Efficiency Assessment for Crop Classification Using Multi-Sensor Data in Google Earth Engine
Keywords:
Machine learning, Crop classification, Sentinel-1, Sentinel-2, Google Earth EngineAbstract
Accurate mapping of agricultural lands and crop distribution are critical for food security, sustainable development, and policymakers. In this research, agricultural crops were classified using multi-sensor images of Sentinel-1 and Sentinel-2 in Rahim Yar Khan district of Pakistan. The study used the cloud computing platform, Google Earth Engine (GEE), and compared the classification performance of the Random Forest (RF) algorithm using the Sentinel-1 (VV, HV, and HV+VV), Sentinel-2, and integration of the datasets. Ground truth information developed through field surveys and high-resolution images were used as reference samples for training and validation. The fusion of Sentinel-1 and Sentinel-2 data increase the features for better classification of crop types. Post-processing procedures guaranteed that maps were visually clear and devoid of noise, allowing for precise crop mapping and land cover land use categorization. The classification findings showed that crop pixels were effectively classified, with high accuracy for classes including sugarcane, cotton, rice and water bodies. The RF classifier with the fused data produced the highest accuracy (overall accuracy 93%, and Kappa coefficient 90%), followed by the multispectral Sentinel-2 (89 %), Sentinal-1 VV+VH (72%), Sentinel – 1 VH (66 %), and Sentinel – 1 VV (62%). The study highlights the importance of data integration to increase the classification accuracy of major crops (sugarcane, cotton, and rice) in the region. While some classes demonstrated exceptional classification accuracy, others (such as Orchard) indicated need for improvement for further refinement in categorization procedures and approaches. Overall, the study provides useful insights into the use of multi-sensor remote sensing data in agricultural monitoring and decision-making processes.
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