Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping

Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops...

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Autores principales:	Shilan Felegari, Alireza Sharifi, Kamran Moravej, Muhammad Amin, Ahmad Golchin, Anselme Muzirafuti, Aqil Tariq, Na Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Sentinel 1 and 2 Copernicus Sentinels crop classification food security agricultural monitoring remote sensing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/9c889c0d2df44aba9df56ec1e45e9bc3
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id	oai:doaj.org-article:9c889c0d2df44aba9df56ec1e45e9bc3
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spelling	oai:doaj.org-article:9c889c0d2df44aba9df56ec1e45e9bc32021-11-11T15:10:12ZIntegration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping10.3390/app1121101042076-3417https://doaj.org/article/9c889c0d2df44aba9df56ec1e45e9bc32021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10104https://doaj.org/toc/2076-3417Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.Shilan FelegariAlireza SharifiKamran MoravejMuhammad AminAhmad GolchinAnselme MuzirafutiAqil TariqNa ZhaoMDPI AGarticleSentinel 1 and 2Copernicus Sentinelscrop classificationfood securityagricultural monitoringremote sensingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10104, p 10104 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sentinel 1 and 2 Copernicus Sentinels crop classification food security agricultural monitoring remote sensing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Sentinel 1 and 2 Copernicus Sentinels crop classification food security agricultural monitoring remote sensing Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Shilan Felegari Alireza Sharifi Kamran Moravej Muhammad Amin Ahmad Golchin Anselme Muzirafuti Aqil Tariq Na Zhao Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
description	Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field. The purpose of this study is to study the shortcomings and strengths of combined radar data and optical images to identify the type of crops in Tarom region (Iran). For this purpose, Sentinel 1 and Sentinel 2 images were used to create a map in the study area. The Sentinel 1 data came from Google Earth Engine’s (GEE) Level-1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. The Sen2Cor method was used to mask for clouds and cloud shadows, and the Sentinel 2 Level-1C data was sourced from the Copernicus Open Access Hub. To estimate the purpose of classification, stochastic forest classification method was used to predict classification accuracy. Using seven types of crops, the classification map of the 2020 growth season in Tarom was prepared using 10-day Sentinel 2 smooth mosaic NDVI and 12-day Sentinel 1 back mosaic. Kappa coefficient of 0.75 and a maximum accuracy of 85% were reported in this study. To achieve maximum classification accuracy, it is recommended to use a combination of radar and optical data, as this combination increases the chances of examining the details compared to the single-sensor classification method and achieves more reliable information.
format	article
author	Shilan Felegari Alireza Sharifi Kamran Moravej Muhammad Amin Ahmad Golchin Anselme Muzirafuti Aqil Tariq Na Zhao
author_facet	Shilan Felegari Alireza Sharifi Kamran Moravej Muhammad Amin Ahmad Golchin Anselme Muzirafuti Aqil Tariq Na Zhao
author_sort	Shilan Felegari
title	Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
title_short	Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
title_full	Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
title_fullStr	Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
title_full_unstemmed	Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
title_sort	integration of sentinel 1 and sentinel 2 satellite images for crop mapping
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9c889c0d2df44aba9df56ec1e45e9bc3
work_keys_str_mv	AT shilanfelegari integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT alirezasharifi integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT kamranmoravej integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT muhammadamin integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT ahmadgolchin integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT anselmemuzirafuti integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT aqiltariq integrationofsentinel1andsentinel2satelliteimagesforcropmapping AT nazhao integrationofsentinel1andsentinel2satelliteimagesforcropmapping
_version_	1718437160927887360

Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping

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