Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

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 b...

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Autores principales:	Subhadip Dey, Narayanarao Bhogapurapu, Saeid Homayouni, Avik Bhattacharya, Heather McNairn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Sentinel-1 polarimetry dual-pol crop characterization phenology unsupervised classification Science Q
Acceso en línea:	https://doaj.org/article/7cca86ff896b430194d5ba681747f379
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spelling	oai:doaj.org-article:7cca86ff896b430194d5ba681747f3792021-11-11T18:55:50ZUnsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data10.3390/rs132144122072-4292https://doaj.org/article/7cca86ff896b430194d5ba681747f3792021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4412https://doaj.org/toc/2072-4292Global 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, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> and the scattering entropy, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>H</mi><mi>xP</mi></msub></semantics></math></inline-formula>. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> to the development of crop morphology at different phenological stages. During the initial growth stages, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mi>xP</mi></msub><mo>/</mo><msub><mi>θ</mi><mi>xP</mi></msub></mrow></semantics></math></inline-formula> 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.Subhadip DeyNarayanarao BhogapurapuSaeid HomayouniAvik BhattacharyaHeather McNairnMDPI AGarticleSentinel-1polarimetrydual-polcrop characterizationphenologyunsupervised classificationScienceQENRemote Sensing, Vol 13, Iss 4412, p 4412 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sentinel-1 polarimetry dual-pol crop characterization phenology unsupervised classification Science Q
spellingShingle	Sentinel-1 polarimetry dual-pol crop characterization phenology unsupervised classification Science Q Subhadip Dey Narayanarao Bhogapurapu Saeid Homayouni Avik Bhattacharya Heather McNairn Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
description	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, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> that utilizes the 2D Barakat degree of polarization and the elements of the covariance matrix. We also propose an unsupervised clustering scheme using <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> and the scattering entropy, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>H</mi><mi>xP</mi></msub></semantics></math></inline-formula>. We utilize time-series Sentinel-1 data of canola and wheat fields over a Canadian test site to show the sensitivity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> to the development of crop morphology at different phenological stages. During the initial growth stages, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> values are low due to the low vegetation density. In contrast, at advanced phenological stages, we observe decreased values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>θ</mi><mi>xP</mi></msub></semantics></math></inline-formula> due to the appearance of complex canopy structure. Similarly, the effectiveness of the unsupervised <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>H</mi><mi>xP</mi></msub><mo>/</mo><msub><mi>θ</mi><mi>xP</mi></msub></mrow></semantics></math></inline-formula> 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.
format	article
author	Subhadip Dey Narayanarao Bhogapurapu Saeid Homayouni Avik Bhattacharya Heather McNairn
author_facet	Subhadip Dey Narayanarao Bhogapurapu Saeid Homayouni Avik Bhattacharya Heather McNairn
author_sort	Subhadip Dey
title	Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
title_short	Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
title_full	Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
title_fullStr	Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
title_full_unstemmed	Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data
title_sort	unsupervised classification of crop growth stages with scattering parameters from dual-pol sentinel-1 sar data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7cca86ff896b430194d5ba681747f379
work_keys_str_mv	AT subhadipdey unsupervisedclassificationofcropgrowthstageswithscatteringparametersfromdualpolsentinel1sardata AT narayanaraobhogapurapu unsupervisedclassificationofcropgrowthstageswithscatteringparametersfromdualpolsentinel1sardata AT saeidhomayouni unsupervisedclassificationofcropgrowthstageswithscatteringparametersfromdualpolsentinel1sardata AT avikbhattacharya unsupervisedclassificationofcropgrowthstageswithscatteringparametersfromdualpolsentinel1sardata AT heathermcnairn unsupervisedclassificationofcropgrowthstageswithscatteringparametersfromdualpolsentinel1sardata
_version_	1718431654271254528

Unsupervised Classification of Crop Growth Stages with Scattering Parameters from Dual-Pol Sentinel-1 SAR Data

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