Generating High Spatial and Temporal Surface Albedo with Multispectral-Wavemix and Temporal-Shift Heatmaps

Surface albedo is a key variable influencing ground-reflected solar irradiance, which is a vital factor in boosting the energy gains of bifacial solar installations. Therefore, surface albedo is crucial towards estimating photovoltaic power generation of both bifacial and tilted solar installations. Varying across daylight hours, seasons, and locations, surface albedo is assumed to be constant across time by various models. The lack of granular temporal observations is a major challenge to the modeling of intra-day albedo variability. Though satellite observations of surface reflectance, useful for estimating surface albedo, provide wide spatial coverage, they too lack temporal granularity. Therefore, this paper considers a novel approach to temporal downscaling with imaging time series of satellite-sensed surface reflectance and limited high-temporal ground observations from surface radiation (SURFRAD) monitoring stations. Aimed at increasing information density for learning temporal patterns from an image series and using visual redundancy within such imagery for temporal downscaling, we introduce temporally shifted heatmaps as an advantageous approach over Gramian Angular Field (GAF)-based image time series. Further, we propose Multispectral-WaveMix, a derivative of the mixer-based computer vision architecture, as a high-performance model to harness image time series for surface albedo forecasting applications. Multispectral-WaveMix models intra-day variations in surface albedo on a 1 min scale. The framework combines satellite-sensed multispectral surface reflectance imagery at a 30 m scale from Landsat and Sentinel-2A and 2B satellites and granular ground observations from SURFRAD surface radiation monitoring sites as image time series for image-to-image translation between remote-sensed imagery and ground observations. The proposed model, with temporally shifted heatmaps and Multispectral-WaveMix, was benchmarked against predictions from models image-to-image MLP-Mix, MLP-Mix, and Standard MLP. Model predictions were also contrasted against ground observations from the monitoring sites and predictions from the National Solar Radiation Database (NSRDB). The Multispectral-WaveMix outperformed other models with a Cauchy loss of 0.00524, a signal-to-noise ratio (SNR) of 72.569, and a structural similarity index (SSIM) of 0.999, demonstrating the high potential of such modeling approaches for generating granular time series. Additional experiments were also conducted to explore the potential of the trained model as a domain-specific pre-trained alternative for the temporal modeling of unseen locations. As bifacial solar installations gain dominance to fulfill the increasing demand for renewables, our proposed framework provides a hybrid modeling approach to build models with ground observations and satellite imagery for intra-day surface albedo monitoring and hence for intra-day energy gain modeling and bifacial deployment planning.