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Advances in Meteorology
Volume 2017 (2017), Article ID 9314801, 14 pages
https://doi.org/10.1155/2017/9314801
Research Article

Reliability of MODIS Evapotranspiration Products for Heterogeneous Dry Forest: A Study Case of Caatinga

1Laboratório de Sensoriamento Remoto e Geoprocessamento, Universidade Federal de Pernambuco, 50670901 Recife, PE, Brazil
2Empresa Brasileira de Pesquisa Agropecuária, Centro de Pesquisa Agropecuária do Trópico Semi-Árido, 56302970 Petrolina, PE, Brazil
3Spatial Sciences Laboratory, Texas A&M University, College Station, TX 77845, USA

Correspondence should be addressed to Rodrigo de Queiroga Miranda; moc.liamg@adnarimq.ogirdor

Received 23 September 2016; Accepted 29 November 2016; Published 24 January 2017

Academic Editor: Minha Choi

Copyright © 2017 Rodrigo de Queiroga Miranda et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Evapotranspiration (ET) is normally considered as the sum of all water that evaporates from the soil and transpires from plants. However, accurately estimating ET from complex landscapes can be difficult because of its high spatial heterogeneity and diversity of driver factors, which make extrapolation of data from a point to a larger area quite inaccurate. In this paper, we hypothesize that MODIS products can be of use to estimate ET in areas of Caatinga vegetation, the hydrology of which has not been adequately studied. The experiment was conducted in a preserved level area of Caatinga in which meteorological and water flux measures were taken throughout 2012 by eddy covariance. Evapotranspiration estimates from eddy covariance were compared with remotely sensed evapotranspiration estimates from MOD16A2 and SAFER products. Correlations were performed at monthly, 8-day, and daily scales; and produced values of monthly scale = 0.92, 8-day scale = 0.88, and daily scale = 0.85 for the SAFER algorithm. Monthly MOD16A2 data produced a value of , and they may be useful because they are free, downloadable, and easy to use by researchers and governments.