Abstract
Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, these descriptors are derived either from the covariance matrix elements or from the eigendecomposition. Therefore, this approach fails to utilize the complete polarization information of the scattered wave. In this study, we propose a target characterization parameter, θxP that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using θxP and the scattering entropy, HxP. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of θxP to the development of crop morphology at different phenological stages. During the initial growth stages, θxP values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of θxP due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised HxP/θxP clustering plane is also evident from the temporal clustering plots. This innovative clustering framework is beneficial for the operational use of Sentinel-1 SAR data for agricultural applications.
Highlights
Identification and monitoring of crop phenological stages are essential factors in agriculture for estimating crop production
The selection of Sentinel-1 datasets was based on acquisition dates that were near coincident with in-situ measurement periods
We analyze the temporal dynamics of crops using the proposed dualpolarimetric descriptors
Summary
Identification and monitoring of crop phenological stages are essential factors in agriculture for estimating crop production. For dual polarimetric HH-HV or VV-VH data, Cloude et al [24] proposed an eigen-based decomposition technique In this technique, the 2 × 2 covariance matrix is decomposed into two orthogonal eigenvectors that are further utilized to derive a target characterization parameter. Dey et al [43,44] proposed a model-free target characterization parameter using full, compact and dual co-pol data [22] This parameter utilizes the Barakat degree of polarization [45] and elements of the coherency matrix to characterize diverse target types. We derive a target characterization parameter for dualpol SAR data in this study This parameter jointly utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix.
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