Urban heat islands have garnered significant attention due to their potential impact on human life. Previous studies on urban heat islands have focused on characterizing temporal and spatial variations over longer periods of time. In this study, we investigated the urban heat island (UHI) in Birmingham from September 2013 to August 2014 using higher temporal resolution SEVIRI satellite surface temperature data along with data from the Birmingham Urban Climate Laboratory (BUCL) meteorological station and the UK Meteorological Office meteorological station. Our aim was to characterize the diurnal variations in the surface urban heat island intensity (SUHII) and canopy urban heat island intensity (CUHII) and to explore their relationship under the influence of three factors (day/nighttime, season, and wind speed) using regression analysis. Our findings reveal that SUHII and CUHII exhibit relatively stable patterns at night but vary significantly during the day with opposite diurnal trends. In addition, SUHII and CUHII were more variable in spring and summer but less variable in winter. During the nighttime, SUHII represents CUHII with high confidence, especially during spring and summer, but less so during the cold season. In addition, SUHII represents CUHII with greater confidence under low-wind conditions. This study deepens our understanding of the diurnal dynamics of urban heat islands and the influence of atmospheric conditions on the relationship between surface and canopy heat islands in urban areas. The results of this study can be used for heat island studies in cities that lack high-precision observation networks and to guide sustainable urban development.

The Tarim River Basin (TRB) faces significant ecological challenges due to global warming, making it essential to understand the changes in the climates of its sub-basins for effective management. With this aim, data from national meteorological stations, ERA5_Land, and climate indices from 1961 to 2021 were used to analyze the temperature and precipitation variations in the TRB and its sub-basins and to assess their climate sensitivity. Our results showed that (1) the annual mean temperature increased by 0.2 °C/10a and precipitation increased by 7.1 mm/10a between 1961 and 2021. Moreover, precipitation trends varied significantly among the sub-basins, with that in the Aksu River Basin increasing the most (12.9 mm/10a) and that in the Cherchen River Basin increasing the least (1.9 mm/10a). Moreover, ERA5_Land data accurately reproduced the spatiotemporal patterns of temperature (correlation 0.92) and precipitation (correlation 0.72) in the TRB. (2) Empirical Orthogonal Function analysis identified the northern sections of the Kaidu, Weigan, and Yerqiang river basins as centers of temperature sensitivity and the western part of the Kaidu and Cherchen River Basin as the center of precipitation sensitivity. (3) Global warming is closely correlated with sub-basin temperature (correlation above 0.5) but weakly correlated with precipitation (correlation 0.2~0.5). TRB temperatures were found to have a positive correlation with AMO, especially in the Hotan, Kashgar, and Aksu river basins, and a negative correlation with AO and NAO, particularly in the Keriya and Hotan river basins. Precipitation correlations between the climate indices were complex and varied across the different basins.

Grassland ecosystems are an important component of global terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, it is important to study the carbon dioxide (CO₂) process in the Middle Tien Shan grassland ecosystem, which can be regarded as a typical representative of the mountain grasslands in Xinjiang. Eddy covariance (EC) and the global carbon fluxes dataset (GCFD) were utilized to continuously monitor the Middle Tien Shan grassland ecosystem in Xinjiang throughout the 2018 growing season. The findings revealed notable daily and monthly fluctuations in net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). On a daily basis, there was net absorption of CO₂ during the day and net emission during the night. The grassland acted as a carbon sink from 6:00 to 18:00 and as a carbon source for the remaining hours of the day. On a monthly scale, June and July served as carbon sinks, whereas the other months acted as carbon sources. The accumulated NEE, GPP, and Reco during the growing season were −329.49 g C m⁻², 779.04 g C m⁻², and 449.55 g C m⁻², respectively. On the half-hourly and daily scales, soil temperature (Ts) was the main contributor to CO₂ fluxes and had the greatest influence on the variations in CO₂ fluxes. Additionally, air temperature (Ta) showed a strong correlation with CO₂ fluxes. The grassland ecosystems exhibited the strongest CO₂ uptake, reaching its peak at soil temperatures of 25 °C. Moreover, as the air temperatures rose above 15 °C, there was a gradual decrease in NEE, while CO₂ uptake increased. The applicability of GCFD data is good in the grassland ecosystem of the Middle Tien Shan Mountains, with correlations of 0.59, 0.81, and 0.73 for NEE, GPP, and Reco, respectively, compared to field observations. In terms of remote sensing spatial distribution, the Middle Tien Shan grassland ecosystem exhibits a carbon sink phenomenon.

Intensive economic and human activities present challenges to the carbon storage capacity of terrestrial ecosystems, particularly in arid regions that are sensitive to climate change and ecologically fragile. Therefore, accurately estimating and simulating future changes in carbon stocks on the northern slope economic belt of Tianshan Mountains (NSEBTM) holds great significance for maintaining ecosystem stability, achieving high-quality development of the economic belt, and realizing the goal of “carbon neutrality” by 2050. This study examines the spatiotemporal evolution characteristics of the NSEBTM carbon stocks in arid regions from 1990 to 2050, utilizing a combination of multi-source data and integrating the Patch-generating Land use Simulation (PLUS) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models. Additionally, an attribution analysis of carbon stock changes is conducted by leveraging land use data. The findings demonstrate that (1) the NSEBTM predominantly consists of underutilized land, accounting for more than 60% of the total land area in the NSEBTM. Unused land, grassland, and water bodies exhibit a declining trend over time, while other forms of land use demonstrate an increasing trend. (2) Grassland serves as the primary reservoir for carbon storage in the NSEBTM, with grassland degradation being the leading cause of carbon loss amounting to 102.35 t over the past three decades. (3) Under the ecological conservation scenario for 2050 compared to the natural development scenario, there was a net increase in carbon storage by 12.34 t; however, under the economic development scenario compared to the natural development scenario, there was a decrease in carbon storage by 25.88 t. By quantitatively evaluating the land use change in the NSEBTM and its impact on carbon storage in the past and projected for the next 30 years, this paper provides scientific references and precise data support for the territorial and spatial decision making of the NSEBTM, thereby facilitating the achievement of “carbon neutrality” goals.

Wind profile light detection and ranging (LiDAR) is an important tool for observing features within the atmospheric boundary layer. Observations of the wind field and boundary layer height from coherent Doppler wind LiDARs (CDWLs) under sandy and dusty weather conditions were evaluated using observations from two CDWLs and one GTS radio sounding located at the northern edge of the Tibetan plateau from 1 May to 30 August 2021. The results showed that CDWL has good applicability in reproducing wind fields in dust, precipitation, and in clear-sky conditions, and that it is superior to the v wind field for real measurements of the u wind fields. In terms of the planetary boundary layer height (PBLH), the validity of the inversion of PBLH in dusty weather was higher than that under clear-sky conditions. It was found that the PBLH retrieved by the CDWL at 20:00 (BJT) was better than that at 08:00 (BJT). The diurnal variation amplitude of the PBLH before the occurrence of a sandstorm was larger than the diurnal variation amplitude of the PBLH occurring during a sandstorm.

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems.