Modelling Two Sugarcane Agro-Industrial Yields Using Sentinel/Landsat Time-Series Data and Their Spatial Validation at Different Scales in Costa Rica

Abstract

Sugarcane production is a relevant socioeconomic activity in Costa Rica that requires tools to improve decision-making, particularly with the advancement of agronomic management using remote sensing (RS) techniques. Some contributions have evaluated sugarcane yield with RS methods, but some gaps remain, such as the lack of operational models for predicting yields and joint estimation with sugar content. Our study is a contribution to this topic that aims to apply an empirical, operational, and robust method to estimate sugarcane yield (SCY) and sugar content (SC) through the combination of field variables, climatic data, and RS vegetation indices (VIs) extracted from Sentinel-2 and Landsat-8 imagery in a cooperative in Costa Rica for four sugarcane harvest cycles (2017–2018 to 2020–2021). Based on linear regression models, four approaches using different VIs were evaluated to obtain the best models to improve the RMSE results and to validate them (using the harvest cycle of 2021–2022) at two management scales: farm and plot. Our results show that the historical yield average, the maximum historical yield, and the growing cycle start were essential factors in estimating SCY and the former variable for SC. For SCY, the most explicative VI was the Simple Ratio (SR), whereas, for SC, it was the Ratio Vegetation Index (RVI). Adding VIs from different months was essential to obtain the phenological variability of sugarcane, being the most common results September, December and January. In SC estimation, precipitation (in May and December) was a clear explicatory variable combined mainly with RVI, whereas in SCY, it was less explanatory. In SCY, RMSE showed values around 8.0 t·ha−1, a clear improvement from 12.9 t·ha−1, which is the average obtained in previous works, whereas in SC, it displayed values below 4.0 kg·t−1. Finally, in SCY, the best validation result was obtained at the plot scale (RMSE of 7.7 t·ha−1), but this outcome was not verified in the case of SC validation because the RMSE was above 4.0 kg·t−1. In conclusion, our operational models try to represent a step forward in using RS techniques to improve sugarcane management at the farm and plot scales in Costa Rica.

Keywords

• sugarcane yield
• sugar content estimation
• Sentinel-2 and Landsat-8
• vegetation indices
• multivariate linear regression
• spatial variability