Optimizing precision irrigation management by assimilating in situ observations in remote sensing models: research and algorithm development

• Grant number: 20/05781-4
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: November 01, 2021
• End date: July 31, 2022
• Field of knowledge: Agronomical Sciences - Agricultural Engineering - Soil and Water Engineering
• Principal Investigator: Wagner Wolff
• Grantee: Wagner Wolff
• Company: Wagner Wolff ME
• CNAE: Atividades de apoio à agricultura ; Pesquisa e desenvolvimento experimental em ciências físicas e naturais
• City: Piracicaba
• Associated researchers: Diego de Liz ; Fabio Ricardo Marin ; Marcos Vinícius Folegatti ; Rubens Duarte Coelho
• Associated scholarship(s): 21/12245-4 - Optimizing precision irrigation management by assimilating in situ observations in remote sensing models: research and algorithm development, BP.TT; 21/12007-6 - Optimizing precision irrigation management by assimilating in situ observations in remote sensing models: research and algorithm development, BP.PIPE

Abstract

Prescribing the water consumption of crops through the spatio-temporal variation of evapotranspiration (ET) over irrigated agricultural areas is important for helping manage water resources management and increase productivity. Many remote sensing (SR) models have been developed to estimate ET, the most widely used is the Surface Energy Balance Algorithm for Land (SEBAL). However, SEBAL was developed for orbital sensors and particular regions, in which functions and empirical parameters employed by it correspond to these regions and sensors, thus generating great uncertainties regarding the applicability of this model in other regions. In situ measures can be used to assimilate data in SEBAL and thus reduce such uncertainty. Therefore, the objective of this project will be to calibrate the SEBAL model for both orbital (onboard of satellites) and suborbital (onboard of unmanned aerial vehicles) sensors and thus develop an assimilation algorithm at Web cloud for prescribing irrigation depths. Initially, the project will be carried out in a testing area located in the city of Piracicaba in the State of São Paulo. Using the SEBAL model, all the components of the energy balance and, consequently, the ET will be estimated. Lysimmetric data, soil moisture and biometric measurements will be used as in situ reference values and by subtracting the values estimated by SEBAL the residues will be obtained. Therefore, the calibration will be done maximizing the quality of adjustment of these residues. The Particle Swarm Optimization stochastic optimization method will be used for the numerical maximization of the objective function. The new empirical parameters will be obtained in uncertainty levels and will be used in the retrieval algorithm of irrigation depth by considering the variability spatio-temporal of RS and the precision of in situ measurements. Thus, the product generated will provide more assertive information for handling precision irrigation. The consumer market for this product is not only associated with irrigators and irrigation management companies but also with producers who want to know the dynamics of water consumption in their crops and correlate this with the productivity forecast. (AU)