Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.

Net primary productivity (NPP), as an indicator of ecological functioning, plays an important role in regional and global carbon cycles. Although many studies have estimated the NPP of vegetation on the Qinghai Plateau (QP), the existing NPP datasets over the QP are either of low spatial resolution or limited-duration time-series. These shortcomings restrict our ability to explore the spatial distribution and long-term trends of NPP at a finer scale. To address this gap, we present a new monthly NPP dataset (QP NPP30) at a high spatial resolution (30 m) over the QP for the period 1987-2021. We constructed this dataset using the Carnegie-Ames-Stanford-Approach (CASA) model and multi-source data, including reconstructed normalized difference vegetation index (NDVI) data, reanalysis data, land cover, and other ancillary data. To reconstruct the NDVI, a harmonic regression model based on the Google Earth Engine (GEE) was applied to the NDVI time series data. Statistical analysis of QP NPP30 showed that the NPP in the QP has increased over the past 35 years (0.92 gC/m² /yr ). Furthermore, we found that NPP is concentrated in June, July, and August, accounting for approximately 73% of the annual total. To validate our dataset, we compared it with measured NPP and with the MODIS NPP product (MOD-NPP). Our results demonstrated that QP NPP30 has similar spatial patterns to MOD-NPP but offers richer spatial detail. Specifically, QP NPP30 has a higher accuracy than MOD-NPP, by comparing with the measured data (r = 0.695, RMSE = 132.823 gC/m² /yr for QP NPP30; r = 0.328, RMSE = 158.586 gC/m² /yr for MOD-NPP).

The sparsity regularization based on the L₁ norm can significantly stabilize the solution of the ill-posed sparsity inversion problem, e.g., azimuth super-resolution of radar forward-looking imaging, which can effectively suppress the noise and reduce the blurry effect of the convolution kernel. In practice, the total variation (TV) and TV-sparsity (TVS) regularizations based on the L₁ norm are widely adopted in solving the ill-posed problem. Generally, however, the existence of bias is ignored, which is incomplete in theory. This paper places emphasis on analyzing the partially biased property of the L₁ norm. On this basis, we derive the partially bias-corrected solution of TVS and TV, which improves the rigor of the theory. Lastly, two groups of experimental results reflect that the proposed methods with partial bias correction can preserve higher quality than those without bias correction. The proposed methods not only distinguish the adjacent targets, suppress the noise, and preserve the shape and size of targets in visual terms. Its improvement of Peak Signal-to-Noise Ratio, Structure-Similarity, and Sum-Squared-Errors assessment indexes are overall 2.15%, 1.88%, and 4.14%, respectively. As such, we confirm the theoretical rigor and practical feasibility of the partially bias-corrected solution with sparsity regularization based on the L₁ norm.

This study analyses the impact of Land Use Land Cover (LULC) dynamics on the development of Urban Heat Islands (UHI) in a diverse topographic region of Nowshera district, Pakistan. The rapid increase in the study region is causing a rise in land surface temperatures, and subsequently bring alterations in the local climate. The growth of cities and changes in LULC have modified the surface of land and near-surface atmospheric temperature and changed the thermal properties of the gray infrastructure (built-up areas), causing warming compared to the area of non-urbanized surroundings, contributing to the formation of the Urban Heat Island effect. For this study, Landsat 5 (1990), Landsat 7 (2005), and Landsat 8 (2020) were used. LST, LULC, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBaI), and Urban Index (UI) were derived to assess the influence of LULC changes on UHI. The analysis revealed a 12.44% increase in built-up areas between 1990 and 2020, driven by population growth and urbanization, which led to a 2°C rise in mean LST, with maximum temperatures increasing from 46°C to 48°C. In contrast, vegetation cover, water bodies, and barren land declined by 6.1%, 1.3%, and 5%, respectively, reflecting trade-offs between urban expansion and natural resources. Higher LST values were concentrated around main urban centers and gray infrastructure, while green infrastructure and water bodies experienced lower LST. A negative correlation was observed between NDVI and LST, whereas LST showed positive correlations with NDBI, NDBaI, and UI. The urban and rural temperature range widened with LULC change during the study period. These changes in LST were mainly associated with changes in LULC. This study will be helpful in highlighting the importance of vegetation areas, especially in urban areas, in minimizing the increasing impacts of UHI.