Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (...

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Autores principales:	Lucio Mascolo, Tomas Martinez-Marin, Juan M. Lopez-Sanchez
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	phenology grid-based filter SAR Sentinel-1 Science Q
Acceso en línea:	https://doaj.org/article/aea20fcdfc0e496eb3ceeb0eedf2794f
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spelling	oai:doaj.org-article:aea20fcdfc0e496eb3ceeb0eedf2794f2021-11-11T18:54:00ZOptimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data10.3390/rs132143322072-4292https://doaj.org/article/aea20fcdfc0e496eb3ceeb0eedf2794f2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4332https://doaj.org/toc/2072-4292In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ <i>R</i><sup>2</sup> ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.Lucio MascoloTomas Martinez-MarinJuan M. Lopez-SanchezMDPI AGarticlephenologygrid-based filterSARSentinel-1ScienceQENRemote Sensing, Vol 13, Iss 4332, p 4332 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	phenology grid-based filter SAR Sentinel-1 Science Q
spellingShingle	phenology grid-based filter SAR Sentinel-1 Science Q Lucio Mascolo Tomas Martinez-Marin Juan M. Lopez-Sanchez Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
description	In the last decade, suboptimal Bayesian filtering (BF) techniques, such as Extended Kalman Filtering (EKF) and Particle Filtering (PF), have led to great interest for crop phenology monitoring with Synthetic Aperture Radar (SAR) data. In this study, a novel approach, based on the Grid-Based Filter (GBF), is proposed to estimate crop phenology. Here, phenological scales, which consist of a finite number of discrete stages, represent the one-dimensional state space, and hence GBF provides the optimal phenology estimates. Accordingly, contrarily to literature studies based on EKF and PF, no constraints are imposed on the models and the statistical distributions involved. The prediction model is defined by the transition matrix, while Kernel Density Estimation (KDE) is employed to define the observation model. The approach is applied on dense time series of dual-polarization Sentinel-1 (S1) SAR images, collected in four different years, to estimate the BBCH stages of rice crops. Results show that 0.94 ≤ <i>R</i><sup>2</sup> ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33.
format	article
author	Lucio Mascolo Tomas Martinez-Marin Juan M. Lopez-Sanchez
author_facet	Lucio Mascolo Tomas Martinez-Marin Juan M. Lopez-Sanchez
author_sort	Lucio Mascolo
title	Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
title_short	Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
title_full	Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
title_fullStr	Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
title_full_unstemmed	Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
title_sort	optimal grid-based filtering for crop phenology estimation with sentinel-1 sar data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/aea20fcdfc0e496eb3ceeb0eedf2794f
work_keys_str_mv	AT luciomascolo optimalgridbasedfilteringforcropphenologyestimationwithsentinel1sardata AT tomasmartinezmarin optimalgridbasedfilteringforcropphenologyestimationwithsentinel1sardata AT juanmlopezsanchez optimalgridbasedfilteringforcropphenologyestimationwithsentinel1sardata
_version_	1718431666230263808

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data

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