SMAP Soil Moisture Product Assessment over Wales, U.K., Using Observations from the WSMN Ground Monitoring Network

Abstract

Soil moisture (SM) is the primary variable regulating the soil temperature (ST) differences between daytime and night-time, providing protection to crop rooting systems against sharp and sudden changes. It also has a number of practical applications in a range of disciplines. This study presents an approach to incorporating the effect of ST for the accurate estimation of SM using Earth Observation (EO) data from NASA’s SMAP sensor, one of the most sophisticated satellites currently in orbit. Linear regression analysis was carried out between the SMAP-retrieved SM and ground-measured SM. Subsequently, SMAP-derived ST was incorporated with SMAP-derived SM in multiple regression analysis to improve the SM retrieval accuracy. The ability of the proposed method to estimate SM under different seasonal conditions for the year 2016 was evaluated using ground observations from the Wales Soil Moisture Network (WSMN), located in Wales, United Kingdom, as a reference. Results showed reduced retrieval accuracy of SM between the SMAP and ground measurements. The R2 between the SMAP SM and ground-observed data from WSMN was found to be 0.247, 0.183, and 0.490 for annual, growing and non-growing seasons, respectively. The values of RMSE between SMAP SM and WSMN observed SM are reported as 0.080 m3m−3, 0.078 m3m−3 and 0.010 m3m−3, with almost zero bias values for annual, growing and non-growing seasons, respectively. Implementation of the proposed scheme resulted in a noticeable improvement in SSM prediction in both R2 (0.558, 0.440 and 0.613) and RMSE (0.045 m3m−3, 0.041 m3m−3 and 0.007 m3m−3), with almost zero bias values for annual, growing and non-growing seasons, respectively. The proposed algorithm retrieval accuracy was closely matched with the SMAP target accuracy 0.04 m3m−3. In overall, use of the new methodology was found to help reducing the SM difference between SMAP and ground-measured SM, using only satellite data. This can provide important assistance in improving cases where the SMAP product can be used in practical and research applications.

An Examination of the SMAP Operational Soil Moisture Products Accuracy at the Tibetan Plateau

“…The temporal variations over the investigated period as well as the rapid reaction of the SSM to weather changes are captured by in situ SSM and the SPL3SM_36km product (Figure 2). These variations indicate that the satellite SSM estimates are highly responsive to precipitation events [48]. In situ SSM shows recognizable seasonal variations ranging between the values of 0.05 and 0.5.…”

“…As such, the differences in spatial resolution could be reduced. However, in this study, the ground station measurements were assumed to be the true values of the 9 km and 36 km footprints [48,53].…”

“…SMAP radiometer has a 9 km/36 km spatial resolution footprint. In general, the characterization of SSM spatiotemporal variability using point-based in situ measurements is challenging, as they do not align perfectly with the SMAP footprints [48]. The spatial resolution differences may introduce uncertainties since it is impossible to represent the satellite observation's exact scale by ground observation.…”

“…Herein, to address this spatial representation issue, ground-based observations have been aggregated at the 9 km and 36 km grid scale. This is an effective approach also widely used in analogous validation experiments [34,48,49]. Subsequently, station data within the same pixel were averaged.…”

“…The horizontal bar denotes the mean value. Notably, these statistical parameters have been used in similar verification studies implemented in SSM operational products [7,27,48,50].…”