Summary
This study demonstrates that hyperspectral (PRISMA) and multispectral (Sentinel-2) satellite imagery can effectively predict nutrient concentrations in cereal grain crops before harvest, offering a potentially cost-effective and spatially scalable alternative to laboratory-based chemical analysis. Using field samples from Italy and remote sensing data collected at key crop development stages, the authors developed predictive models achieving moderate to good performance (R² = 0.51–0.73), with Sentinel-2 paired with regression modelling generally outperforming simpler spectral indices. This approach addresses a practical bottleneck in monitoring crop nutrient quality at scale, which is relevant to addressing micronutrient malnutrition globally.
UK applicability
The methodology could potentially be adapted to UK arable systems, though model calibration would be needed for British soil types, climate conditions, and crop varieties. The use of Sentinel-2 imagery is particularly applicable as it provides free, routinely acquired data over the UK, though validation on UK farm conditions would be essential before operational deployment.
Key measures
Coefficient of determination (R²) and root mean square error (RMSE) for predictions of eight macronutrients and micronutrients (Ca, Fe, K, Mg, N, P, S, Zn) across four crop types using two-band vegetation indices (TBVIs) and partial least squares regression (PLSR) modelling approaches
Outcomes reported
The study developed and compared two remote sensing approaches (PRISMA and Sentinel-2 imagery combined with two-band vegetation indices or partial least squares regression) to predict nutrient concentrations (calcium, iron, potassium, magnesium, nitrogen, phosphorus, sulphur, and zinc) in corn, rice, soybean, and wheat grains before harvest. Model performance varied by crop and nutrient, with coefficient of determination values ranging from 0.51 to 0.73 for the best predictions.
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