Modelos para la estimación del rendimiento de la caña de azúcar en Costa Rica con datos de campo e índices de vegetación

Bryan Alemán-Montes, Pere Serra, Alaitz Zabala

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

3 Citas (Scopus)

Resumen

Remote sensing offers important inputs for sugarcane yield estimation, since its temporal and spatial approaches allow monitoring the phenological cycle of the crop. The objective of this research was to apply an operational method for the estimation of sugarcane yield and sugar content through the combination of field variables with vegetation indices, calculated with the satellite sensors on board Sentinel-2 and Landsat-8 in a cooperative from Costa Rica. In addition, historical harvest data and start months of phenological cycle were used to estimate sugarcane yield and sugar content per ton using multiple linear regressions. The integration of historical data and Simple Ratio (SR) vegetation index, calculated in different steps of the phenological cycle (in the months of September, December and January), allowed us to obtain an estimation model of sugarcane yield (tons of sugarcane per hectare) with a regression coefficient (R2) of 0.64 and a RMSE of 8.0 tons/ha. While for sugar content (kilograms of refined sugar per ton) we obtained a R2 of 0.59 integrating historical variables and the vegetation indexes SR and Green Normalized Difference Vegetation Index (GNDVI); in this case the RMSE was 4.9 kg/tons. Ultimately, this operational method of yield estimation provides tools for decision making before, during and after the harvest stage.
Título traducido de la contribuciónModels for the estimation of sugarcane yield in Costa Rica with field data and vegetation indices
Idioma originalEspañol
Páginas (desde-hasta)1-13
Número de páginas13
PublicaciónRevista de Teledetección
Volumen2023
N.º61
DOI
EstadoPublicada - 30 ene 2023

Palabras clave

  • Landsat-8
  • Sentinel-2
  • linear regression
  • sugarcane
  • vegetation indexes

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