Nighttime Land Surface Temperature Research Articles

A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021

Abstract. Land surface temperature (LST) is a key variable for monitoring and evaluating global long-term climate change. However, existing satellite-based twice-daily LST products only date back to 2000, which makes it difficult to obtain robust long-term temperature variations. In this study, we developed the first global historical twice-daily LST dataset (GT-LST), with a spatial resolution of 0.05∘, using Advanced Very High Resolution Radiometer (AVHRR) Level-1b Global Area Coverage (GAC) data from 1981 to 2021. The GT-LST product was generated using four main processes: (1) GAC data reading, calibration, and preprocessing using open-source Python libraries; (2) cloud detection using the AVHRR-Phase I algorithm; (3) land surface emissivity estimation using an improved method considering annual land cover changes; (4) LST retrieval based on a nonlinear generalized split-window algorithm. Validation with in situ measurements from Surface Radiation Budget (SURFRAD) sites and Baseline Surface Radiation Network sites showed that the overall root-mean-square errors (RMSEs) of GT-LST varied from 1.6 to 4.0 K, and nighttime LSTs were typically better than daytime LSTs. Intercomparison with the Moderate Resolution Imaging Spectroradiometer LST products (MYD11A1 and MYD21A1) revealed that the overall root-mean-square difference (RMSD) was approximately 3.0 K. Compared with MYD11A1 LST, GT-LST was overestimated, and relatively large RMSDs were obtained during the daytime, spring, and summer, whereas the significantly smaller positive bias was obtained between GT-LST and MYD21A1 LST. Furthermore, we compared our newly generated dataset with a global AVHRR daytime LST product at the selected measurements of SURFRAD sites (i.e., measurements of these two satellite datasets were valid), which revealed similar accuracies for the two datasets. However, GT-LST can additionally provide nighttime LST, which can be combined with daytime observations estimating relatively accurate monthly mean LST, with an RMSE of 2.7 K. Finally, we compared GT-LST with a regional twice-daily AVHRR LST product over continental Africa in different seasons, with RMSDs ranging from 2.1 to 4.3 K. Considering these advantages, the proposed dataset provides a better data source for a range of research applications. GT-LST is freely available at https://doi.org/10.5281/zenodo.7113080 (1981–2000) (Li et al., 2022a), https://doi.org/10.5281/zenodo.7134158 (2001–2005) (Li et al., 2022b), and https://doi.org/10.5281/zenodo.7813607 (2006–2021) (J. H. Li et al., 2023).

Identification of Urban Functional Areas and Urban Spatial Structure Analysis by Fusing Multi-Source Data Features: A Case Study of Zhengzhou, China

The identification and delineation of urban functional zones (UFZs), which are the basic units of urban organisms, are crucial for understanding complex urban systems and the rational allocation and management of resources. Points of interest (POI) data are weak in identifying UFZs in areas with low building density and sparse data, whereas remote sensing data lack the necessary semantic information for functional zoning, and single-source data cannot perform a highly comprehensive characterization of complex UFZs. To address these issues, this study proposes a method for identifying UFZs by fusing multi-attribute features from multi-source data and introduces nighttime light and land surface temperature (LST) indicators as functional zoning references, taking the main urban area of Zhengzhou as an example. The experimental results show that the POI data with integrated three-level semantic information can characterize the semantic information of functional areas well, and the incorporation of multi-spectral, nighttime light, and LST data can further improve the recognition accuracy by approximately 10.1% compared with the POI single-source data. The final recognition accuracy and kappa coefficient reached 84.00% and 0.8162, respectively, indicating that the method is largely consistent with the actual situation and is feasible. The analysis showed that the main urban area of Zhengzhou as a whole is characterized by the coordinated development of single and mixed functional areas, in which a distinct residential-commercial-public complex is formed, and the urban functional areas on the block scale have diverse attributes. This study can provide a decision-making reference for the future development planning and management of Zhengzhou, China.

