Evapotranspiration measurements in pasture, crops, and native Brazilian Cerrado based on UAV-borne multispectral sensor.

Autor: de Lima, Gabriella Santos Arruda, Ferreira, Manuel Eduardo, Sales, Jepherson Correia, de Souza Passos, Joelson, Maggiotto, Selma Regina, Madari, Beata Emoke, de Melo Carvalho, Márcia Thaís, de Almeida Machado, Pedro Luiz Oliveira
Předmět:
Zdroj: Environmental Monitoring & Assessment; Nov2024, Vol. 196 Issue 11, p1-21, 21p
Abstrakt: In Brazil, agriculture consumes most of the available freshwater, especially in the Cerrado biome, where the rain cycle is marked by long periods of drought. This study, conducted at the Brazilian Agricultural Research Corporation (Embrapa) Research Corporation unit in Santo Antônio de Goiás, Goiás, Brazil, estimated evapotranspiration (ET) in different crops and soil cover. Using multispectral unmanned aerial vehicle (UAV) images, Sentinel satellite data, weather station information, and towers employing the eddy covariance method, we applied the "Simple Algorithm for Evapotranspiration Retrieving" (SAFER) to calculate ET in common bean, pasture, and semideciduous seasonal forest areas. The results showed a good agreement between UAV and satellite data, with R2 = 0.84, also validated with flow towers by the eddy covariance method. UAV-based ET was observed to correspond well to tower (EC) during full vegetative development of beans but is underestimated at the beginning of planting and in the final periods of plant senescence, due to the influence of soil or straw cover. These findings contribute to a better understanding of water dynamics in the system and to enhancing sustainable agricultural practices. This method, adapted for multispectral aerial imaging, can be applied flexibly and on-demand, in different contexts and ground cover. The study highlights the importance of integrated agricultural practices for better management of water resources and preservation of the Cerrado in balance with cultivation areas. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index