A Method to Determine the Optimal Period for Field-Scale Yield Prediction Using Sentinel-2 Vegetation Indices

Roberto Colonna (), Nicola Genzano (), Emanuele Ciancia, Carolina Filizzola, Costanza Fiorentino, Paola D’Antonio and Valerio Tramutoli
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Roberto Colonna: School of Engineering, University of Basilicata, 85100 Potenza, Italy
Nicola Genzano: Department ABC (Architecture, Built Environment and Construction Engineering), Politecnico di Milano, Via Ponzio 31, 20133 Milano, Italy
Emanuele Ciancia: Satellite Application Centre (SAC), Space Technologies and Applications Centre (STAC), 85100 Potenza, Italy
Carolina Filizzola: Satellite Application Centre (SAC), Space Technologies and Applications Centre (STAC), 85100 Potenza, Italy
Costanza Fiorentino: School of Agricultural, Forest, Food, and Environmental Sciences (SAFE), University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
Paola D’Antonio: School of Agricultural, Forest, Food, and Environmental Sciences (SAFE), University of Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
Valerio Tramutoli: School of Engineering, University of Basilicata, 85100 Potenza, Italy

Land, 2024, vol. 13, issue 11, 1-18

Abstract: This study proposes a method for determining the optimal period for crop yield prediction using Sentinel-2 Vegetation Index (VI) measurements. The method operates at the single-field scale to minimize the influence of external factors, such as soil type, topography, microclimate variations, and agricultural practices, which can significantly affect yield predictions. By analyzing historical VI data, the method identifies the best time window for yield prediction for specific crops and fields. It allows adjustments for different space–time intervals, crop types, cloud probability thresholds, and variable time composites. As a practical example, this method is applied to a wheat field in the Po River Valley, Italy, using NDVI data to illustrate how the approach can be implemented. Although applied in this specific context, the method is exportable and can be adapted to various agricultural settings. A key feature of the approach is its ability to classify variable-length periods, leveraging historical Sentinel-2 VI compositions to identify the optimal window for yield prediction. If applied in regions with frequent cloud cover, the method can also identify the most effective cloud probability threshold for improving prediction accuracy. This approach provides a tool for enhancing yield forecasting over fragmented agricultural landscapes.

Keywords: remote sensing agriculture; crop monitoring techniques; field-level forecasting; phenological analysis; high-resolution vegetation data; S2 imagery applications; ideal timing acquisition; NDVI; clear pixel procedure; agricultural productivity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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