RT info:eu-repo/semantics/article
T1 Estimation of potato yield using satellite data at a municipal level: A machine learning approach
A1 Salvador González, Pablo
A1 Gómez, Diego
A1 Sanz Justo, María Julia
A1 Casanova Roque, José Luis
K1 Física
K1 Teledetección
K1 Potato yield
K1 Meteorological data
K1 Satellite imagery
K1 Municipal level
K1 Rendimiento de patatas
K1 Datos meteorológicos
K1 Imágenes de satélite
K1 Ámbito municipal
K1 22 Física
K1 3103.04 Protección de Los Cultivos
AB Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.
PB MDPI
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/59075
UL https://uvadoc.uva.es/handle/10324/59075
LA eng
NO ISPRS Int. J. Geo-Inf, 2020, vol.9, n. 6, 343
NO Producción Científica
DS UVaDOC
RD 18-nov-2024