Luling Liu

Solar photovoltaic (PV) power generation, known for its affordability and environmental benefits, is a key component of the global energy supply. However, the lack of comprehensive, timely, and precise global PV datasets has limited spatial analysis of PV potential. We developed a new method to identify PV panels globally, producing an annual 20-meter resolution dataset for 2019–2022. This dataset offers unprecedented detail and accuracy for future research and policy-making. A two-stage PV classification framework was built using U-Net and positive unlabelled learning with random forest (PUL-RF). U-Net first recognizes PVs from sub-meter Google Earth images, expanding positive PV samples for the second stage, where PUL-RF classifies Sentinel-2 images on a large scale. The dataset was evaluated with IoU and F1-Score metrics, achieving over 90% accuracy. Compared to existing datasets, it provides better precision and spatial detail, showing global PV growth of over 60% between 2019 and 2022, with developing countries leading the increase.

Reliable electricity supply is critical to achieving Sustainable Development Goal 7 (SDG7), but it is difficult to measure and assess, especially in less developed countries and regions. Nighttime light (NTL) remote sensing is particularly well-suited to monitor artificial light. However, the daily NTL from NASA's Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) Black Marble product fluctuates due to angular effect, making it challenging to capture NTL anomalies accurately. This study proposed a new method to overcome angular effect and detect low-value anomalies, serving the assessment of electricity supply reliability. We identified the optimal number of viewing zenith angle (VZA) groups, determining the magnitude of the angular effect. Subsequently, we obtained the characteristics and typical thresholds of the NTL variations at different observation angles, resulting from the observation geometry and the spatial structure of the surface. Finally, the spatial structure information of the angular effect is integrated to monitor the NTL anomalies over the time series. Anomalies with actual power outages were validated at 200 random points in Johannesburg, South Africa. The results show that the method effectively overcomes the impact of angular effect and successfully detects the power outage signals with an overall accuracy of 89.41%, precision of 83.78%, and recall of 84.64%. Moreover, the electricity anomaly rate in Johannesburg increased from 36% to 51% between 2020 and 2023, with elevated areas mainly in densely residential areas and commercial centers. The proposed method can be used to assess electricity supply reliability at regional and global scales.

The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R²: 0.93) and the city scale (R²: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales.