Optimal Use of Multi-Sensor Data for Precision Agriculture: Sentinel-1 and Sentinel-2 Fusion in Crop Classification

Effective land monitoring and land use classification are critical for proper management of resources especially in heterogeneous and climate diverse areas. Consequently, this study seeks to test the hypothesis that the integration of Sentinel-1 radar and Sentinel-2 optical data enhances the degree of discrimination of crops in major farming areas of Morocco from the years 2020 to 2022. A three-dimensional coordinate system was established which included a series of processing stages that started with cloud masking, scaling of reflectance, and radar optical integration. At each year’s end, temporal averages and composites were created using selected Sentinel-2 spectral bands B2, B3, B4, B8, B11, B12 and Sentinel-1 VV & VH dual polarization channels. Ground truth samples from four major crops; Baley, Crop, D. Wheat and S. Wheat were used as the training set in a Random Forest classifier. The results for the three agricultural zones indicated high overall accuracies greater than 80% for each year, with the application of a combination of radar and optical data sets contributing greatly towards the ability to differentiate the crops located in cloud folded and spectral overlapping areas. Many classes had high consumer accuracy (≥70%) levels, yet several crops, like D. Wheat, had poor producer accuracy, possibly due to the uneven distribution of ground truth data sets. The small amount of Kappa coefficients between 0.50 and 0.60 also indicate moderate agreement similar to the validation data and thus more accurate ground truth and class targeted feature detection is needed. This study emphasizes the relevance notes of the multi-sensor data fusion technology for crop monitoring and also landcover classification which contributes to precision farming and resources management. Future work will focus on including temporal characteristics as well as state-of-the-art machine learning techniques to solve class balance issues and improve classification performance.