Lysimetric calibration and uncertainty analysis of empirical parameters of the SEBAL algorithm in subtropical climate

• Grant number: 16/15342-2
• Support type: Scholarships in Brazil - Post-Doctorate
• Effective date (Start): February 01, 2017
• Effective date (End): December 10, 2020
• Field of knowledge: Agronomical Sciences - Agricultural Engineering - Soil and Water Engineering
• Principal researcher: Marcos Vinícius Folegatti
• Grantee: Wagner Wolff
• Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated scholarship(s): 17/09708-7 - Rainfall mapping from cellular commercial microwave links: parameters calibration and uncertainty analysis in subtropical climate, BE.EP.PD

Abstract

Understanding the spatio-temporal variation of evapotranspiration (ET) over irrigated agricultural areas is important for helps to water management. Many remote sensing methods have been developed to estimate ET, among them the most widely used is the Surface Energy Balance Algorithm for Land (SEBAL). However, SEBAL was developed for particular satellite sensors and regions, in which functions and empirical parameters within their algorithm are corresponding to these regions and sensors. Therefore, the aim of this study is going to be calibrate the SEBAL algorithm for images of Landsat 8, through calibration by lysimetric data in subtropical climate. The study will be carried out in two subtropical climate regions in Brazil. The first in the city of Piracicaba in the São Paulo state and the second in the city of Dourados in the Mato Grosso do Sul state. The images Landsat 8 and the meteorological data are matching for the year 2013 to 2018 on a monthly scale for images and of 15 minutes for the meteorological data. Utilizing the SEBAL algorithm is going to be estimated all the components of the energy balance and consequently the ET. Thus, the calibration is going to be done using the maximum likelihood method for residuals adjusted to Gaussian distribution. The stochastic optimization method Particle Swarm Optimisation is going to be used to the numerical maximization of log-likelihood function. The new parameters empirical is going to be obtained in uncertainty levels and is going to be used to compose the update of SEBAL algorithm for images Landsat 8 in subtropical climate. (AU)