Abstract
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m−3 and 0.001 m3 m−3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m−3, but increased the root zone bias by 0.014 m3 m−3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.
Highlights
The importance of soil moisture in hydrological and land surface boundary layer processes has long been recognized (e.g., [1,2,3,4]), and the need for high quality soil moisture observations to enhance our understanding of these processes has been identified [5]
We compare the assimilation of the Neural network (NN) retrievals without further bias correction (DA-NN) to the assimilation of the same retrievals using standard local CDF-matching bias correction (DA-NN-lCDF)
The local bias correction applied in the DA-NN-lCDF experiment removes systematic differences between the model and the observations prior to the assimilation and—by design—results in mean soil moisture differences without strong spatial features (Figure 1b)
Summary
The importance of soil moisture in hydrological and land surface boundary layer processes has long been recognized (e.g., [1,2,3,4]), and the need for high quality soil moisture observations to enhance our understanding of these processes has been identified [5]. Direct observations of soil moisture can be obtained with in situ sensors, but these are constrained to point-scale measurements at a limited number of locations. Satellite instruments are able to observe soil moisture globally with a local revisit time of 2–3 days. Two passive L-band satellite missions have been launched in recent years, the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission in 2009 [8] and the National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission in 2015 [7]. Soil moisture retrieval products from SMOS and SMAP have been shown to have high skill in capturing soil moisture variations [9,10], many applications require observations of the complete soil moisture profile and with finer spatial and temporal resolutions than those of SMOS and SMAP
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