Improving Crop Classification Accuracy with Integrated Sentinel-1 and Sentinel-2 Data: a Case Study of Barley and Wheat

Crop classification plays a crucial role in ensuring food security, agricultural policy development, and effective land management. Remote sensing data, particularly Sentinel-1 and Sentinel-2 data, has been widely used for crop mapping and classification in cloudy regions due to their high temporal and spatial resolution. This study aimed to enhance the classification accuracy of grain crops, specifically barley and wheat, by integrating Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral instrument (MSI) data. The study utilized two classification models, random forest (RF) and classification and regression trees (CART), to classify the grain crops based on the integrated data. The results showed an overall accuracy (OA) of 93%, and a Kappa coefficient (K) of 0.896 for RF, and an OA of 89.15% and K of 0.84 for the CART classifier. The integration of both radar and optical data has the potential to improve the accuracy of crop classification compared to using a single-sensor classification technique. The significance of this study is that it demonstrates the effectiveness of integrating radar and optical data to improve crop classification accuracy. These findings can be used to support crop management, environmental monitoring, and policy development, particularly in areas with cloud cover or limited optical data. The study's implications are particularly relevant in the context of global food security, where accurate crop classification is essential for monitoring crop health and yield estimation. Concisely, this study provides a useful approach for crop classification using Sentinel-1 and Sentinel-2 data integration, which can be employed to support sustainable agriculture and food security initiatives.