Estimation of daily average downward shortwave radiation from MODIS data using principal components regression method: Fars province case study

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Abstract

Downward shortwave radiation is a key quantity in the land-atmosphere interaction. Since the moderate resolution imaging spectroradiometer data has a coarse temporal resolution, which is not suitable for estimating daily average radiation, many efforts have been undertaken to estimate instantaneous solar radiation using moderate resolution imaging spectroradiometer data. In this study, the principal components analysis technique was applied to capture the information of moderate resolution imaging spectroradiometer bands, extraterrestrial radiation, aerosol optical depth, and atmospheric water vapour. A regression model based on the principal components was used to estimate daily average shortwave radiation for ten synoptic stations in the Fars province, Iran, for the period 2009-2012. The Durbin-Watson statistic and autocorrelation function of the residuals of the fitted principal components regression model indicated that the residuals were serially independent. The results indicated that the fitted principal components regression models accounted for about 86-96% of total variance of the observed shortwave radiation values and the root mean square error was about 0.9-2.04 MJ m−2 d−1. Also, the results indicated that the model accuracy decreased as the aerosol optical depth increased and extraterrestrial radiation was the most important predictor variable among all.

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International Agrophysics

The Journal of Institute of Agrophysics of Polish Academy of Sciences

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SCImago Journal Rank (SJR) 2018: 0.399
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