Opportunities and Challenges of Spaceborne Sensors in Delineating Land Surface Temperature Trends: A Review

Understanding the land surface temperature (LST) trends is crucial for policymakers and stakeholders to develop adaptation and mitigation strategies suitable for a sustainable environment coping in the face of climate change. This article presents a systematic review of the studies related to delineating spaceborne sensor-based LST trends, including information on the instruments and constellations of satellites (missions) that provide thermal infrared (TIR) and passive microwave (PMW) observations. About 99% of the studies used TIR, where 76% were Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra/Aqua) observations. Opportunities, challenges, and research gaps for using the TIR and PMW observations were also explored, with instruments onboard either polar-orbiting or geostationary satellites. We identified that the calibrated dataset (e.g., processed, harmonized, and standardized) is extremely limited for each constellation, with multiple satellites and instruments, to make it fully useful for the entire mission period. A few problematic methodological concepts were identified, including using a few images in a longer time series. Using only a few images, acquired on different calendar months in different years, would not provide the true annual trends over the study period because they can be influenced by seasonal variations. To estimate the warming or cooling daytime, nighttime, or diurnal LST trends, the use of MODIS observations could be useful, even though it does not acquire images during the maximum or minimum temperature in a daily cycle. This article indicated further investigations into those research gaps and recommended directions to overcome most of these limitations.

Detection of geothermal potential based on land surface temperature derived from remotely sensed and in-situ data

ABSTRACT Geothermal energy is a renewable and environmentally sustainable resource of increasing importance. However, areas with geothermal potential are not easily detected by traditional field investigations, requiring the development of new, robust, and reliable models for detection. In this study, remote sensing data and ground-based variables were used to detect and analyze geothermal resource potential areas. General Land Surface Temperature (GLST) was integrated using 5 years of remote sensing data. Landsat 8 daytime GLST (Landsat-GLST), Moderate Resolution Imaging Spectroradiometer (MODIS) daytime GLST (MODIS-DLST), and MODIS nighttime GLST (MODIS-NLST) data were integrated with Landsat Nighttime Land Surface Temperature (Night-LST), which not only filled the gap of Landsat Night-LST but also improved the spatial resolution of MODIS nighttime temperatures. Specifically, three independent variables (Night-LST, Distance From Known Geothermal Resource Points [DFGP], and Distance From Geological Faults [DFF]) were used to develop a weighted model to form a Geothermal Detection Index (GDI) based on Principal Component Analysis (PCA). Along with field verification, the GDI was successfully used to identify three geothermal activity areas in Tengchong City, Yunnan Province. Overall, this work provides a novel method for detecting geothermal potential to support the successful exploitation of geothermal resources.

ASSESSMENT OF SPATIAL AND TEMPORAL DISTRIBUTION OF URBAN HEAT ISLANDS (UHI) IN SEMI-ARID CLIMATE

An urban heat island phenomenon has increased in the last decades due to rapid urbanization, resulting in a significant impact on local climate. In this study, remote sensing data was used to analyze Spatiotemporal patterns of Urban Heat Island Intensity (UHII) over a 20 years period in a semi-arid climate area. The relationship between the Land Surface Temperature (LST), vegetation and Land Cover Types (LCTs) were examined. The relation between the UHII and its driving factors (different LCTs and meteorological conditions) was analyzed. Analysis of 8-day daytime and nighttime LST data acquired from MODerate-resolution Imaging Spectroradiometer (MODIS) shows that Amman has a significant UHII in both daytime and night time. The results show a negative correlation between the LST and vegetation indicators and between the UHII and the wind speed average, indicating a positive correlation between the UHII and temperature. Vegetation has been proven to significantly reduce LST, mainly in the daytime, due to its cooling effect that results from the transpiration process and shadow effect.

Land Surface Temperature Retrieval From Sentinel-3A SLSTR Data: Comparison Among Split-Window, Dual-Window, Three-Channel, and Dual-Angle Algorithms

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison owing to its dual-angle viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature-based and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types. The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Pérez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42 K, 1.79 K and 2.05 K, respectively. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature. The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.

