Tese
Imagens de satélite para predição espaço-temporal da produtividade de milho e soja em diferentes escalas geográficas
Fecha
2019-09-10Autor
Schwalbert, Raí Augusto
Institución
Resumen
As global food security issues become increasingly challenging, reliable estimates of crop yields are
becoming more imperative than ever for the scientific community. Today, with greater ease of
accessing remote sensing data from satellite-embedded sensors, this source of information has become
very promising for developing crop yield forecast models. Nevertheless, the use of such models is still
limited in most operational efforts to monitor crop yield at different geographic scales. In general,
satellite-based yield forecast models can be evaluated by considering three aspects: i) the accuracy of
the predictions; ii) the date when the yield forecast is released in relation to the crop harvest date; and
iii) the spatial scale of the forecasting unit, (e.g. country, state, county, field, etc.). The main objectives
of this study were: i) to develop a complete model based on satellite images capable of predicting corn
(in the US Corn Belt) and soybean (in the state of Rio Grande do Sul – Brazil) in county and
municipality levels, respectively; ii) evaluate the performance of the model after the inclusion of
weather variables along with satellite derived vegetation indices; iii) test different machine learning
algorithms to predict yield at the regional level; and iv) evaluate the generalization capacity of
predictive models developed at field level when applied to fields in different regions from which they
were parameterized. The main results were: i) satellite-based predictive models and weather variables
can anticipate corn yield by up to 122 days (approximately 16 days prior to the first USDA/NASS
state-level corn yield report) with an mean absolute error of less than 1 Mg ha-1, and soybean yield by
up to 70 days with an mean absolute error of 0.42 Mg ha-1; ii) air temperature, canopy surface
temperature and vapor pressure deficit improved model performance in relation to models based only
on vegetation indices (NDVI and EVI); iii) the Long Short Term Memory Neural Network algorithm
performed better compared to the other algorithms tested (e.g. random forest and ordinary least
squares regression); and iv) the models parameterized at field level presented limited generalization
capacity outside the limits where they were adjusted, but similarities in the data distribution used for
model parameterization can provide guidance on how they can be extrapolated. The results presented
in this study have potential to assist farmers and policy makers in the decision making process. Future
studies on this topic should explore the fusion of mechanistic (process-based) with empirical models in
order to increase the spatio-temporal limits of predictability and make models less dependent on third
party data.