Abstract:
This report provides an assessment of Version 5 of the SMAP Level 4 Surface and Root Zone Soil Moisture (L4_SM) product, which was first released on 27 August 2020. The assessment includes comparisons of L4_SM soil moisture estimates with in situ measurements from SMAP core validation sites and sparse networks. The assessment further provides a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product, including observation-minus-forecast (O-F) brightness temperature residuals and soil moisture analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid-cell scale of the data product.
The Version 5 L4_SM algorithm was recalibrated to work with the substantially changed calibration of the assimilated Version 5 (R17) Level-1C brightness temperatures. Specifically, the brightness temperature scaling parameters in the Version 5 L4_SM algorithm are based on five years of SMAP observations (April 2015 – March 2020) and no longer rely on data from the Soil Moisture and Ocean Salinity (SMOS) mission. The Version 5 L4_SM algorithm also benefits from an updated calibration of the microwave radiative transfer model parameters. Moreover, the land surface modeling system underpinning the L4_SM algorithm uses an improved surface aerodynamic roughness length formulation. Furthermore, an error in the fitting procedure used for one of the topography-related functions in the Catchment model was corrected. This error potentially affected the simulation of soil moisture in about 2% of all land surface elements in previous versions. Finally, the Version 5 L4_SM algorithm includes major software infrastructure upgrades, including full compliance with the modular and extensible Earth System Modeling Framework, to facilitate future science algorithm development.
An analysis of the time-average surface and root zone soil moisture shows that the global pattern of arid and humid regions is captured by the Version 5 L4_SM estimates. Compared to Version 4, the Version 5 surface and root-zone soil moisture is generally slightly drier, owing primarily to a bug fix in the ensemble perturbations algorithm. The bug fix also removed an error in the long-term water balance of the Version 4 product, which did not close even after accounting for the (small) effect of the soil moisture analysis increments. Because of these climatological differences, the Version 4 and Version 5 products should not be combined into a single dataset for use in applications.
Results from the core validation site comparisons indicate that Version 5 of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the root-mean square (RMS) error after removal of the long-term mean error, i.e., ubRMSE≤0.04 m3 m-3, where the error is vs. the unknown true soil moisture. Computed directly against core site in situ measurements at the 9 km scale, the average unbiased RMS difference of the 3-hourly L4_SM data is 0.040 m3 m 3 for surface soil moisture and 0.027 m3 m-3 for root zone soil moisture. When factoring in the measurement error of the in situ data, the L4_SM product clearly meets the 0.04 m3 m-3 ubRMSE requirement. The L4_SM estimates are an improvement compared to estimates from a model-only open loop (OL5030) simulation, which demonstrates the beneficial impact of the SMAP brightness temperature data. Overall, L4_SM surface and root zone soil moisture estimates are more skillful than OL5030 estimates, with statistically significant improvements for surface soil moisture R and anomaly R values (based on 95% confidence intervals). Results from comparisons of the L4_SM product to in situ measurements from more than 400 sparse network sites corroborate the core validation site results.
The instantaneous soil moisture analysis increments lie within a reasonable range and result in spatially smooth soil moisture analyses. The long-term mean soil moisture analysis increments make up only a small fraction of the water budget. The O-F residuals exhibit only small regional biases on the order of 1-3 K between the (rescaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is reasonably unbiased. The globally averaged time series standard deviation of the O-F residuals is 5.5 K, which reduces to 3.5 K for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that regionally the L4_SM assimilation algorithm over- or underestimates the total (model and observation) error present in the system.
In summary, Version 5 of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.