Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates

Abstract

International audienceGlobal Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes ("tropical humid", "temperate humid", etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ("desert temperate", "desert tropical", etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI ≤ 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI >1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI = 6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10 years, which can be used to study land-atmosphere processes

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

“…Conversely, both SMAP L4 SM and AMSR2 L3 SM exhibited weak correlations with the reference data in dry regions (e.g., for the Shrub/Scrub land cover type in the western region) (Figures 7 and 9), since that region is mainly desert with low vegetation cover (Figures 13 and 14). These results could be related to the low range of variation in soil moisture in these regions, which roughly corresponds to the expected retrieval accuracy of the remotely sensed products [22]. Meanwhile, both SMAP L4 SM and AMSR2 L3 SM also exhibited weak correlations with the reference data and large TC estimate error in evergreen forest regions.…”

Soil Moisture Mapping from Satellites: An Intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over Two Dense Network Regions at Different Spatial Scales

“…For a consistent and fair comparison, in our study we use the nearest interpolation method to resample the SMAP passive, the SMOS, and the FY3B soil moisture products to a spatial resolution of 0.25 • , the same as that of JAXA or LPRM AMSR2 and ESA CCI, as well as to resample the SMAP enhanced passive soil moisture to a spatial resolution of 0.1 • , the same as that of JAXA AMSR2. The same method has been applied in many comparison studies of soil moisture products [16,22,57,58]. The blue grid at a 0.1 • resolution and the red grid at a 0.25 • resolution, shown in Figure 1, were selected as the validation grids in this study, since they contain the most stations in the two network regions.…”

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

“…Several studies have demonstrated soil moisture estimation from space using passive microwave (e.g., [2][3][4][5]) and active microwave (scatterometer) (e.g., [4,6,7]) at a coarse (25-150 km) spatial resolution. On the other hand, active microwave SAR data have been successfully used to retrieve the soil moisture at a finer (less than 100 m) spatial resolution but with a coarse (approximately 3 weeks) temporal resolution [8][9][10][11].…”

“…SMOS provides soil moisture at a temporal resolution of 3 days at the equator and at a nominal spatial resolution of 40 km [26]. Several validation studies provided insights into the reliability and robustness of the soil moisture estimates from SMOS [2,[27][28][29] and over India [30]. RADAR SATellite-2 (RADARSAT-2) is an active microwave satellite mission operating in C-band, capable of retrieving surface soil moisture at a spatial resolution of less than 100 m and with a temporal resolution of 24 days [31].…”