An Intercomparison of Two Satellite-Based Hyperspectral Imagery (PRISMA & EnMAP) for Agricultural Mapping: Potential of these sensors to produce hyperspectral time-series essential for tracking crop phenology and enhancing crop type mapping

In recent decades, space-borne hyperspectral sensors have demonstrated significant potential for agricultural monitoring by providing rich spectral information, improved feasibility, and cost-effectiveness compared to multispectral satellite imagery. In this study, we investigated the consistency of two hyperspectral satellite sensors, PRISMA and EnMAP, for agricultural mapping during the 2025 growing season in the Meknes region: one of the most fertile and productive areas for cereals and vegetables at the national level of Morocco. The primary objective was to conduct a comparative analysis of the two datasets and perform a binary classification (crop vs. no-crop) to support land use monitoring, inform decision-making, and enable advanced crop type mapping.

Our methodology included a correlation analysis of reflectance values across the visible to near-infrared (VNIR) and shortwave infrared (SWIR) ranges, as well as the evaluation of NDVI indices using two methods: band averaging and hyperspectral NDVI (hNDVI). Classification was performed using three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and CatBoost—based on 16 optimal hyperspectral narrow-bands (i.e., 427,  535, 567, 714, 775, 805, 839, 913, 977, 1175, 1246, 1295, 1717, 2077, 2191, 2343 nm) from PRISMA and EnMAP that best capture the variability of vegetation biophysical and biochemical characteristics.

Results demonstrated high Pearson correlation coefficients between the two sensors, with r=0.93 in the VNIR and r=0.91 in the SWIR ranges. NDVI comparison also showed strong consistency results, with correlations of r=0.84 using the hNDVI method and r=0.85 using band averaging. The utilization of optimal hyperspectral narrow-bands achieved superior classification accuracies of 99.95% with PRISMA and 99.65% with EnMAP, with SVM outperforming other algorithms, followed by RF and CatBoost. Moreover, an Explainable Artificial Intelligence (XAI) based analysis indicated that bands in the NIR and SWIR regions were the most critical features driving these high classification performances.

These findings highlight the consistency and complementarity of PRISMA and EnMAP for agricultural monitoring. They also demonstrate the potential of these sensors to produce hyperspectral time-series essential for tracking crop phenology and enhancing crop type mapping, thereby overcoming the constraints posed by limited revisit intervals in current imaging spectroscopy missions.