Iranian Remote Sensing and GIS Society / Shahid Beheshti University Iranian Journal of Remote Sensing and GIS 2008-5966 16 3 2024 06 21 Crop Mapping Using a Combination of Sentinel-1 and 2 Images in Ardabil Province Crop Mapping Using a Combination of Sentinel-1 and 2 Images in Ardabil Province 25 46 103095 10.48308/gisj.2023.103095 FA Bahar Asadi Msc, Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran Ali Shamsoddini Associate professor, Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran 0000-0002-4559-7563 Journal Article 2022 05 10 <strong>Introduction</strong>: Identifying and mapping crops provides important information to agricultural lands management and cultivation area estimation of crops. Optical and radar images are valuable resources for classifiying agricultural land. Features deriverd from optical images contain information about the reflectance signatures of various products, while radar images provides information about the structural characteristics and distribution mechanisms of products. The combination of these two sources can create a complementary dataset with a significant number of spectral, texture and polarized temporal features for the classification of agricultural land<br /><strong>Material and methods</strong>: This study aims to explore the significance of red edge bands for the segregation of crops such aswheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using the random forest method and support vector machine. To conduct the analysis, a time series of Sentinel-1 and 2 images 2019 in the northwest region of Ardabil was retrieved from the Google Earth Engine (GEE) platform. The study evaluates the effectiveness of spectral and temporal information, plant indices and backscatter information on the crop mapping by examining different combinations of bands. Through the random forest feature selection method, essential features are identified and utilized as inputs for both the random forest and support vector machine classifiers.<br /><strong>Results and discussion</strong>: The random forest provided the most favorable outcomes across all scenarios. The results revealed that incorporating red edge wavelengths and red edge-based vegetation indices proved more beneficial than other bands and vegetation indices for differentiating between barley, beans, broad beans, and flax. The most optimal outcome among various feature combinations was associated with the time series of spectral features from Sentinel-2 images combined with the time series of Sentinel-1 images, resulting in an overall accuracy of 84.67% and a kappa coefficient of 82.31%.  Furthermore, the results demonstrated that red edge bands and red edge-based vegetation indices effectively distinguish between different types of crops<br /><strong>Conclusion</strong>: It is recommended to carefully consider the selection of specific spectral bands to achieve higher accuracy in separation of crops. It is important to highlight that combining radar and optical images consistently yields superior results compared to classification methods based on a single sensor, leading to increased classification information. <strong>Introduction</strong>: Identifying and mapping crops provides important information to agricultural lands management and cultivation area estimation of crops. Optical and radar images are valuable resources for classifiying agricultural land. Features deriverd from optical images contain information about the reflectance signatures of various products, while radar images provides information about the structural characteristics and distribution mechanisms of products. The combination of these two sources can create a complementary dataset with a significant number of spectral, texture and polarized temporal features for the classification of agricultural land<br /><strong>Material and methods</strong>: This study aims to explore the significance of red edge bands for the segregation of crops such aswheat, barley, alfalfa, beans, broad beans, flax, corn, sugar beet and potatoes using the random forest method and support vector machine. To conduct the analysis, a time series of Sentinel-1 and 2 images 2019 in the northwest region of Ardabil was retrieved from the Google Earth Engine (GEE) platform. The study evaluates the effectiveness of spectral and temporal information, plant indices and backscatter information on the crop mapping by examining different combinations of bands. Through the random forest feature selection method, essential features are identified and utilized as inputs for both the random forest and support vector machine classifiers.<br /><strong>Results and discussion</strong>: The random forest provided the most favorable outcomes across all scenarios. The results revealed that incorporating red edge wavelengths and red edge-based vegetation indices proved more beneficial than other bands and vegetation indices for differentiating between barley, beans, broad beans, and flax. The most optimal outcome among various feature combinations was associated with the time series of spectral features from Sentinel-2 images combined with the time series of Sentinel-1 images, resulting in an overall accuracy of 84.67% and a kappa coefficient of 82.31%.  Furthermore, the results demonstrated that red edge bands and red edge-based vegetation indices effectively distinguish between different types of crops<br /><strong>Conclusion</strong>: It is recommended to carefully consider the selection of specific spectral bands to achieve higher accuracy in separation of crops. It is important to highlight that combining radar and optical images consistently yields superior results compared to classification methods based on a single sensor, leading to increased classification information. https://gisj.sbu.ac.ir/article_103095_b23b14467d5d2726c475332e3192894c.pdf