A good working knowledge of photosynthetically active radiation (PAR) is of vital requirement for determining the terrestrial photosynthesis, primary productivity calculation, ecosystem-atmosphere carbon dioxide, plant physiology, biomass production, natural illumination in greenhouses, radiation climate, remote sensing of vegetation, and radiation regimes of plant canopy, photosynthesis, productivity models of vegetation, etc. However, routine measurement of PAR is not available in most location of interest across the globe. During the past 77 years in order to estimate PAR on hourly, daily and monthly mean basis, several empirical models have been developed for numerous locations globally. As a result, numerous input parameters have been utilized and different functional forms applied. This study was aim at classifying and reviewing the empirical models employed for estimating PAR across the globe. The empirical models so far utilized were classified into ten main categories and presented base on the input parameters applied. The models were further reclassified into numerous main sub-classes (groups) and finally presented according to their developing year. In general, 757 empirical models, 62 functional forms and 32 groups were reported in literature for estimating PAR across the globe. The empirical models utilized were equally compared with models developed using different artificial neural network (ANN); and the result revealed that ANN models are more suitable for estimating PAR across the globe. Thus, this review would provide solar energy researchers with input parameters and functional forms that have been widely used to up to date, and recognizing their importance in estimating PAR globally. Citation: Nwokolo, S. C., and Amadi, S. O. (2018). A Global Review of Empirical Models for Estimating Photosynthetically Active Radiation. Trends in Renewable Energy, 4(2), 236-327. DOI: 10.17737/tre.2018.4.2.007
