Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as "open-loop" models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byrans Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3E(SWI), SMOSSWI, AMSR2(SWI), and ASCAT(SWI), with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50% of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six openloop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by C0 :12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by C0:06, suggesting that data assimilation yields significant benefits at the global scale

Relief effects on the L-band emission of a bare soil

In a combined experimental and model study, we investigated effects of surface topography (relief) on the thermal L-band emission of a sandy soil. To this end, brightness temperatures of two adjacent footprint areas were measured quasi-simultaneously with an L-band radiometer at the observation angle of 55° relative to nadir for one year. One footprint featured a distinct relief in the form of erosion gullies with steep slopes, whereas the surface of the second footprint was smooth. Additionally, hydrometeorological variables, in situ soil moisture and temperature were measured, and digital terrain models of the two scenes were derived from terrestrial laser scanning. A facet model, taking into account the topography of the footprint surfaces as well as the antenna’s directivity, was developed and brightness temperatures of both footprints were simulated based on the hydrometeorological and in situ soil data. We found that brightness temperatures of the footprint with the distinct surface relief were increased at horizontal and decreased at vertical polarization with respect to those of the plane footprint. The simulations showed that this is mainly due to modifications of local (facet) observation angles and due to polarization mixing caused by the pronounced relief. Measurements furthermore revealed that brightness temperatures of both areas respond differently to changing ambient conditions indicating differences in their hydrological properties

Prediction of the error induced by topography in satellite microwave radiometric observations

A numerical simulator of satellite microwave radiometric observations of mountainous scenes, developed in a previous study, has been used to predict the relief effects on the measurements of a spaceborne radiometer. For this purpose, the trends of the error due to topography, i.e., the difference between the antenna temperature calculated for a topographically variable surface and that computed for a flat terrain versus the parameters representing the relief, have been analyzed. The analysis has been mainly performed for a mountainous area in the Alps by assuming a simplified land-cover scenario consisting of bare terrain with two roughness conditions (smooth and rough soils) and considering L- and C-bands, i.e., those most suitable for soil moisture retrieval. The results have revealed that the error in satellite microwave radiometric observations is particularly correlated to the mean values of the height and slope of the radiometric pixel, as well as to the standard deviations of the aspect angle and local incidence angle. Both a regression analysis and a neural-network approach have been applied to estimate the error as a function of the parameters representing the relief, using the simulator to build training and test sets. The prediction of the topography effects and their correction in radiometric images have turned out to be feasible, at least for the scenarios considered in this study