Using bigdata from remote sensing for evapotranspiration prediction and water management in irrigated coffee

Abstract

Agriculture is the world's most important land-use activity. Agriculture not only affects land cover change, but also has a profound impact on the sustainable development of the social economy, food security, water and the environment, environmental services, climate change and the carbon cycle. Thus, Brazil is the world's largest coffee producer, with a 32% share of world production, with Arabica coffee accounting for most of the production. To stay on top of production, the monitoring of several variables assumes importance for a continuous production gain, we highlight the evapotranspiration (ET), important for the management of different crops. In this work, we aim to evaluate different methods of estimating evapotranspiration by remote sensing in different locations in Arabica and Robusta coffees. First, we give an overview of commonly applied evapotranspiration (ET) models using remote sensing data to provide an overview of regional-scale evapotranspiration estimation from satellite data. Generally, these models vary widely in inputs, key assumptions, and accuracy of results. This review summarizes the basic theories of methods for estimating solar (shortwave), thermal (longwave), and evapotranspiration (latent heat flux) radiation from both earth and satellite, which are inherently complex to measure on a large scale. In papers 2 and 3 we apply two different methods of estimating evapotranspiration by remote sensing (METRIC and SAFER). The joint applications of the METRIC and SAFER algorithms allowed to understand the variation of ET in the irrigated coffee field with high spatial (30 and 10 m) and temporal (16 and 5 days) resolution, and from this variation in ET, to understand how to better manage irrigation in this crop. Even so, the good accuracy of the METRIC model was found in the estimation of evapotranspiration, thus providing important information for data input in the water balance for the determination of irrigation projects.