Abstract.A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T , is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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Crop monitoring is of great importance for e.g., yield prediction and increasing water use efficiency. The Copernicus Sentinel-1 mission operated by the European Space Agency provides the opportunity to monitor Earth’s surface using radar at high spatial and temporal resolution. Sentinel-1’s Synthetic Aperture Radar provides co- and cross-polarized backscatter, enabling the calculation of microwave indices. In this study, we assess the potential of Sentinel-1 VV and VH backscatter and their ratio VH/VV, the cross ratio (CR), to monitor crop conditions. A quantitative assessment is provided based on in situ reference data of vegetation variables for different crops under varying meteorological conditions. Vegetation Water Content (VWC), biomass, Leaf Area Index (LAI) and height are measured in situ for oilseed-rape, corn and winter cereals at different fields during two growing seasons. To quantify the sensitivity of backscatter and microwave indices to vegetation dynamics, linear and exponential models and machine learning methods have been applied to the Sentinel-1 data and in situ measurements. Using an exponential model, the CR can account for 87% and 63% of the variability in VWC for corn and winter cereals. In oilseed-rape, the coefficient of determination ( R 2 ) is lower ( R 2 = 0.34) due to the large difference in VWC between the two growing seasons and changes in vegetation structure that affect backscatter. Findings from the Random Forest analysis, which uses backscatter, microwave indices and soil moisture as input variables, show that CR is by and large the most important variable to estimate VWC. This study demonstrates, based on a quantitative analysis, the large potential of microwave indices for vegetation monitoring of VWC and phenology.

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