In the TU Wien change detection approach, is it assumed that there are incidence angles for wet and dry conditions at which the backscattering coefficient is stable despite seasonal changes in above ground vegetation biomass [1]. The incidence angle behavior of the backscattering coefficient is determined by making use of the fact that the scatterometer provides instantaneous measurements at multiple incidence angles. The incidence angle dependency is described by a second order polynomial, the slope and curvature of which have a distinct annual cycle determined by vegetation growth and decay. For operational soil moisture retrievals, these vegetation parameters (slope and curvature) are currently obtained for each day of the year based on the climatology of the entire data record. However, recent algorithm developments have allowed the dynamic estimation of these parameters. Their variation in time has been found to reflect interannual variations in vegetation phenology [2].
Results will be presented from an analysis of spatio-temporal variations in the vegetation parameters across the North American Prairies. The seasonal cycle and interannual variability of the parameters were found to vary across the transition from temperate to arid grassland. Furthermore, variations in vegetation moisture content and geometry result in differences between the values obtained using the morning and evening orbit overpasses. In addition to providing insight into vegetation condition, we will discuss the influence these variations have on normalized backscatter and retrieved soil moisture in semi-arid grasslands.
References:
[1] Wagner, W., G. Lemoine, and H. Rott, A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data, Remote Sensing of Environment, Volume 70, Issue 2, November 1999, Pages 191-207.
[2] M. Vreugdenhil, W. A. Dorigo, W. Wagner, R. A. M. de Jeu, S. Hahn and M. J. E. van Marle, "Analyzing the Vegetation Parameterization in the TU-Wien ASCAT Soil Moisture Retrieval," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3513-3531, June 2016.
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