Accounting for seasonality in a soil moisture change detection algorithm for ASAR Wide Swath time series

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

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology, and climatic modeling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) climate change initiative (CCI), using finer spatial resolution synthetic aperture radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse-scale CCI essential climate variable (ECV) soil moisture (ECV SM) product in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture data sets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture data sets at the Finnish sites. Overall, the two different satellite-derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse-scale ECV SM product to track soil moisture variability in time