Abstract
Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.
Highlights
Soil moisture modulates water and energy feedback between the land surface and the lower atmosphere by determining the partitioning of incoming net radiation into latent and sensible heat (Seneviratne et al, 2010, 2013)
The presence of errors in the vertical variability of soil properties and surface soil moisture (SSM)–root-zone soil moisture (RZSM) coupling strength are selected because both factors control the propagation of soil moisture error from the surface soil layer to deeper layers, and we focus on both the SSM and RZSM retrieval accuracy
Relative to SSM, the benefit of Soil Moisture Active Passive (SMAP) data assimilation (i.e., L4) is reduced for RZSM, and the mean relative R improvement is only 7 % ( R = 0.034 [–]). This reduction is expected since assimilated SMAP Tbs are primarily sensitive to soil moisture conditions in the surface (0–5 cm) layer
Summary
Soil moisture modulates water and energy feedback between the land surface and the lower atmosphere by determining the partitioning of incoming net radiation into latent and sensible heat (Seneviratne et al, 2010, 2013). The SMAP observations contain temporal data gaps and are only representative of conditions within only the first 5 cm of the vertical soil moisture column (Entekhabi et al, 2010). To address these limitations, the SMAP Level-4 surface and root-zone soil moisture (L4) algorithm assimilates SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM) to derive an analysis of surface (0– 5 cm) and root-zone (0–100 cm) soil moisture estimates with global, 3-hourly coverage (Reichle et al, 2017a, b, 2019)
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