Remote Sensing Applications and Decision Support

Simplified triangle method for estimating evaporative fraction over soybean crops

[+] Author Affiliations
Daniela Fernanda Silva-Fuzzo, Jansle Vieira Rocha

University of Campinas, School of Agricultural Engineering—UNICAMP, FEAGRI—Avenue Candido Rondon, 501, 13083-875 Campinas SP, Brazil

J. Appl. Remote Sens. 10(4), 046027 (Dec 19, 2016). doi:10.1117/1.JRS.10.046027
History: Received March 29, 2016; Accepted November 17, 2016
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Abstract.  Accurate estimates are emerging with technological advances in remote sensing, and the triangle method has demonstrated to be a useful tool for the estimation of evaporative fraction (EF). The purpose of this study was to estimate the EF using the triangle method at the regional level. We used data from the Moderate Resolution Imaging Spectroradiometer orbital sensor, referring to indices of surface temperature and vegetation index for a 10-year period (2002/2003 to 2011/2012) of cropping seasons in the state of Paraná, Brazil. The triangle method has shown considerable results for the EF, and the validation of the estimates, as compared to observed data of climatological water balance, showed values >0.8 for modified “d” of Wilmott and R2 values between 0.6 and 0.7 for some counties. The errors were low for all years analyzed, and the test showed that the estimated data are very close to the observed data. Based on statistical validation, we can say that the triangle method is a consistent tool, is useful as it uses only images of remote sensing as variables, and can provide support for monitoring large-scale agroclimatic, specially for countries of great territorial dimensions, such as Brazil, which lacks a more dense network of meteorological ground stations, i.e., the country does not appear to cover a large field for data.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Daniela Fernanda Silva-Fuzzo and Jansle Vieira Rocha
"Simplified triangle method for estimating evaporative fraction over soybean crops", J. Appl. Remote Sens. 10(4), 046027 (Dec 19, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.046027


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