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Spatial information on inundation dynamics is expected to improve greenhouse gas estimates in climate models. Satellite data can provide land cover information from local to global scale. The detection capability for dynamics is however limited. Cloud cover and daylight independent methods are required for frequent updates. Suitable are therefore sensors which make use of microwaves. The purpose of the present study is to assess such data for determination of wetland dynamics from the viewpoint of use in climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band due to, among microwave systems, comparably good spatial resolution and data availability. Continuity is also expected for such systems. Simple classification algorithms can be applied to detect open water in an automatised way allowing the processing of time series. Such approaches are robust when the water surface is smooth. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150m resolution) have been investigated for implementation of an automated detection procedure of open water fraction. More than 4000 samples (single acquisitions tiled into 0.5 degree grid cells) have been analysed for July/August 2007 and 2008. Modification of input parameters results in differences below 1% open water fraction. The actual challenge is the frequent occurrence of waves due to wind and precipitation. This reduces the separability of the water class from other land cover. The possible update intervals for surface water extent are therefore decreased considerably. Statistical measures of the backscatter distribution can be applied in order to retrieve the for classification suitable data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution has been used for assessment. On average only 40% of acquisitions allow a separation of the open water class. Satellite data are available every 2-3 days over the Western Siberian study region. With respect to the irregular acquisition intervals and varying length of unsuitable weather periods a minimum update interval of 10 days is suggested for the Northern Eurasian test case. Although SAR data availability is currently constraint future satellite missions which aim for operational services such as Sentinel-1 with its C-band SAR instrument may provide the basis for inundation monitoring in support of climate modelling. [ABSTRACT FROM AUTHOR]

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling.

The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R²) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.