Paper
19 October 2005 Evapotranspiration estimation in the Brazil using NDVI data
Luciana Rossato, Regina C. S. Alvala, Nelson Jesus Ferreira, Javier Tomasella
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Abstract
The purpose of this study is to analyze the monthly mean spatial and temporal distribution of evapotranspiration (ET) during the 1981-2000 period in Brazil using remotely sensed data. The methodology involves the use of a relationship between ET and Normalized Difference Vegetation Index (NDVI). ET was estimated for the main Brazilian biomes through the Penman-Monteith method using climatological data from 194 Brazilian meteorological stations during the 1961-1990 period. NDVI data for the July 1981 to July 2000 period was obtained from Advanced Very High Resolution Radiometer (AVHRR) sensor on board the NOAA satellite. A relatively high correlation coefficient between ET and NDVI (r=0.81) was found, showing a near linear relationship involving these variables. Also, the monthly mean ET over Brazil was estimated using NDVI data. The results showed that the ET rate in the Amazon region is not well defined because the maximum values occur after the rainy season, while for the Northeast Brazil, the highest ET values occur in according to period of rainy season. The annual cycle of ET is most defined in the Central region, with maximum values occurring in January to May period and minimum in September. In the South and Southeast regions, the annual cycle ET does not change very much. Finally, this study suggests that NDVI is an important variable for indirectly monitoring ET over large areas, thus with great potential for agronomical and climatic applications.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luciana Rossato, Regina C. S. Alvala, Nelson Jesus Ferreira, and Javier Tomasella "Evapotranspiration estimation in the Brazil using NDVI data", Proc. SPIE 5976, Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 59761G (19 October 2005); https://doi.org/10.1117/12.626793
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Cited by 11 scholarly publications.
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KEYWORDS
Vegetation

Data modeling

Climatology

Satellites

Sensors

Meteorology

Biological research

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