Towards sustainable agriculture in Iran using a machine learning-driven crop mapping framework - Iman Khosravi (European Journal of Remote Sensing)
The Ministry of Agriculture-Jihad (MAJ) and the Iranian Space Agency (ISA) aim to accurately estimate the cultivated area of strategic crops and evaluate their annual yield through meticulous crop mapping. However, Iran lacks a comprehensive, integrated approach using remote sensing and machine learning for this purpose. This study addressed this gap by developing a versatile, user-friendly crop mapping framework for Iran, utilizing Landsat-8 time series data and classical machine learning algorithms. Marvdasht in Fars province was selected as the pilot area due to its high diversity of agricultural crop types and its status as a significant agricultural hub in Iran. Furthermore, the most widely used and flexible methods available in crop mapping studies such as decision tree (DT), random forest (RF), rotation forest (RoF), support vector machine (SVM), and dynamic time warping (DTW) were used in this study. The results showed that the DTW and RF methods outperformed others, achieving approximately 96% accuracy and improving overall accuracy by 8% in creating the crop map for the pilot area. Additionally, this study demonstrated the effectiveness of Landsat-8 bands 2 to 5 along with the normalized difference vegetation index (NDVI) in reliably identifying all crops in the region. The proposed framework shows promise for significantly advancing crop mapping practices in Iran.