The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model

Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

Effect of Deforestation on Land Surface Temperature in the Chiquitania Region, Bolivia

Neotropical forests offer alternatives to surface cooling and their conservation is an effective solution for mitigating the effects of climate change. Little is known about the importance of tropical dry forests for temperature regulation in Chiquitania, a region with increasing deforestation rates. The impact that deforestation processes are having on the surface temperature in Chiquitania remains an open question. This study evaluated trends in forest cover loss based on land surface temperatures (°C) in forested and deforested areas in Chiquitania. We hypothesized a positive relationship between higher deforestation and a temperature increase, which would decrease the resilience of highly disturbed Chiquitano forests. We evaluated ten sampling sites (10 × 10 km), including five in forested areas with some type of protection and the other five in areas with populated centers and accelerated forest loss. We developed scripts on the Google Earth Engine (GEE) platform using information from the Normalized Difference Vegetation Index (NDVI, MOD13A2) and the daytime and nighttime Land Surface Temperature (LST, MYD11A1) from MODIS products for the period 2001–2021. The statistical significance of the trends of the time series averages of the MODIS products was analyzed using a nonparametric Mann–Kendall test and the degree of the relationship between the variables was determined using the Pearson statistic. Our results based on NDVI analysis showed consistent vegetation growth in forested areas across the study period, while the opposite occurred in deforested lands. Regarding surface temperature trends, the results for daytime LST showed a positive increase in the four deforested areas. Comparatively, daytime LST averages in deforested areas were warmer than those in forested areas, with a difference of 3.1 °C. Additionally, correlation analyses showed a significant relationship between low NDVI values due to deforestation in three sites and an increase in daytime LST, while for nighttime LST this phenomenon was registered in two deforested areas. Our results suggest a significant relationship between the loss of forest cover and the increase in land surface temperature in Chiquitania. This study could be the first step in designing and implementing an early climate–forest monitoring system in this region.

LST-R: A method for assessing land surface temperature reduction in urban, hot and semi-arid Global South

Over the next 30 years, temperatures are expected to increase in hot semi-arid zones. Despite increasing studies on urban heat, cooling measures suitable for this climate zone remain poorly investigated. The proposed method is innovative because it focuses on significant landscape metrics for determining the land surface temperature (LST) and evaluating cooling measures. Recurrence of warm spells was identified analysing the daily air temperatures. Daytime and night-time LST data acquired from space were correlated with landscape metrics extracted from very high-resolution satellite imagery. Stepwise linear regression was used to identify the significant metrics that affected it. Cooling measures were selected considering implementation leeway; performance of existing measures; strengths, weaknesses, opportunities, and threats, equity analyses. Although the method was tested in Niamey, Niger, it can be applied to any city or town in hot semi-arid Global South, requiring decision-making support on cooling policies.•Landscape metrics are consistent with development standard and general requirements.•Evaluation of measures to reduce land surface temperature includes experts... advice.•Equity of measures to reduce land surface temperature is considered.

The Impact of Urban Expansion on the Urban Thermal Environment: A Case Study in Nanchang, Jiangxi, China

Urban expansion has been changing the urban thermal environment. Understanding the spatial distribution and temporal trends in the urban thermal environment is important in guiding sustainable urbanization. In this study, we focused on the land use/land cover (LULC) changes and urban expansion in Nanchang city, Jiangxi province, China. The four elements in the remote sensing-based ecological index (RSEI) are heat, greenness, dryness, and wetness, which correspond to the land surface temperature (LST), NDVI, NDBSI, and WET, respectively. According to the synthetic images of the average indices, we conducted temporal trend analysis together with statistical significance test for these images. We conducted partial correlation analyses between LST and NDVI, NDVSI, as well as WET. In addition, we used the LULC maps to analyze the multi-year trends in urban expansion. Then, we superimposed the trends in daytime and nighttime LST in summer on urban expansion area to extract the LST trends at sample locations. The results showed that LULC in Nanchang has substantially changed during the study period. The areas with statistically significant trends in LST coincided with the urban expansion areas. Land cover change was the main reason for LST change in Nanchang. In particular, artificial surfaces showed the greatest increase in LST; for per 100 km2 expansion in artificial surfaces, the daytime and nighttime LST increased by 0.8 °C and 0.7 °C, respectively. Among all the study land cover types, water bodies showed the greatest differences in LST change between the daytime and nighttime. There were statistically significant correlations between increases in LST and increases in NDBSI as well as decreases in NDVI and WET. In view of the considerable impact of urban expansion on the urban thermal environment, we urge local authorities to emphasize on urban greening when carrying out urban planning and construction.

