Disaggregation of SMOS Soil Moisture in Southeastern Australia

Abstract

Disaggregation based on Physical And Theoretical 4 scale Change (DisPATCh) is an algorithm dedicated to the dis-5 aggregation of soil moisture observations using high-resolution 6 soil temperature data. DisPATCh converts soil temperature fields 7 into soil moisture fields given a semi-empirical soil evaporative 8 efficiency model and a first-order Taylor series expansion around 9 the field-mean soil moisture. In this study, the disaggregation 10 approach is applied to soil moisture and ocean salinity (SMOS) 11 data over the 500 km by 100 km AACES (Australian Airborne 12 Calibration/validation Experiments for SMOS) area. The 40-km 13 resolution SMOS surface soil moisture pixels are disaggregated 14 at 1-km resolution using the soil skin temperature derived from 15 moderate resolution imaging spectroradiometer (MODIS) data, 16 and subsequently compared with the AACES intensive ground 17 measurements aggregated at 1-km resolution. The objective is to 18 test DisPATCh under various surface and atmospheric conditions. 19 It is found that the accuracy of disaggregation products varies 20 greatly according to season: while the correlation coefficient be-21 tween disaggregated and in situ soil moisture is about 0.7 during 22 the summer AACES, it is approximately zero during the winter 23 AACES, consistent with a weaker coupling between evaporation 24 and surface soil moisture in temperate than in semi-arid climate. 25 Moreover, during the summer AACES, the correlation coefficient 26 between disaggregated and in situ soil moisture is increased from 27 0.70 to 0.85, by separating the 1-km pixels where MODIS temper-28 ature is mainly controlled by soil evaporation, from those where 29 MODIS temperature is controlled by both soil evaporation and 30 vegetation transpiration. It is also found that the 5-km resolution 31 atmospheric correction of the official MODIS temperature data 32 has a significant impact on DisPATCh output. An alternative at-33 mospheric correction at 40-km resolution increases the correlation 34 coefficient between disaggregated and in situ soil moisture from 35 0.72 to 0.82 during the summer AACES. Results indicate that

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

“…The NSCE of the fixed window output is also greater than the coarse input NSCE for all resolutions considered, which suggests independently downscaled when the fixed window procedure is used. They are also similar to the abrupt changes observed for other methods that downscale each cell independently [Fang and Lakshmi, 2014, Merlin et al, 2013, Merlin et al, 2012.…”

“…Other studies have downscaled multiple coarse grid values but have not considered this issue in depth. Several of these algorithms downscale each coarse grid cell independently from the adjacent coarse grids [Fang and Lakshmi, 2014, Merlin et al, 2013, Merlin et al, 2012, but the resulting soil moisture maps show unnatural discontinuities in the soil moisture values at the coarse grid boundaries. Such discontinuities might be problematic for applications like routing vehicles across the landscape [Flores et al, 2014].…”

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

“…Similar performance was observed by Molero et al [2], for which the best agreement between SMOS and the in situ data were obtained for a semiarid region. Other studies also reported better results for validating soil moisture remote sensing in semiarid land in comparison to temperate regions [45,46]. The validation of SMOS data accomplished by Molero et al [2] was completed in four study areas with different climate characteristics.…”

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

“…These studies pointed out the degradation of the retrieval due to the impact of surface roughness and vegetation. Several studies suggested spatial downscaling of passive microwave soil moisture products using physical and statistical approaches (e.g., [12][13][14][15][16][17]) or by assimilating coarse scale soil moisture into a Land Data Assimilation System (LDAS) (e.g., [18]). However, few studies have demonstrated merging the active and passive microwave soil moisture [4,19] to improve the spatial and temporal availability at a moderate spatial resolution.…”

“…However, few studies have demonstrated merging the active and passive microwave soil moisture [4,19] to improve the spatial and temporal availability at a moderate spatial resolution. The spatial downscaling using physical approaches are based on the assumption that the evaporative flux is controlled by the amount of water available in the top surface layer [12,15]. The spatial distribution of evaporative flux is computed using the visible/infrared satellite products available from the satellites such as MODIS and Landsat.…”