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

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.

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

“…The reason may be related to the change detection algorithm used to retrieve the soil moisture from the SAR images. It is based on parameters that have been shown to be sensitive to the season [71] and on assumptions derived from the use of SM time series by ERS scatterometer data [29]. Nevertheless, we have demonstrated here that ASAR WS data are suitable to assess the quality of the coarser spatial resolution ECV SM product.…”

HydroCosmo: The Monitoring of Hydrological Parameters on Agricultural Areas by using Cosmo-SkyMed Images

“…In particular, vegetation biomass is better identified at a high incidence angle and HV polarization, whereas soil moisture is well retrieved at HH polarization and at steeper incidence angles [e.g. Bentamy et al, 1994;Zribi et al, 2007;Van Doninck et al, 2012]. On the other hand, most applications require estimates of the global parameters that are useful for agricultural yield prediction and management, such as leaf area index and plant water content.…”

Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas

“…At a low spatial resolution, the change in radar signal with time could be related to the change of soil moisture only. In the change detection approach, the radar signal on a given date is compared to the radar signal acquired in very wet and very dry periods to provide a relative surface soil moisture index, ranging between 0 and 1 (0 for the driest conditions, 1 for the wettest conditions) [36][37][38]. Using the change detection approach, Van doninck et al [37] operationally mapped the soil moisture over Calabria (Italy) using ASAR Wide Swath images in VV polarization with an accuracy of 7.3 vol %.…”

“…In the change detection approach, the radar signal on a given date is compared to the radar signal acquired in very wet and very dry periods to provide a relative surface soil moisture index, ranging between 0 and 1 (0 for the driest conditions, 1 for the wettest conditions) [36][37][38]. Using the change detection approach, Van doninck et al [37] operationally mapped the soil moisture over Calabria (Italy) using ASAR Wide Swath images in VV polarization with an accuracy of 7.3 vol %. Zribi et al [36] operationally mapped the soil moisture over the northern and central parts of Tunisia using ENVISAT/ASAR data with a precision of approximately 3.5 vol %.…”