Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island

This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset is the daily incoming shortwave (SIS) product from CM SAF SARAH-E. spatial resolution 0.05° × and temporal coverage 2007 to 2016. first five years (2007–2011) are used as training data, remaining (2012–2016) test data in model. Datasets were detrended, de-seasonalized, normalized before being applied multiple (MLR), principal component (PCR), stepwise (SR), partial least squares (PLSR), which perform mapping. statistical analysis using MAE, MSE, RMSE shows that PCR model had smallest compared other three seems better SSR over Reunion Island. Although provides results, its quite large.