High-Resolution SMAP-Derived Root-Zone Soil Moisture Using an Exponential Filter Model Calibrated per Land Cover Type

Abstract

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.

Remote sensing and global databases for soil moisture estimation at different depths in the Pernambuco state, Northeast Brazil

“…Studies have highlighted the importance of the soil moisture determination at the root zone (Stefan et al, 2021;Pablos et al, 2018;Tobin et al, 2017), especially, for agricultural applications. The data availability of root zone soil moisture is of great importance for hydrological processes and drought monitoring in the Northeast Brazil because of water scarcity arising from recurrent periods of drought.…”

“…An alternative to taking measurements at monitoring stations is to establish a relationship between the moisture on the surface and in the soil profile, as proposed in previous studies (Albergel et al, 2008;Wagner et al, 1999). The soil water index (SWI) applies an exponential filter on data estimated, for example, by the soil moisture and ocean salinity (SMOS) satellite and soil moisture active passive (SMAP) satellite (Ford et al, 2014;Pablos et al, 2018;Stefan et al 2021). In addition, the soil moisture estimated with the filter can be used to evaluate the data simulated by hydrological and land-surface models.…”

“…The SWI has been applied in a variety of studies, including validation of the root zone soil moisture per land cover type (Stefan et al, 2021), different climate regions (Gao et al, 2019;Zohaib et al, 2017), spatiotemporal trends (Zohaib et al, 2017), and comparison with other methods (Zhang et al, 2021;Gao et al, 2019), demonstrating its usability. On the other hand, recent studies have attempted to demonstrate the suitability of using soil moisture from remote sensing and land-surface models for drought monitoring (Zhang et al, 2021;Liu et al, 2019;Cammalleri et al, 2017).…”