Document Type : Research Paper
Authors
1
Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran
2
Bahareh Bahmanabadi, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran
3
Agricultural Engineering Research Department, Khuzestan Agricultural and Natural Resources Research and Education Center, (AREEO), Ahwaz, Iran
10.22059/ijswr.2025.402830.670012
Abstract
Accurate mapping of crop types and their spatial distribution, along with estimating cultivated areas, plays a critical role in water resource planning and agricultural land management in arid and semi‐arid regions. In this study, field surveys were first conducted to collect precise geographic coordinates of selected crop fields, including wheat, maize, sesame, and alfalfa, and these reference points were subsequently integrated into the Google Earth Engine environment for alignment with remote sensing data. Sentinel-2 imagery was processed through atmospheric correction and cloud masking, while Sentinel-1 data underwent geometric correction and speckle filtering. A set of spectral indices derived from Sentinel-2, together with backscatter intensity and polarization ratio features extracted from Sentinel-1, was generated and integrated to enhance the separability of crop types with similar phenological characteristics. The collected reference samples were divided into training (70%) and validation (30%) subsets, and three classification algorithms, Random Forest, Support Vector Machine, and Extreme Gradient Boosting, were applied to produce the final crop type map. Model performance was evaluated using the confusion matrix, overall accuracy, and the Kappa coefficient. The results indicated that the Random Forest algorithm achieved the highest performance, with an overall accuracy of 96% and a Kappa coefficient of 0.93, demonstrating superior discrimination of crop types, particularly wheat. The Extreme Gradient Boosting model ranked second with an accuracy of 89%, while the Support Vector Machine exhibited lower performance. Comparison of the estimated cultivated areas with official agricultural statistics further confirmed the high reliability of the Random Forest results.
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