27 October 2014 Net radiation estimated by remote sensing in Cerrado areas in the Upper Paraguay River Basin
Marcos Alves Fausto, Nadja Gomes Machado, José de Souza Nogueira, Marcelo Sacardi Biudes
Author Affiliations +
Abstract
The Cerrado is a heterogeneous landscape which is shrinking due to deforestation, giving rise to managed ecosystems. The land cover changes alter net radiation (Rn), which determines the quantity of available energy to the energy balance partition. The objectives of this study were (1) to determine the spatial pattern of the vegetation indices, albedo, and land surface temperature (LST) and (2) to evaluate the Rn estimated by Landsat 5 Thematic Mapper (TM) images over Cerrado areas in the Upper Paraguay River Basin. We estimated the vegetation indices, albedo, LST, and Rn of five selected vegetation types. The values estimated by Landsat 5 TM images had seasonal variations with higher values of the vegetation indices and lower values of the albedo and the LST during the wet season. The riparian and Cerrado strictu sensu had higher values of vegetation indices and lower albedo and LST than grasslands. The Rn estimated by Landsat 5 TM images was highly correlated with the measured Rn. The Rn had a seasonal pattern, following the solar radiation, with higher values during the wet season and varied spatially with higher values in the riparian forest and Cerrado strictu sensu and lower in the grasslands. This study showed the applicability of the Landsat 5 TM images to estimate Rn, which can help to understand the heterogeneity in the study area.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2014/$25.00 © 2014 SPIE
Marcos Alves Fausto, Nadja Gomes Machado, José de Souza Nogueira, and Marcelo Sacardi Biudes "Net radiation estimated by remote sensing in Cerrado areas in the Upper Paraguay River Basin," Journal of Applied Remote Sensing 8(1), 083541 (27 October 2014). https://doi.org/10.1117/1.JRS.8.083541
Published: 27 October 2014
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Cited by 25 scholarly publications.
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KEYWORDS
Radon

Vegetation

Earth observing sensors

Landsat

Solar radiation

Remote sensing

Error analysis

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