Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy. Issue 2 (1st March 2020)

• Record Type: Journal Article
• Authors: Varade, Divyesh; Singh, Gulab; Dikshit, Onkar; Manickam, Surendar
• Is Part Of: Water resources research. Volume 56:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-01
• Subjects: snow -- SAR polarimetry -- entropy -- anisotropy -- Himalayas -- RADARSAT‐2; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR025449
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Recently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy ( H ) and anisotropy ( A ) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H (1‐ A ) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection. Plain Language Summary: Fully polarimetric SAR (PolSAR) data are widely used for the studies of snow geophysical parameters requiring good quality snow cover area (SCA) maps. There are limited methods in the literature for SCA mapping using fully PolSAR data. Some of these methods are based on multitemporal backscatter response, which requires more data sets that are commercial and expensive; efforts toward the development of methods, which can discriminate snow using a single data sets, are desirable. This study examines the potential of the proposed radar snow fraction (RSF) index, which is based on fully polarimetric SAR data utilizing the response of entropy ( H ) and anisotropy ( A ) for snow cover mapping. The parameter RSF is an extension of the H (1‐ A ) metric, which utilizes a sigmoidal filter function to this metric. The proposed method, the conventional radar snow index (RSI), and the other polarimetric parameters are evaluated for SCA mapping for different L‐band and C‐band ALOS‐2/PALSAR‐2 and RADARSAT‐2 data sets for rugged terrains in various locations in the Indian Himalayas. This study also compared the RSF with RSI for SCA mapping and delineated better results for the RSF. Key Points: We propose an index radar snow fraction for snow cover mapping using fully polarimetric SAR data The radar snow fraction is evaluated for five L‐band ALOS‐2/PALSAR‐2 and 2 C‐band RADARSAT‐2 data sets for Indian Himalayas The performance of the radar snow fraction was observed to be better than other polarimetric parameters