Abstract
Abstract. The prime aim of this study was to assess the potential of semi-empirical water cloud model (WCM) in simulating hybrid-polarized SAR backscatter signatures (RH and RV) retrieved from RISAT-1 data and integrate the results into a graphical user interface (GUI) to facilitate easy comprehension and interpretation. A predominant agricultural wheat growing area was selected in Mathura and Bharatpur districts located in the Indian states of Uttar Pradesh and Rajasthan respectively to carry out the study. The three-date datasets were acquired covering the crucial growth stages of the wheat crop. In synchrony, the fieldwork was organized to measure crop/soil parameters. The RH and RV backscattering coefficient images were extracted from the SAR data for all the three dates. The effect of four combinations of vegetation descriptors (V1 and V2) viz., LAI-LAI, LAI-Plant water content (PWC), Leaf water area index (LWAI)-LWAI, and LAI-Interaction factor (IF) on the total RH and RV backscatter was analyzed. The results revealed that WCM calibrated with LAI and IF as the two vegetation descriptors simulated the total RH and RV backscatter values with highest R2 of 0.90 and 0.85 while the RMSE was lowest among the other tested models (1.18 and 1.25 dB, respectively). The theoretical considerations and interpretations have been discussed and examined in the paper. The novelty of this work emanates from the fact that it is a first step towards the modeling of hybrid-polarized backscatter data using an accurately parameterized semi-empirical approach.
Highlights
With an increase in the number of earth observation satellites in the last few decades, use of satellite data has expanded in the context of regional and global monitoring of vegetation
A total of 140 observations were made for the crop parameters across the season while 80 soil samples were collected from bare fields to record soil properties like volumetric moisture, roughness, soil texture, type, etc
Since there is no theoretical basis to define the best set of descriptors and to predict the values of A and B coefficients, we tested four different combinations of descriptors (Table 2)
Summary
With an increase in the number of earth observation satellites in the last few decades, use of satellite data has expanded in the context of regional and global monitoring of vegetation. Ever since the launch of microwave spaceborne sensors like Radarsat-2 and European Remote Sensing Satellites (ERS), several studies have tried to develop an understanding of the temporal backscatter variation of C-band SAR measurements from a wheat crop, with the view of extracting some valuable information (Ferrazzoli et al, 1997; Saich & Borgeaud, 2000; Macelloni et al, 2001). The temporal backscatter response from the wheat fields, especially in X/C-band using vertical-vertical (VV), horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations as well as at different incidence angles across the growing season has been well interpreted and documented in the literature (Cookmartin et al, 2000; Mattia et al, 2003; He et al, 2016). The launch of ISRO’s first indigenous microwave satellite RISAT-1 has made it feasible to explore the use hybrid-polarized data for crop studies
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