Paddy Crop Monitoring Using Landsat 8 and Sentinel Data: A Case Study in Kandi Mandal of Telangana, India

Abstract

Monitoring paddy crop using remote sensing data has become increasingly prevalent due to its ability to provide timely and spatially comprehensive information. This study focuses on the utilization of Landsat 8 and Sentinel satellite imagery for monitoring paddy crops in the Kandi Mandal of Telangana, India. The primary objective is to assess the feasibility and effectiveness of these satellite datasets in capturing key agricultural parameters such as vegetation indices and land use changes throughout the crop growth cycle. The methodology involves preprocessing of satellite imagery to derive spectral indices like Normalized Difference Vegetation Index (NDVI). Ground truth data collected through field surveys are used for validation and accuracy assessment. Temporal analysis of satellite data over multiple growing seasons provides insights into crop health, growth stages, and phenological changes. Results indicate that combining Landsat 8 and Sentinel data enhances the accuracy of crop monitoring compared to using a single dataset alone. The study demonstrates the potential of remote sensing technologies in supporting precision agriculture practices and informing decision-making processes for sustainable crop management.