were collected between 6 and 7 a.m., local time. This article covers the assessment of the temporal accuracy of SMAP soil moisture by incorporating various upscaling methods. Four upscaling methods are used in this study: arithmetic average, Voronoi diagram, topographic wetness index, and land cover weighted average. The agreement between SMAP soil moisture and the upscaled in-situ measurements was gauged using the root-mean-squared difference, the mean difference, and the unbiased root-mean-squared difference. The consistency of the temporal variability of SMAP soil moisture data resulting from the four upscaling methods was analyzed. The results revealed that SMAP retrievals (soil moisture data) are systematically higher than in situ observations during the different seasons. The results indicate that the highest performance of SMAP soil moisture retrievals is in September with an ubRMSD value of 0.03 m3.m−3 for the morning and evening overpasses, which can be attributed to a lower vegetation density during the seasonal transition. The agreement with in-situ observations degrades during March–April with ubRMSD values above 0.04 m3.m−3, reaching ~0.06 m3.m−3 in April, which can be attributed to the non-reliability of in-situ measurements due to freeze\thaw transition and the challenging determination of the soil effective temperature. The ubRMSD is also higher than 0.04 m3.m−3 in the months of May–June, which could be due to the introduced vegetation effect during the growth season. These findings are consistent across all the upscaling methods. The average ubRMSD over the study period is 0.055 m3.m−3, which falls short of meeting the mission’s performance target. This study proves the need to enhance SMAP retrieval over forest sites.

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.