Satellite-based solar power data is becoming more and more important because of its continuous temporal and spatial availability. However, its reliability can be enhanced through quality control and calibration against ground-based measurement data. Here, a holistic methodology is employed for the adaptation of satellite-based data for estimating solar energy. For the purpose, high-quality ground-based measurement data and satellite-based datasets are assessed across 12 sites in three small islands located in the Indian Ocean. Initially, both datasets go through a rigorous quality control process. A quantitative analysis of irradiance and insolation data is then conducted. Eventually, site adaptation of satellite-based data is performed using bias removal technique and statistical analysis of datasets. A set of seven statistical performance indicators is used to support the assessment. Analysis of datasets shows that adaptation of peak values should be performed separately. Results showed that despite the small surface areas of the islands studied, a spatial variation of insolation can be depicted. A temporal variation of insolation is also noted with a peak in the summer and low insolation levels in winter. Peak irradiance values tend to exceed solar constant for all sites. Variations of peak irradiance can only be noticed in ground-based measurement data. While insolation levels are comparable in the summer season for all the sites, insolation levels in the winter season are higher in the sites with lower latitudes. Calibration factors for peak irradiance, monthly and annual average irradiance as well as yearly insolation are presented.