Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
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Título: | Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data |
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Autor/es: | Mascolo, Lucio | Martínez Marín, Tomás | Lopez-Sanchez, Juan M. |
Grupo/s de investigación o GITE: | Señales, Sistemas y Telecomunicación |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Phenology | Grid-based filter | SAR | Sentinel-1 |
Área/s de conocimiento: | Teoría de la Señal y Comunicaciones |
Fecha de publicación: | 28-oct-2021 |
Editor: | MDPI |
Cita bibliográfica: | Mascolo L, Martinez-Marin T, Lopez-Sanchez JM. Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data. Remote Sensing. 2021; 13(21):4332. https://doi.org/10.3390/rs13214332 |
Resumen: | 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 ≤ R2 ≤ 0.98, 5.37 ≤ RMSE ≤ 7.9 and 20 ≤ MAE ≤ 33. |
Patrocinador/es: | This research was funded in part by the Spanish Ministry of Science and Innovation (State Agency of Research, AEI) and the European Funds for Regional Development (EFRD) under Projects TEC2017-85244-C2-1-P and PID2020-117303GB-C22, and in part by the University of Alicante (ref. VIGROB-114). |
URI: | http://hdl.handle.net/10045/119444 |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs13214332 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/rs13214332 |
Aparece en las colecciones: | INV - SST - Artículos de Revistas |
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