Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O − F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O − F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O − F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m−3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O − F residuals, ~0.01 (~0.003) m3 m−3 for surface (root zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O − F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O − F autocorrelations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain

“…As previously stated, the SMOS is only capable of retrieving the SM content of the soil top layer, which surely underestimates the root-zone SM. In the near future, the availability of root-zone SM estimates [92,93] is expected to improve the current estimation based on surface SM data for agricultural drought monitoring. Nevertheless, recent research has shown the capability of using satellite surface SM estimations for drought monitoring, e.g., using the surface SM observations from the Advanced Microwave Scanning Radiometer for Earth (AMSR-E) observation system and SMAP, after applying a cumulative distribution function (CDF) matching in situ surface SM data to remove the systematic bias and dynamic range differences [79,94].…”

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

“…The root zone soil moisture estimates are evaluated against an average of the in situ measurements for the 0-100 cm layer with each measurement weighted by the vertical extent of the represented layer. The SCAN and USCRN data were subjected to an extensive quality control process as detailed, for example, in De Lannoy et al [42] and Appendix C of Reichle et al [43]. After quality control, 181 stations were used from the SCAN and 138 from the USCRN.…”

“…The L4_SM system has been extensively tested and validated [18,52] and thus the skill of the L4_SM estimates can be considered as somewhat of a baseline for the amount of information that a DA system can extract from the SMAP observations. To some extent, the comparison with the L4_SM estimates also assesses the feasibility of the NN as a tool to project SMAP Tb into the modeled soil moisture space, which is similar to the projection of modeled soil moisture estimates into the SMAP Tb space by the L4_SM radiative transfer model (RTM) (while bearing in mind that the Tb observations are locally rescaled in the L4_SM system).…”

New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves

“…Effective dielectric constant (real part) ε eff Equations (53)- (58) Table 2 Dielectric constant for sand (real part) ε sand 3 from Table 6 Dielectric constant for clay, silt (real part) ε clay,silt 5 from Table 6 Dielectric constant for sand (imaginary part) ε clay,silt,sand 0.078 from Table 6 Soil water content (mm 3 /mm 3 ) w Measurement in simulation or estimation in retrieval Dielectric constant of bound water ε' bound , ε" bound Equations (25) and (26) required Equation (27), (33), (59) Dielectric constant of air ε' air , ε" air 1, 0 from Table 6 Angular frequency ω 2πf (e.g., f = 1.4 × 10 9 Hz for L-band)…”

“…Recently, new "physically-based" radiative transfer models were proposed [14,16,20] for the retrieval of near surface soil moisture from passive microwave measurements. These can be better combined with data assimilation [21][22][23], especially for the estimation of the root zone soil moisture using SMOS [24,25] and SMAP [26,27]. By combining our dielectric mixing model with the radiative transfer models, the TB can be obtained more accurately in the microwave spectral region, which is of course beneficial not only for the data assimilation scheme but also the field campaign for the calibration and validation such as SMOSREX [28] or SMAPVEX [29].…”