Haze is harmful to human health besides degrades the human welfare and environment. Haze information needs to be quickly disseminated to public so that necessary measures can be promptly taken to prevent further losses. Satellite remote sensing offers a better alternative over conventional methods in measuring haze concentration due to its capability to record atmospheric data continuously, spatially and cost-effectively. This study explores the capability of a scene-based technique called the haze optimized transformation (HOT) in quantifying haze. Landsat-8 data with hazy, moderate and clear conditions were initially identified and downloaded from USGS website. Bands 2 and 4 are used to derive HOT images from these data. Haze in-situ measurements in API (Air Pollution Index) obtained from the Malaysian Department of Environment are coupled with the HOT images where the relationship between HOT and API values are then determined. Regression analysis is used to determine the relationship between HOT and API where the strength of the correlation is indicated by coefficient of determination (R2). The accuracy of the API map is eventually assessed using visual analysis and root-mean-square error (RMSE). The results show that there is a weak relationship between HOT and API that led to the quite low accuracy in the API map obtained. This is likely due to the quite lengthy gap between the API measurement and satellite overpass time. © 2015 Muhammad Fahmi Razali et al.