The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau
Introduction
As a critical component in the terrestrial water cycle, soil moisture controls a variety of the hydro-meteorological and biogeochemical processes, which is even more evident in semiarid areas where strong coupling between soil moisture and precipitation occurs (Koster et al., 2004). Microwave remote sensing (Bartalis et al., 2007, Kerr et al., 2001, Njoku et al., 2003) and land surface modeling (Entin et al., 1999, Henderson-Sellers et al., 1993) are possible ways to obtain surface soil moisture (SSM) at regional or global scales. However, the accuracy of microwave satellite product is often not satisfactory for many research and application purposes (Chen et al., 2013, dall'Amico et al., 2012). Meanwhile, the SSM estimated by land surface modeling strongly depends on the model structure and parameters as well as the accuracy of input forcing data (Henderson-Sellers et al., 1993).
It is commonly recognized that the low-frequency microwave emissions are highly related to SSM (Njoku and Entekhabi, 1996, Schmugge, 1978). For the past decades, various SSM products have been developed with the launch of several microwave sensors, such as AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) (Koike et al., 2004, Njoku and Chan, 2006, Owe et al., 2008) and ASCAT (METOP-A Advanced Scatterometer) (Bartalis et al., 2007). The newly launched SMOS satellite works at L-band (1.4 GHz) which is considered ideal for retrieving SSM (Kerr et al., 2001). Prior to this mission and up to present, several validations of SMOS SSM against intensive ground measurements have been conducted and obtained different biases, with most of them beyond the anticipation of the mission (Al Bitar et al., 2012, Albergel et al., 2012, dall'Amico et al., 2012, Dente et al., 2012, Gherboudj et al., 2012, Pan et al., 2012, Sanchez et al., 2012). The aforementioned evaluations are mainly conducted in Europe (Albergel et al., 2012, dall'Amico et al., 2012, Dente et al., 2012, Sanchez et al., 2012) and North America (Al Bitar et al., 2012, Gherboudj et al., 2012, Jackson et al., 2012, Pan et al., 2012). Yet, two major issues remain to be considered for further evaluation and utilization of SMOS SSM data.
The first one is about the spatial representativeness. The original spatial resolution of SMOS brightness temperature (TB) varies with incidence angles and has a nominal resolution of about 43 km. Taking the L2 data, for example, SSM is first obtained by minimizing the differences between observed and modeled TB at multi-viewing angles within a working area of 123 km × 123 km, and then oversampled to 15-km node scales (Kerr, Waldteufel, Richaume, et al., 2010). In previous evaluations, some simply conducted the node-to-site validation (Albergel et al., 2012), or averaged the closest in-situ point measurements within a SMOS node (dall'Amico et al., 2012, Gherboudj et al., 2012), and all found the SMOS data with large biases. However, Jackson et al. (2012) implemented evaluations at watershed scale by averaging both SMOS and in-situ measurements within a 600-km2 area and found the SMOS data can approach the expected accuracy. Sanchez et al. (2012) considered different scale-matching strategies when evaluating SMOS L2 SSM data within a 1,300-km2 area, and found the average-to-average (both SMOS L2 SSM and in-situ data are averaged in spatial) evaluation shows slightly better accuracy than at a single SMOS node scale. Nevertheless, in an even larger scale (40 km × 90 km), Dente et al. (2012) found the SMOS data failed in capturing the ground truth. Therefore, the impact of spatial scale on the oversampled L2 data still needs to be investigated in great detail.
The second issue is the low temporal resolution of SMOS SSM data (~ 3 days globally, if available). Due to the severe contamination of RFI at L-band (Oliva et al., 2012), SMOS retrievals are unavailable in quite a few regions over the world (Dente et al., 2012). A possible way to overcome this problem is by using land data assimilation, which is capable to take the advantage of continuous land surface model-output and make use of satellite observations (Crow and Wood, 2003, Houser et al., 1998, Li et al., 2007, Tian et al., 2009, Yang et al., 2007). In fact, great efforts have been made to directly assimilate the SMOS brightness temperature to estimate soil moisture within the framework of weather forecast (Kerr et al., 2010b, Sabater et al., 2011).
In this study, we evaluate the SMOS SSM data, investigate its scale-dependence, and conduct land data assimilation to explore the optimal utilization of SMOS soil moisture products for the Tibetan Plateau, where land–atmosphere interaction greatly impacts the energy and water cycle of the Asian monsoon system. We first evaluate the SMOS L2 and L3 SSM data within a newly established soil moisture network located in the central Tibetan Plateau. Evaluations at the SMOS node scale and at a coarser scale (~ 100 km) are conducted to study the scale-dependence of the SMOS product applicability. Then the selected SMOS SSM data are assimilated into a land surface model to achieve better temporal resolution and accuracy. Details about the network data, SMOS SSM data, and descriptions on evaluation strategy and the land data assimilation system are provided in Section 2. Evaluation and data assimilation results are presented in Section 3. Finally, all the analyses are summarized in Section 4.
Section snippets
Ground data
The ground truth is collected within a recently established Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) within a spatial coverage of about 100 km × 100 km that matches a typical GCM (Global Climate Model) grid (Fig. 1a). This area has a generally slowly-varying terrain with rolling hills. Nearly 94% of the area is covered by alpine meadows, with very small water bodies at the west edge. High elevation and low temperature conditions are associated with very
SMOS Evaluation results
Fig. 4 shows the statistics of all SMOS retrievals at the node scale for the whole evaluation period. Generally, all retrievals show great spatial variations, and this is more evident for the descending overpass. Nevertheless, when averaged at a 100-km grid (Fig. 5a–d), the SMOS SSM data can follow the seasonal variations of ground truth much better. Meanwhile, there are fewer (for descending overpass; Fig. 4b and d) or even no (for ascending overpass; Fig. 4a and c) retrievals at all nodes
Summary
The recently launched SMOS satellite provides a magnificent opportunity to monitor the ground surface soil moisture. Various evaluations on the SMOS retrievals have been carried out worldwide, but mostly focused in Europe and America. Besides, the recently publicized SMOS L3 product is rarely evaluated. This study evaluates both SMOS L2 and L3 soil moisture products against a newly established Tibetan Plateau soil moisture network, based on which the SMOS L2 data are selected to be assimilated
Acknowledgment
This research was financially supported by National Natural Science Foundation of China (grant nos. 41325019 and 41190083), and the National Natural Science Foundation of China (grant no. 41325019). The SMOS L3 data were obtained from the “Centre Aval de Traitement des Données SMOS” (CATDS), operated for the “Centre National d'Etudes Spatiales” (CNES, France) by IFREMER (Brest, France). GLDAS datasets are collected from the NASA Goddard Earth Sciences Data and Information Services Center (GES
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