Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

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

et al.. Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, Elsevier, 2012, Abstract A disaggregation algorithm is applied to 40 km resolution SMOS (Soil Moisture and Ocean Salinity) surface soil moisture using 1 km resolution MODIS (MODerature resolution Imaging Spectroradiometer), 90 m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), and 60 m resolution Landsat-7 data. DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived soil evaporative efficiency (ratio of actual to potential evaporation) and near-surface soil moisture. The objective is three-fold: (i) evaluating DISPATCH at a range of spatial resolutions using readily available multi-sensor thermal data, (ii) deriving a robust calibration procedure solely based on remotely sensed data, and (iii) testing the linear or nonlinear behaviour of soil evaporative efficiency. Disaggregated soil moisture is compared with the 0-5 cm in situ measurements collected each month land area in Catalunya, Spain. The target downscaling resolution is set to 3 km using MODIS data and to 100 m using ASTER and Landsat data. When comparing 40 km SMOS, 3 km disaggregated and 100 m disaggregated data with the in situ measurements aggregated at corresponding resolution, results indicate that DISPATCH improves the spatio-temporal correlation with in situ measurements at both 3 km and 100 m resolutions. A yearly calibration of DISPATCH is more efficient than a daily calibration. Assuming a linear soil evaporative efficiency model is adequate at kilometric resolution. At 100m resolution, the very high spatial variability in the irrigated area makes the linear approximation poorer. By accounting for non-linearity effects, the slope of the linear regression between disaggregated and in situ measurements is increased from 0.2 to 0.5. Such a multi-sensor remote sensing approach has potential for operational multi-resolution monitoring of surface soil moisture and is likely to help parameterize soil evaporation at integrated spatial scales.

No abstract

A deterministic approach for downscaling 40km resolution Soil Moisture and Ocean Salinity (SMOS) observation is developed from 1km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is xed to 10 times the spatial resolution of MODIS thermal data (10km). Four dierent analytic downscaling relationships are derived from MODIS derived and physically-based model predictions of soil evaporative eciency (). The four downscaling algorithms dier with regards to i) the assumed relationship (linear or nonlinear) between and near-surface soil moisture, and ii) the scale at which soil parameters are available (40km or 10km). The 1km resolution airborne L-band brightness temperature from the National Airborne Field Experiment 2006 (NAFE'06) are used to generate a time series of eleven clear sky 40km by 60km near-surface soil moisture observations to represent SMOS pixels across the three-week experiment. The overall root mean square dierence between downscaled and observed soil moisture varies between 1.4% v/v and 1.8% v/v depending on the downscaling algorithm with soil moisture values ranging from 0 to 15% v/v. The accuracy and robustness of the downscaling algorithms are discussed in terms of their assumptions and applicability to SMOS