Data Assimilation to Extract Soil Moisture Information from SMAP Observations

Abstract

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 National Aeronautics and Space Administration (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 unbiased root-mean-square error (ubRMSE) by 0.005 m 3 m −3 and 0.001 m 3 m −3 , respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m 3 m −3 , but increased the root zone bias by 0.014 m 3 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 skill degradation in other land surface variables.

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

“…The objective of TxSON was to establish a spatially representative measure of SWC for the calibration and validation of remotely sensed estimates (Chan et al, 2016, 2018; Colliander et al, 2017b, 2018; Kim et al, 2017; Ouellette et al, 2017; Bindlish et al, 2018; Das et al, 2018), upscaling exercises (Clewley et al, 2017), and data assimilation and land surface model validation (Kolassa et al, 2017, 2018; Reichle et al, 2017). Much of the past work has focused on SMAP products that are posted to the Equal‐Area Scalable Earth Version 2 (EASE2) grid.…”

Application of soil moisture information for operational water management

“…Data-driven modelling methods are suitable alternatives for process-based modelling (Todini, 2007;Solomatine and Ostfeld, 2008), especially when large amounts of data are available, such as in the Netherlands. Among others, machine learning methods for the prediction of soil moisture conditions are promising (Kolassa et al, 2017;Cai et al, 2019). In this study, we show the applicability of transfer function-noise modelling for Transfer function-noise (TFN) modelling is a data-driven method to model an observed time series by applying a linear transformation of deterministic input series known as stress series (Von Asmuth et al, 2002).…”

“…We use the SMAP (Soil Moisture Active Passive) L3 Enhanced Radiometer-only daily gridded soil moisture product for the data assimilation scheme (Entekhabi et al, 2010;Chan et al, 2018;O'Neill et al, 2018). The value of SMAP data for hydrological data assimilation has been shown in several studies (Kolassa et al, 2017;Lievens et al, 2017;Koster et al, 2018;Blyverket et al, 2019). The delivery of the enhanced SMAP soil moisture products was motivated by the gap that emerged after failure of the SMAP radar Das et al, 2018).…”

“…2 shows the performance of the soil moisture TFN modelling approach in comparison with the SMAP validation studies ofColliander et al (2017) andChan et al (2018), as well as the data-driven soil moisture modelling approach ofKolassa et al (2017), who also used SMAP observations as training data. As these studies applied spatially averaged results for the Twente study area, the TFN model results are also spatially averaged.…”

“…Especially the TFN model validated for the year 2016 performs well as shown by the uRMSE, bias, RMSE, and correlation coe cient error metrics. The comparison with the data-driven approach byKolassa et al (2017) shows that SMAP TFN modelling for…”