Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada
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Título: | Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada |
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Autor/es: | Xie, Qinghua | Lai, Kunyu | Wang, Jinfei | Lopez-Sanchez, Juan M. | Shang, Jiali | Liao, Chunhua | Zhu, Jianjun | Fu, Haiqiang | Peng, Xing |
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: | Synthetic aperture radar (SAR) | Polarimetric SAR (PolSAR) | Crop classification | Crop monitoring | Time-series | RADARSAT-2 | Agriculture |
Área/s de conocimiento: | Teoría de la Señal y Comunicaciones |
Fecha de publicación: | 5-abr-2021 |
Editor: | MDPI |
Cita bibliográfica: | Xie Q, Lai K, Wang J, Lopez-Sanchez JM, Shang J, Liao C, Zhu J, Fu H, Peng X. Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada. Remote Sensing. 2021; 13(7):1394. https://doi.org/10.3390/rs13071394 |
Resumen: | Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channel ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research. |
Patrocinador/es: | This research was funded in part by the National Natural Science Foundation of China (Grant No. 41,804,004, 41,820,104,005, 41,531,068, 41,904,004), the Canadian Space Agency SOAR-E Program (Grant No. SOAR-E-5489), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUG190633), and the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under project TEC2017-85244-C2-1-P. |
URI: | http://hdl.handle.net/10045/114123 |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs13071394 |
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/rs13071394 |
Aparece en las colecciones: | INV - SST - Artículos de Revistas |
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