Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

Data Assimilation to Extract Soil Moisture Information from SMAP Observations

“…Level 1 Tbs have been collected since 31 March 2015 and are provided on the 36-km resolution Equal-Area Scalable Earth version 2 (EASEv2) grid [26] as daily half-orbit files. Here we use the SMAP NN soil moisture retrieval product [25], the official SMAP Level-2 passive soil moisture retrieval product [10], and the SMAP Level 4 soil moisture analysis [18].…”

“…The SMAP L4_SM data product is generated by assimilating SMAP Level-1C Tb anomalies into the CLSM using the DA system discussed in Section 3.1 combined with a tau-omega radiative transfer model [18,35]. SMAP Tbs with an 'acceptable' quality flag-as defined in [36]-are assimilated when the model does not indicate active precipitation, frozen soil, or snow cover.…”

“…Here, we extracted surface and root zone soil moisture estimates from the Version 2 L4_SM data [18,37] for our study period and study domain (Section 3.2).…”

“…This generates higher horizontal resolution estimates of the full soil moisture profile with complete spatio-temporal coverage and often with a higher skill than that of the model or satellite observations alone [11][12][13][14][15][16][17][18].…”

“…One common approach is to match the cumulative distribution function (CDF) of the observations to that of the model estimates at each location [23,24]. Alternatively, Reichle et al [18] rescale the assimilated Tbs such that their seasonally-varying climatology matches that of the simulated Tbs in each location. While such localized bias correction techniques fulfill the requirements of the DA system, they can considerably alter the spatial and temporal patterns of the observation mean and variability, thereby removing some of the independent information provided by the satellite instruments.…”