4.6 Article

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

期刊

APPLIED SCIENCES-BASEL
卷 11, 期 21, 页码 -

出版社

MDPI
DOI: 10.3390/app112110104

关键词

Sentinel 1 and 2; Copernicus Sentinels; crop classification; food security; agricultural monitoring; remote sensing; data analysis; SAR; random forest

资金

  1. National Natural Science Foundation of China [42071374]

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This study combines radar data and optical images to identify crop types in the Tarom region. By using Sentinel 1 and Sentinel 2 images, a classification map was created to achieve high accuracy and reliable information in crop identification.
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.

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