Abstract
Abstract. A change detection algorithm is applied on a three year time series of ASAR Wide Swath images in VV polarization over Calabria, Italy, in order to derive information on temporal soil moisture dynamics. The algorithm, adapted from an algorithm originally developed for ERS scatterometer, was validated using a simple hydrological model incorporating meteorological and pedological data. Strong positive correlations between modelled soil moisture and ASAR soil moisture were observed over arable land, while the correlation became much weaker over more vegetated areas. In a second phase, an attempt was made to incorporate seasonality in the different model parameters. It was observed that seasonally changing surface properties mainly affected the multitemporal incidence angle normalization. When applying a seasonal angular normalization, correlation coefficients between modelled soil moisture and retrieved soil moisture increased overall. Attempts to account for seasonality in the other model parameters did not result in an improved performance.
Highlights
Up till the operational retrieval of spatially distributed soil moisture from remote sensing systems is limited to coarse resolution radiometers and scatterometers
Downscaling to 1 km resolution was done to be able to compare the Advanced Synthetic Aperture Radar (ASAR) data with the coarser resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data and the soil moisture data obtained from the hydrological model, even though this implies a reduction of spatial resolution to that of ASAR in Global Monitoring mode
In the scatterplot of σ 0(30) and the corresponding NDVI (Fig. 12) of the arable land pixel, a general positive relationship is observed which is consistent with both NDVI and ASAR backscatter being in phase with the seasonal soil moisture cycle
Summary
The operational retrieval of spatially distributed soil moisture from remote sensing systems is limited to coarse resolution radiometers and scatterometers. Other global soil moisture products were generated from data collected by the scatterometers onboard the European Remote Sensing satellites ERS-1 and ERS-2 and their successor, the advanced scatterometer (ASCAT), onboard the MetOp satellites (Wagner et al, 1999b; Bartalis et al, 2007; Naeimi et al, 2009) All of these sensors are characterized by a low spatial resolution (25–50 km), which makes them of limited utility for applications at finer scales. The same product was validated using in situ and airborne soil moisture data over an area in southeastern Australia (Mladenova et al, 2010) These kinds of multitemporal approaches offer opportunities for routinely mapping soil moisture at high spatial resolution with the upcoming Sentinel-1 mission (Attema et al, 2007). The influence of vegetation phenology in the different processing steps is assessed using a simple vegetation index from optical remote sensing
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