Deep Learning-Based Monitoring of Photovoltaic Power Plant Expansion and Assessment of Albedo-Driven Shortwave Energy Changes

Abstract

Large-scale photovoltaic (PV) power plants are expanding rapidly across arid desert regions, driving land-cover transformations and associated changes in surface properties. Most existing remote sensing studies focus primarily on mapping PV distribution, while quantitative assessments of PV-induced land-cover transitions and associated surface property changes remain limited. This study develops an integrated assessment framework that combines time-series Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) prior parameters. Three semantic segmentation models are compared for PV extraction, with independent generalization validation conducted over desert areas in Xinjiang. Constrained by coarse-resolution BRDF products, 10 m broadband white-sky albedo (WSA) is retrieved. The results show that SegFormer outperforms the other two models for PV identification. From 2021 to 2025, the PV-covered area of the Talatan region expanded from 160.35 km2 to 303.26 km2. For the newly converted PV area, surface albedo decreased by 0.0391 and 0.0310 during 2021–2023 and 2023–2025, respectively, corresponding to local albedo-driven shortwave energy changes of 27.12 W·m−2 and 23.55 W·m−2. Consistent variation patterns are observed in the Xinjiang validation site. This study offers an integrated framework for characterizing PV expansion-induced land-cover changes and associated surface property variations, while providing an observation-based assessment of local shortwave energy variations related to surface albedo changes in arid regions.