On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River

Abstract. As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. To better quantify these hydroclimatic changes, it is crucial that the scientific community establishes a reasonably accurate understanding of the spatial patterns associated with the basin hydrologic response. In this study, we employed remotely sensed land surface temperature (LST) and snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess a regional hydrological model applied over the Colorado River Basin between 2003 and 2018. Based on the comparison between simulated and observed LST and SCF spatiotemporal patterns, a stepwise strategy was implemented to enhance the model performance. Specifically, we corrected the forcing temperature data, updated the time-varying vegetation parameters, and upgraded the snow-related process physics. Simulated nighttime LST errors were mainly controlled by the forcing temperature, while updated vegetation parameters reduced errors in daytime LST. Snow-related changes produced a good spatial representation of SCF that was consistent with MODIS but degraded the overall streamflow performance. This effort highlights the value of Earth observing satellites and provides a roadmap for building confidence in the spatiotemporal simulations from regional models for assessing the sensitivity of the Colorado River to climate change.

Integration of multiple earthquakes precursors before large earthquakes: A case study of 25 April 2015 in Nepal

In this work, we analyze magnetic data, Land Surface Temperature (LST), Outgoing Longwave Radiation (OLR), Air Temperature (AT) and Relative Humidity (RH) around the epicenter before and after the large EQs occurred on April 25, 2015 in Nepal. Daytime and nighttime LST values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. We used the magnetic data acquired by Swarm satellites in the top side ionosphere, and daily OLR data was obtained from the United States National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Moreover, AT and RH values were provided from the nearest meteorological ground station to the epicenter located at Katmandu airport. As data analysis, short-term (15 days before and 5 days after the earthquakes) and medium-term (2 months before and 1 month after the earthquakes) evaluations were carried out. For short-term analysis, the results of the magnetic field in the topside ionosphere detected an interesting anomaly on April 12, 2015. Besides, nighttime LST anomaly was observed on April 13, 2015, 14 days before the earthquakes, which was also an anomalous day for AT and RH analysis. Moreover, in the OLR analysis, April 12, 2015 was anomalous day based on the time series of the epicentral pixel that coincides with the magnetic field anomaly. Concerning the medium-term nighttime LST analysis, 13th April was the day, which may be associated with the earthquakes. For the medium-term OLR analysis, April 12, 2015 was found to be related to the seismic activity based on the epicentral pixel. Besides, the RH anomaly on 13 April was the only mutual day with the anomalies of LST and OLR for the medium-term analysis. Based on the short-term and medium-term results, these two days, 12 and April 13, 2015, may be associated with the preparation for the earthquake occurrence. Overall, it is concluded that these methods could be effective and reliable in detecting anomalies that precede upcoming EQs with further investigations.

Satellite-Based Discrimination of Urban Dynamics-Induced Local Bias from Day/Night Temperature Trends across the Nile Delta, Egypt: A Basis for Climate Change Impacts Assessment

The Nile Delta is the most vital region of the desert-dominated country of Egypt. Due to its prominent level of vulnerability to climate change’s negative impacts and its low capacity for adaptation and mitigation, the current study aims to provide accurate quantification of temperature change across the Nile Delta as an integral basis for sustainability and climate change impacts assessment studies. This was achieved through monitoring urban dynamics and detecting LST trends in 91 cities and their rural surroundings. The relevant local urban bias was discriminated from regional/background changes present in diurnal/nocturnal temperature records. The temperature records were then corrected/adjusted by removing this urban bias. Owing to the insufficiency of ground-based meteorological observatories, the investigation utilized moderate resolution imaging spectroradiometer (MODIS) land surface temperatures (LSTs) and Landsat-based datasets (2000–2021). The widely used Mann–Kendall test (MKT) and Theil–Sen estimator (TSE) were employed to assess trends in urban sprawl, LST time series, and the implied association. The analysis revealed that the region has experienced dramatic urbanization, where the total urban expansion was greater than two-thirds (69.1%) of the original urban area in 2000. This was accompanied by a notable warming trend in the day/night and urban/rural LST records. The nocturnal LST exhibited a warming tendency (0.072 °C year−1) larger than the diurnal equivalent (0.065 °C year−1). The urban dynamics were positively correlated with LST trends, whereas the Mediterranean Sea appeared as a significant anti-urbanization moderator, in addition to the Nile River and the prevailing northerly/northwesterly winds. The urban–rural comparison approach disclosed that the urbanization process caused a warming bias in the nighttime LST trend by 0.017 °C year−1 (21.8%) and a cooling bias in the daytime by −0.002 °C year−1 (4.4%). All results were statistically significant at a confidence level of 99%. It is recommended that studies of climate-related sustainability and climate change impact assessment in the Nile Delta should apply a distinction of urban-induced local effect when quantifying the actual regional temperature change.

Response characteristics and influencing factors of carbon emissions and land surface temperature in Guangdong Province, China

The continuous rise of carbon emissions has brought enormous pressure on the human environment. To determine the response characteristics and influencing factors of carbon emissions and land surface temperature (LST), we used the land use, LST, carbon emissions, and socioeconomic data of Guangdong Province in 2000, 2005, 2010, and 2017 through Spearman correlation analysis and factor detection of Geodetector. The findings revealed that carbon emissions had a semi-circular hierarchical structure from 2000 to 2017. The areas with significant carbon emissions were distributed in some counties and districts of Zhongshan, Dongguan, Guangzhou, and Shenzhen, and they continued to increase and expand outward. The annual average LST in Guangdong Province is between 17 and 24 °C, and the average daytime and nighttime LST are 21–28 °C and 13–20 °C, respectively. Total carbon emissions and LST have a positive coefficient of 0.3–0.7. The leading factors of carbon emissions in Guangdong Province are different in different periods, but the influence of economic aggregate (GRP), land scale (LS), and land intensity (LI) on the spatial differentiation of carbon emissions is relatively strong. The interaction effect of factors is more significant than the single factors. The outcome of the study is valuable for decision-makers to formulate emission reduction policies and achieve sustainable urban development.

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

La isla de calor urbano superficial y su manifestación en la estructura urbana de la Ciudad de México

From the processing of high- and low-resolution satellite images (Landsat and Modis), the development of bivariate correlations between daytime and nighttime land surface temperatures, and 27 metrics associated with urban structure and location, this work has analyzed the intensity of the phenomenon of Urban Heat Island manifested in Mexico City in one of the warmest months of the year 2018 to identify the most vulnerable areas to this phenomenon and their urban structure characteristics. Thus, the highest temperatures are found at a shorter distance from the inner city, as the most consolidated area. The population aged 65 or over, which is the most vulnerable to health problems associated with high temperatures and body thermoregulation, is located in areas of the city where the highest night temperatures are concentrated. Likewise, to a lesser extent, there is a direct correlation between high temperatures and areas with higher housing density, a greater surface area of paved streets, and a greater concentration of economic units per hectare. On the contrary, the zones with the lowest temperatures identified within the City were located in areas with the greatest heights above sea level, on steeper slopes, and with a greater surface of open areas. Likewise, the population aged 0 to 14 years, also identified within the range of vulnerability to high temperatures, is located mainly in areas with moderate and/or low temperatures. On the other hand, to identify the diurnal and nocturnal thermal variations, both in artificial and natural cover soil and their land use, thermal profiles were generated with measurements at every 1000 meters. This has allowed it to observe more pronounced thermal oscillations during the day, with the highest temperatures in the cultivation areas, residential land, and mixed-use. On the contrary, night temperatures stabilize and manifest the phenomenon of the Urban Heat Island in which it is shown that the highest temperatures are concentrated in the densest area of the city (Alcaldía Cuauhtémoc and Alcaldía Benito Juárez), which have stored a greater amount of heat due to the characteristics of the materials and composition of the urban environment, and the areas with a natural cover go down due to the ease of dissipating heat.

