Comparative analysis of drought detection using satellite imagery with deep learning models

Drought detection is essential for effective environmental and agricultural planning. In this study, satellite imagery from the USGS Landsat8 dataset, focusing on RGB bands, was used to develop a deep learning-based model for identifying drought conditions. To classify the data, images with few cows were labeled as Class 0, while those with more cows were categorized as Class 1. Drought conditions are detected if an image falls under Class 0, whereas Class 1 indicates no drought. Four deep learning models including EfficientNetB0, MobileNetV2, VGG16, and a custom CNN were trained and evaluated. By analyzing performance metrics such as accuracy, precision, recall, and F1-score, EfficientNetB0 was found to be the most effective. The study demonstrates the potential of deep learning for satellite-based drought detection, offering insights for environmental monitoring and resource management.