Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learning

Abstract

Accurate and timely Land Use and Land Cover (LULC) classification is crucial for effective agricultural planning and decision-making, particularly in regions like the Nile Delta, Egypt, where LULC is rapidly changing. This study addresses the challenge of classifying small, fragmented agricultural fields and road networks by leveraging the synergistic potential of Sentinel-1 and Sentinel-2 data, combined with Machine Learning (ML) and Deep Learning (DL) techniques. Unlike previous studies that often rely on Sentinel-2 or image-based DL, this research introduces a novel approach: a pixel-based ML classification using both Sentinel-1 and Sentinel-2 data. This strategy allowed to effectively capture the spectral and textural information crucial for distinguishing small features, which are often missed by traditional methods. Using distinct temporal datasets and validated ground truth annotations, we trained and tested several ML and DL models, including XGB, Support Vector Classifier, K-Nearest Neighbor, Decision Tree, Random Forest, and LSTM. XGB achieved the highest overall accuracy (94.4 %), whereas Random Forest produced the most accurate map with independent data (91.4 % Overall Accuracy). Integrating Sentinel-1 with Sentinel-2 data improved classification accuracy by 1–7 % compared to using Sentinel-2 alone. Notably, the pixel-based ML approach yielded reliable predictions for small road areas and agricultural fields, which are often challenging to map accurately. This research demonstrates the effectiveness of integrating multi-sensor data with advanced ML/DL for improved LULC classification, particularly for small feature mapping, thus providing critical information for enhanced agricultural planning and decision-making in the Nile Delta.