Heterogeneous warming impacts of desert wind farms on land surface temperature and their potential drivers in Northern China

To address rapid climate change, wind energy has been widely developed in China in the last two decades. However, wind farm (WF) turbulence effects can change the local climate by redistributing temperature, humidity, and heat fluxes. Previous studies indicate that WFs can significantly increase nighttime land surface temperature (LST); however, their conclusions are mainly derived from individual WFs and ignore heterogeneous impacts among multi-WFs in China. Another large source of uncertainty is that the WFs used in previous studies are mainly located in croplands or grasslands, which may obscure direct WF impacts because of the interactions between vegetation and the atmosphere. In this study, we detect impacts with MODIS LST products during 2001–2018 at sixteen WFs in the desert of northern China. The results suggest that the averaged warming impacts of WFs on LST are similar between nighttime (0.237 °C) and daytime (0.250 °C). However, the uncertainty is much greater for daytime (SD = 0.519 °C) than for nighttime (SD = 0.146 °C) due to spatially heterogeneous impacts of desert WFs on LST. Optimal structural equation models suggest that wind speed, precipitation, and distribution patterns of wind turbines mainly explain the spatial heterogeneity of the desert WF impacts on nighttime LST. Given the rapid development of WFs globally, the local warming impacts of WFs and their corresponding mechanisms should be highlighted as a high priority in the fields of energy and climate.

Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWAL8) and split-window algorithm (SWAL8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWAS3 and SWAH8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWAH8 was compared with MWAL8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day.

Grassland greening impacts on global land surface temperature.

Grassland vegetation greenness has been increasing globally during the past decades. Although the vegetation coverage change could have significant effects on climate by affecting albedo and evapotranspiration (ET), the effects of global grassland greening on climate remain unclear due to the lack of long-term field observation data. Here, we used satellite measurements of land surface temperature (LST) from high coverage grassland and adjacent low coverage grassland (divided according to the leaf area index) to quantify, for the first time, the biogeophysical effects of global grassland greening on surface temperatures. Results showed that grassland greening decreased the annual mean LST and daytime LST (LSTD), but did not significantly change nighttime LST (LSTN) globally from 2003 to 2017. Spatially, grassland greening had significant cooling effects on the annual mean LST and LSTD for latitudes south of 50°N due to the cooling effect of increased ET, whereas warming affects on the annual mean LST and LSTD in the high northern latitudes (> 50°N) because of the warming effects of decreased albedo. This study revealed that the effects of grassland greening on surface temperatures changed with latitude. During June, July, and August (JJA), the increasing grassland vegetation coverage decreased the LST between 25°S and 50°N, but increased the mean LST in high northern latitudes. By contrast, grassland greening has no significant effect on the mean LST in the temperate southern hemisphere (> 25°S) during JJA due to cooling and warming effects on LSTD and LSTN, respectively. During December, January, and February, grassland greening decreased the mean LST and LSTD for latitudes south of 25°N, but increased the mean LST and LSTN for latitudes north of 25°N. This study highlights the importance of including grassland vegetation coverage in models of regional surface temperature dynamics and future climate forecast.

Exploring the Variations and Influencing Factors of Land Surface Temperature in the Urban Agglomeration on the Northern Slope of the Tianshan Mountains

Changes in land surface temperature (LST) can have serious impacts on the water cycle and ecological environment evolution, which in turn threaten the sustainability of ecosystems. The urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) is located in the arid and semi-arid regions of northwest China, with an extremely fragile ecological environment and sensitive to climate change. However, studies on the LST of the UANSTM have not received much attention. Therefore, this study explored the spatial distribution pattern, fluctuation characteristics, and influencing factors of the LST of the UANSTM from 2005 to 2021 based on MODIS time series LST data and the geo-detector model with optimal parameters. The results show that the UANSTM is dominated by medium- and high-temperature classes, with high- and extremely high-temperature classes clustered in Turpan City. The daytime and nighttime LST patterns are significantly different, with a typical “daytime cold island and nighttime heat island” feature in the oasis region. During 2005–2021, LST fluctuated greatly in the northwestern part of the UANSTM, with LST showing an increasing trend during both daytime and nighttime, and the warming rate was more intense during daytime than nighttime. The increasing trend of LST in Urumqi, Changji Hui Autonomous Prefecture, Shihezi, and Wujiaqu is very significant and will remain consistent in the future. Precipitation, DEM, and AOD are the most important influencing factors of LST in the UANSTM, where precipitation and DEM are negatively correlated with LST, and AOD is positively correlated with LST. Land cover factors (LULC, NDVI,, and NDBSI) are the next most influential, and socioeconomic factors (NTL, GDP, and POP) are the least influential. The results of this study can provide a scientific reference for the conservation and sustainable development of the ecological environment of the UANSTM.