Optimizing crop monitoring: mapping cultivation stages and types with sentinel-1/2 and random forest algorithm.

Nabil, Mohsen; Farg, Eslam; Afify, Nagwan M.; Arafat, Sayed M.

Crop-type mapping is essential for agricultural monitoring, improving crop yield models, and supporting sustainable land management. Studying crop phenology, which includes the timing from germination to maturity, is equally important for precise agricultural practices. However, maps detailing crop phenology are scarce. This study addresses this gap by mapping both crop types and cultivation time, offering valuable insights for agriculture applications. The research integrates Sentinel-1/2 data, NDVI phenology curves, and machine learning (ML) algorithms through Google Earth Engine to map crops in El-Behera Governorate, Egypt. By stratifying the region into different soil types, it accounts for various agro-environmental conditions. Field visits in July 2022 provided crop-type information, and Sentinel-2 NDVI curves were used to determine cultivation time. The random forest (RF) classifier, known for its accuracy, was employed, achieving an overall accuracy (OA) of 97.4% for crop type and 95.6% for cultivation time, with high Kappa coefficients. Feature importance analysis highlighted the significance of specific spectral bands, particularly in the Red Edge region, for classification accuracy. Hyperparameter tuning across different zones demonstrated the need for customized tuning. This study enhances understanding of agro-environmental variability's impact on crop classification and shows the potential of the RF algorithm in advancing crop mapping, suggesting further studies on how shifts in cultivation time could affect crop yield.

PLANT phenology; RANDOM forest algorithms; AGRICULTURE; CROP yields; SOIL classification

International Journal of Remote Sensing, 2025, Vol 46, Issue 1, p273

0143-1161

Academic Journal

10.1080/01431161.2024.2413025