MY-ALBEDO: an alternative baseline radiation network / Siti Aekbal Salleh … [et al.] - Salleh, Siti Aekbal; Abd.Latif, Zulkiflee; Wan Mohd, Wan Mohd Naim; Chan, Andy (Research Innovation Business Unit, 2014)
The emergence of various climate campaigns and the establishment of numerous climate variables have been the backbone of this research framework. The launch of the satellite Landsat 8 to capture climatic variables of land processes strengthened the realization of this research. One of the Essential Climate Variables (ECV) called albedo was reported to be insufficiently recorded and documented in term of its spatial scale and also temporal resolution. Thus the intensity of this variable towards the simulation of climate change effects needs to be quantified. The global climate and energy research community baseline radiation network often used for validation purpose and for those area where this network has not established yet and verification is highly depends on the onsite observation. Thus, once again the limitation and constraint to the remote sensing data sources is the need of simultaneous observation with satellite overpass. In addition, a comprehensive data archive is freely available online enabling the earth’s scientist to extract historical remote sensing data in which simultaneous onsite observation is impossible. Therefore, an alternative validation method is required to extend the usability of these data. The objectives of this study is to formulate a model and factorise albedo of urban features based on selected variables continuous Air Quality Monitoring station (CAQMs) and to verify the reliability of the model with respect to the in-situ measurement and remotely sensed albedo. The novelty of this research lies in the establishment of statistical regression model to depict land surface albedo based on the climatic variables which widely available from continuous Air Quality monitoring stations (CAQMs) and Automatic Weather stations (AWS). This model alleviates remotely sensed land surface albedo verification process as the spatial distributions of CAQMs and AWs were well distributed and the continuous observation allow for more flexible temporal aspects. This model makes the validation of historical data especially for the area where the baseline radiation network is not established. Hence, it extends the variation of analyses involving the use of historical data and capitalizes on the data archive. The model developed requires very common parameter and allows the utilization of existing monitoring stations or even handheld or portable instruments. No sophisticated software or expensive instruments are needed for the establishment of baseline radiation network. Thus, cost incurred can be very minimal comparatively. Therefore, it may promote better usefulness as well as gaining popularity among the earth scientist. The independent variables were induced based on literature, existing theories and problems and further deduced from observations using Multipple Linear Regression (MLR), the model was quantified using Mean Area Error (MAE), Relative Standard Error (RSE) and Root Mean Square (RMSE). The bootstrap methods are employed to improve the accuracy of statistical estimations. Bootstrap standard error of the ambient temperature coefficients is equal to the original standard error (0.001), highlight the adequacy samples population. This model utilizes remotely sensed land surface albedo and in-situ albedo for verification processes. Spatial distribution of Continuous Air Quality Monitoring station (CAQMs) was used for validating archive remote sending images for this study as its continuous data capturing ability allows for temporal flexibility in validation process.