Jingwen Ni

Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving R² values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% R² improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.

Monitoring autumn vegetation dynamics in alpine regions is crucial for managing local livestock, understanding regional productivity, and assessing the responses of alpine regions to climate change. However, remote sensing-based vegetation monitoring is significantly affected by snowfall. The impact of autumn snowfall, particularly when vegetation has not fully entered dormancy, has been largely overlooked. To demonstrate the uncertainties caused by autumn snowfall in remote sensing-based vegetation monitoring, we analyzed 16 short-term snowfall events in the Qinghai–Tibet Plateau. We employed a synthetic difference-in-differences estimation framework and conducted simulated experiments to isolate the impact of snowfall from other factors, revealing its effects on vegetation indices (VIs) and autumn phenology estimation. Our findings indicate that autumn snowfall notably affects commonly used VIs and their associated phenology estimates. Modified VIs (i.e., Normalized Difference Infrared Index (NDII), Phenology Index (PI), Normalized Difference Phenology Index (NDPI), and Normalized Difference Greenness Index (NDGI)) revealed greater resilience to snowfall compared to conventional VIs (i.e., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) in phenology estimation. Areas with remaining green vegetation in autumn showed more pronounced numerical changes in VIs due to snowfall. Furthermore, the impact of autumn snowfall closely correlated with underlying vegetation types. Forested areas experienced less impact from snowfall compared to grass- and shrub-dominated regions. Earlier snowfall onset and increased snowfall frequency further exacerbated deviations in estimated phenology caused by snowfall. This study highlights the significant impact of autumn snowfall on remote sensing-based vegetation monitoring and provides a scientific basis for accurate vegetation studies in high-altitude regions.

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP.

Snow cover is a sensitive indicator of climate change. Normalized Difference Snow Index (NDSI) acquired from optical remote sensing data is usually used for monitoring snow cover, but the existing data are limited in spatiotemporal resolution. Here, we compared two blending strategies, Blend-then-Index (BI) and Index-then-Blend (IB), for generating high spatiotemporal resolution NDSI (daily, 20 m), and designed two groups of experiments (simulated and real) under three different snow cover periods over the Tibetan Plateau (TP). The Flexible Spatiotemporal DAta Fusion (FSDAF) model was used as the fusion model. MODIS (daily, 500 m) and Sentinel-2A/B (2–5 days, 20 m) data were used as the inputs. The accuracy of the fused NDSI was evaluated from both spectral (root mean square error [RMSE], correlation coefficient [ R ], and average difference [AD]) and spatial (Robert's edge [Edge], and local binary pattern [LBP]) dimensions. Our results showed that the IB strategy produced more accurate NDSI results, with lower RMSE, higher R , and AD closer to zero compared to the BI strategy. In addition, there was no obvious difference in terms of texture between the two fusion strategies. Generally, the IB strategy is a better choice for generating a high spatiotemporal resolution NDSI through the FSDAF model under different snow cover periods on the TP. This study can provide effective guidelines for producing better high-resolution NDSI time series on the TP.