The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m ³ /m ³ ). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L -band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

The inter-comparison of different soil moisture (SM) products over the Tibetan Plateau (TP) reveals the inconsistency among different SM products, when compared to in situ measurement. It highlights the need to constrain the model simulated SM with the in situ measured data climatology. In this study, the in situ soil moisture networks, combined with the classification of climate zones over the TP, were used to produce the in situ measured SM climatology at the plateau scale. The generated TP scale in situ SM climatology was then used to scale the model-simulated SM data, which was subsequently used to scale the SM satellite observations. The climatology-scaled satellite and model-simulated SM were then blended objectively, by applying the triple collocation and least squares method. The final blended SM can replicate the SM dynamics across different climatic zones, from sub-humid regions to semi-arid and arid regions over the TP. This demonstrates the need to constrain the model-simulated SM estimates with the in situ measurements before their further applications in scaling climatology of SM satellite products.

Runoff parameterizations currently adopted by the (i) Noah‐MP model, (ii) Community Land Model (CLM), and (iii) CLM with variable infiltration capacity hydrology (CLM‐VIC) are incorporated into the structure of Noah land surface model, and the impact of these parameterizations on the runoff simulations is investigated for a Tibetan river. Four numerical experiments are conducted with the default Noah and three aforementioned runoff parameterizations. Each experiment is forced with the same set of atmospheric forcing, vegetation, and soil parameters. In addition, the Community Earth System Model database provides the maximum surface saturated area parameter for the Noah‐MP and CLM parameterizations. A single‐year recurrent spin‐up is adopted for the initialization of each model run to achieve equilibrium states. Comparison with discharge measurements shows that each runoff parameterization produces significant differences in the separation of total runoff into surface and subsurface components and that the soil water storage‐based parameterizations (Noah and CLM‐VIC) outperform the groundwater table‐based parameterizations (Noah‐MP and CLM) for the seasonally frozen and high‐altitude Tibetan river. A parameter sensitivity experiment illustrates that this underperformance of the groundwater table‐based parameterizations cannot be resolved through calibration. Further analyses demonstrate that the simulations of other surface water and energy budget components are insensitive to the selected runoff parameterizations, due to the strong control of the atmosphere on simulated land surface fluxes induced by the diurnal dependence of the roughness length for heat transfer and the large water retention capacity of the highly organic top soils over the plateau.

The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR-earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m ³ /m ³ , Bias = -0.083 m ³ /m ³ , R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m ³ /m ³ , Bias = -0.039 m ³ /m ³ , R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m ³ /m ³ , Bias = 0.091 m ³ /m ³ , R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.

The radiometric uncertainty of Synthetic Aperture Radar (SAR) observations and weather-related surface conditions caused by frozen conditions, snow and intercepted rain affect the backscatter ( ${\sigma }^0$ ) observations and limit the accuracy of soil moisture retrievals. This study estimates Sentinel-1’s radiometric uncertainty, identifies the effects of weather-related surface conditions on ${\sigma }^0$ and investigates their impact on soil moisture retrievals for various conditions regarding soil moisture, surface roughness and incidence angle. Masking rules for the surface conditions that disturb ${\sigma }^0$ were developed based on meteorological measurements and timeseries of Sentinel-1 observations collected over five forests, five meadows and five cultivated fields in the eastern part of the Netherlands. The Sentinel-1 ${\sigma }^0$ observations appear to be affected by frozen conditions below an air temperature of 1 ${}^{\circ }\mathrm{C}$ , snow during Sentinel-1’s morning overpasses on meadows and cultivated fields and interception after more than $1.8$ $\mathrm{m}$ $\mathrm{m}$ of rain in the 12 h preceding a Sentinel-1 overpass, whereas dew was not found to be of influence. After the application of these masking rules, the radiometric uncertainty was estimated by the standard deviation of the seasonal anomalies timeseries of the Sentinel-1 forest ${\sigma }^0$ observations. By spatially averaging the ${\sigma }^0$ observations, the Sentinel-1 radiometric uncertainty improves from $0.85$ dB for a surface area of $0.25$ $\mathrm{h}\mathrm{a}$ to $0.30$ dB for 10 $\mathrm{h}\mathrm{a}$ for the VV polarization and from $0.89$ dB to $0.36$ dB for the VH polarization, following approximately an inverse square root dependency on the surface area over which the ${\sigma }^0$ observations are averaged. Deviations in ${\sigma }^0$ were combined with the ${\sigma }^0$ sensitivity to soil moisture as simulated with the Integral Equation Method (IEM) surface scattering model, which demonstrated that both the disturbing effects by the weather-related surface conditions (if not masked) and radiometric uncertainty have a significant impact on the soil moisture retrievals from Sentinel-1. The soil moisture retrieval uncertainty due to radiometric uncertainty ranges from $0.01$ ${\mathrm{m}}^3$ ${\mathrm{m}}^{-3}$ up to $0.17$ ${\mathrm{m}}^3$ ${\mathrm{m}}^{-3}$ for wet soils and small surface areas. The impacts on soil moisture retrievals are found to be weakly dependent on the surface roughness and the incidence angle, and strongly dependent on the surface area (or the ${\sigma }^0$ disturbance caused by a weather-related surface condition for a specific land cover type) and the soil moisture itself.

Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (K_s) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning’s roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning’s coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water levels with optimum floodplain roughness (n_p) with channel roughness n_c = 0.03 m^−1/3/s; SWL2 is simulated water levels with calibrated values for both floodplain roughness n_p = 0.65 m^−1/3/s and channel roughness n_c = 0.021 m^−1/3/s; and SWL3 is simulated water levels with calibrated channel roughness n_c and spatial Manning’s coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R²), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R² = 0.95, which was improved in SWL2 to E = 0.95 and R² = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R² = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models.