New spectral indicator Potato Productivity Index based on Sentinel-2 data to improve potato yield prediction: a machine learning approach. Issue 9 (3rd May 2021)

• Record Type: Journal Article
• Authors: Gómez, Diego; Salvador, Pablo; Sanz, Julia; Casanova, José Luis
• Is Part Of: International journal of remote sensing. Volume 42:Issue 9(2021)
• Page Start: 3426
• Page End: 3444
• Publication Date: 2021-05-03
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2020.1871102
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: The potato ( Solanum tuberosum L.) is one of the most important food crops and is consumed by more than a billion people worldwide. Despite the fact that crop growth models are widely used to optimize crop management and agricultural decision-making, they fail to provide spatial information, whereas satellite-based methods do, and in a cost-effective manner. In order to improve satellite-based crop yield prediction models, we developed a methodology based on Sentinel-2 (S2) data using Machine Learning (ML) techniques. In addition, we included and compared the commonly used Normalized Difference Vegetation Index (NDVI) to a newly developed spectral indicator, the Potato Productivity Index (PPI). Our results showed the capacity of our ML models to predict potato yield with great accuracy (coefficient of determination (R 2 ) = 0.77 and Root Mean Square Error (RMSE) = 15.42%), using S2 bands 2, 3, 4, 5, 8A, and 9, as well as the PPI index. This index aims to provide information concerning plant photosynthetic activity as well as its water stress. The Random Forest (RF) model using the S2 bands and the PPI index as predictors obtained better predictive results (R 2 = 0.77, RMSE = 15.42%) for potato crops than using S2 bands and NDVI (R 2 = 0.66, RMSE = 16.88%). This study demonstrates the suitability of our models to predict potato yields in the region studied and improves on a previous approach presented by the same authors in terms of model interpretability and inter-annual variability.