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Net radiation is a key component of the energy balance, whose estimation accuracy has an impact on energy flux estimates from satellite data. In typical remote sensing evapotranspiration (ET) algorithms, the outgoing shortwave and longwave components of net radiation are obtained from remote sensing data, while the incoming shortwave (R-S(down arrow)) and longwave (R-L(down arrow)) components are typically estimated from weather data using empirical equations. This study evaluates the accuracy of empirical equations commonly used in remote sensing ET algorithms for estimating R-S(down arrow) and R-L(down arrow) radiation. Evaluation is carried out through comparison of estimates and observations at five sites that represent different climatic regions from humid to arid. Results reveal (1) both R-S(down arrow) and R-L(down arrow) estimates from all evaluated equations well correlate with observations (R-2 >= 0.92), (2) R-S(down arrow) estimating equations tend to overestimate, especially at higher values, (3) R-L(down arrow) estimating equations tend to give more biased values in arid and semi-arid regions, (4) a model that parameterizes the diffuse component of radiation using two clearness indices and a simple model that assumes a linear increase of atmospheric transmissivity with elevation give better R-S(down arrow) estimates, and (5) mean relative absolute errors in the net radiation (R-n) estimates caused by the use of R-S(down arrow) and R-L(down arrow) estimating equations varies from 10% to 22%. This study suggests that R-n estimates using recommended incoming radiation estimating equations could improve ET estimates.