Mean Annual Land Surface Temperature Research Articles

Natural forest regeneration is projected to reduce local temperatures

Forest regeneration is a crucial strategy for mitigating and adapting to global warming. Yet its precise impact on local climate remains uncertain, a factor that complicates decision-making when it comes to prioritizing investments. Here, we developed global maps illustrating how natural forest regeneration influences key local climate drivers—land surface temperature (LST), albedo, and evapotranspiration—using models fitted at a 1-km spatial resolution with a random forest classifier. We found that natural forest regeneration can alter annual mean LST by 0.01 °C, −0.59 °C, −0.50 °C, and −2.03 °C in Boreal, Mediterranean, Temperate, and Tropical regions, respectively. These variations underscore the region-specific effects of forest regeneration. Importantly, natural forest regeneration reduces LST across 64% of 1 billion hectares and 75% of 148 million hectares of potentially restorable land under different scenarios. These findings improve understanding of how forest regeneration can help regulate local climate, supporting climate adaptation efforts.

Quantifying the influencing factors of the thermal state of permafrost in Northeast China

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation

Heatwaves pose a significant risk to human society, and assessing vulnerability at the local scale is challenging due to the multifaceted nature of contributing factors. This study focuses on evaluating the heatwave vulnerability of five major cities in Bangladesh: Chittagong, Dhaka, Khulna, Rajshahi, and Sylhet. Heatwave vulnerability index (HVI) was estimated, integrating exposure, sensitivity, and adaptive capacity components. Time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for land surface temperature (LST) spanning 2000–2019 served as the primary dataset. The HVI calculation employed a principal component analysis method. The spatial distribution of the resulting HVI across five metropolitan areas were evaluated by estimating spatial autocorrelation and identifying clusters and hot spots in vulnerable areas. The findings revealed that urban centers characterized by extensive built-up areas exhibited higher exposure to urban heat compared to their surroundings. Dhaka, the capital, recorded the highest mean annual LST, while Sylhet registered the lowest. Rajshahi and Sylhet stood out for having the largest percentage of heat-exposed and sensitive areas, respectively. Sylhet also had the highest percentage of heat-vulnerable areas, reaching 63 %, whereas Dhaka had the highest number of vulnerable individuals, totaling 12.5 million. The findings also suggested that augmenting urban green and blue infrastructures holds potential for mitigating the adverse impacts of heatwaves. The methodology and outcomes of this study provide a valuable foundation for devising heatwave adaptation strategies not only in Bangladesh but also in other regions facing similar challenges.

Spatial and Temporal Variations of Vegetation Phenology and Its Response to Land Surface Temperature in the Yangtze River Delta Urban Agglomeration

In the Yangtze River Delta urban agglomeration, which is the region with the highest urbanization intensity in China, the development of cities leads to changes in land surface temperature (LST), while vegetation phenology varies with LST. To investigate the spatial and temporal changes in vegetation phenology and its response to LST in the study area, this study reconstructed the time series of the enhanced vegetation index (EVI) based on the MODIS EVI product and extracted the vegetation phenology indicators in the study area from 2002 to 2020, including the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL), and analyzed the temporal–spatial patterns of vegetation phenology and LST in the study area, as well as the correlation between them. The results show that (1) SOS was advanced, EOS was postponed, and GSL was extended in the study area from 2002 to 2020, and there were obvious differences in the vegetation phenology indicators under different land covers and cities; (2) LST was higher in the southeast than in the northwest of the study area from 2002 to 2020, with an increasing trend; and (3) there are differences in the response of vegetation phenology to LST across land covers and cities, and SOS responds differently to LST at different times of the year. EOS shows a significant postponement trend with the annual mean LST increase. Overall, we found differences in vegetation phenology and its response to LST under different land covers and cities, which is important for scholars to understand the response of vegetation phenology to urbanization.

Analysis of spatiotemporal variation in land surface temperature on the Tibetan Plateau from 2001 to 2020

ABSTRACT With global climate warming intensifying, the Land Surface Temperature (LST) variation on the Tibetan Plateau (TP) has become a hot topic in environmental studies. This letter employs spatial analysis to identify LST distribution characteristics, the Pettitt test to detect abrupt changes, and Fast Fourier Transform (FFT) to analyse periodic variations. Analysing mean annual and seasonal LST from 2001 to 2020, the results indicate the following: (1) Over the past 20 years, the TP’s LST increased slowly at 0.08°C per decade, with significant seasonal variations. Warming trends dominate in spring, summer, and autumn, shifting from the northern to the southwestern plateau. Autumn shows the most significant warming at 0.31°C per decade, while winter shows a cooling trend at −0.46°C per decade. (2) The Pettitt test reveals no statistically significant abrupt changes in mean annual and seasonal LST, though potential change points were detected in 2013 for mean annual LST, and in 2005, 2006, 2014, and 2017 for summer, spring, autumn, and winter, respectively. This indicates relative stability in interannual temperature changes, with some asynchronous seasonal variations. FFT analysis indicates dominant short-term variability with a period of 3.33 years and secondary cycles of 10 and 5 years.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing

The rapid development of solar energy worldwide has attracted increasing attention due to its climatic and environmental impacts. Using MODIS data, we quantified the effects of solar farms (SFs) on albedo, vegetation (using enhanced vegetation index (EVI) as a proxy), and land surface temperature (LST) based on 116 large SFs across the world. The results show that the installation of SFs decreased the annual mean surface shortwave albedo by 0.016 ± 0.009 (mean ± 1 STD) and reduced the EVI by 0.015 ± 0.019 relative to the surrounding areas. SFs produced a strong cooling effect of −0.49 ± 0.43 K in the annual mean land surface temperature during the daytime and a weaker cooling effect of −0.21 ± 0.25 K during the nighttime. The greatest impacts on albedo and daytime LST were observed in barren land, followed by grassland and cropland, while the opposite order applied for vegetation impact. In terms of seasonal and latitudinal variations, the largest impact was observed at high latitudes in winter on albedo, at mid-latitudes in summer on vegetation, and at low latitudes in spring–summer transitions on daytime LST. Correlation analysis showed that the albedo and LST impacts were enhanced over large SFs with high capacity. The vegetation and LST impacts were both correlated with geographic and climatic factors and dependent on the type of SF (photovoltaic or concentrating solar power). Our global assessment provides observational evidence for the effects of SF construction on the environment and local climate, which can help the sustainable development of solar energy.

Machine learning-based assessment and simulation of land use modification effects on seasonal and annual land surface temperature variations

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Spatiotemporal change patterns and driving factors of land surface temperature in the Yunnan-Kweichow Plateau from 2000 to 2020

In this study, the spatiotemporal change patterns and driving factors of land surface temperature (LST) on the Yunnan-Kweichow Plateau (YKP) during 2000–2020 are investigated by using the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST dataset provided by National Tibetan Plateau Data Center. The YKP LST spatiotemporal change patterns are revealed at annual, seasonal, monthly, and daily scales. Furthermore, seven driving factors such as air temperature, land cover types, normalized difference vegetation index, precipitation, solar radiation, elevation, and latitude are quantified the impacts on LST spatial heterogeneity at annual scale. The main findings are as follows: (1) Annual mean LST increases by 0.016 K/year. Annual mean daytime LST slightly decreases by 0.009 K/year. Annual mean nighttime LST significantly increases by 0.042 K/year. (2) The trend and seasonal components of the daily, daily mean daytime, and daily mean nighttime LST have five and four breakpoints respectively, indicating that the variation of LST is unstable during 2000–2020 on the YKP. (3) The LST lapse rates at nighttime are generally higher than those at daytime on the YKP at the annual, seasonal, and monthly scales. The LST maximum lapse rate is 0.59 K/100 m in summer nighttime, and the LST minimum lapse rate is 0.18 K/100 m in winter daytime. (4) The controlling effects of seven factors are generally stronger in the nighttime than those in the daytime. The factors of elevation and air temperature dominate the LST spatial distribution on the YKP, with a contribution rate of >70 %. In addition, the interactions among the seven factors are all enhancing the effects on the spatial distribution of annual mean LST, including bivariate enhancement and nonlinear enhancement. This study contributes to the mitigation and adaptation to climate change of LST in the plateau and plays a theoretical reference role in formulating corresponding policies for environmental protection.

Greenland-Ice-Sheet Surface Temperature and Melt Extent from 2000 to 2020 and Implications for Mass Balance

Accurate monitoring of surface temperature and melting on the Greenland Ice Sheet (GrIS) is important for tracking the ice sheet’s mass balance as well as global and Arctic climate change. Using a moderate-resolution-imaging-spectroradiometer (MODIS)-derived land-surface-temperature (LST) data product with a resolution of 1 km from 2000 to 2020, the temporal and spatial variations of annual and seasonal ‘clear-sky’ surface temperature were evaluated. We also monitored summer surface melting and studied the relationship between the mass balance of the ice sheet and changes in surface temperature and melting. The results show that the mean annual LST during the study period is −24.86 ± 5.46 °C, with the highest of −22.48 ± 5.61 °C in 2010 and the lowest temperature of −26.49 ± 5.30 °C in 2015. With the change of season, the spatial variation of the ice-sheet surface temperature changes greatly. 2012 and 2019 experienced the warmest summers (−5.92 ± 4.01 °C and −6.51 ± 3.93 °C), with extreme cumulative melting detected on the ice-sheet surface (89.9% and 89.7%, respectively), and 2002 also experienced a greater extent of melting. But short period of melt in 2002 and 2019 (30.6% and 31.4%, respectively), accounted for a larger proportion, with neither the duration nor intensity of the melt reaching that of 2012. There is a strong correlation between the GrIS surface temperature and its mass balance. By fitting the relationship between surface temperature and mass balance, it was found that 93.83% (6.17%) of the ice-sheet response to surface-temperature change was via surface-mass balance (discharge and basal-mass balance).

Pixel level spatial variability modeling using SHAP reveals the relative importance of factors influencing LST

As an important indicator of the regional thermal environment, land surface temperature (LST) is closely related to community health and regional sustainability in general, and is influenced by multiple factors. Previous studies have paid scant attention to spatial heterogeneity in the relative contribution of factors underlying LST. In this study of Zhejiang Province, we investigated the key factors affecting daytime and nighttime annual mean LST and the spatial distribution of their respective contributions. The eXtreme Gradient Boosting tree (XGBoost) and Shapley Additive exPlanations algorithm (SHAP) approach were used in combination with three sampling strategies (Province-Urban Agglomeration -Gradients within Urban Agglomeration) to detect spatial variation. The results reveal heterogenous LST spatial distribution with lower LST in the southwestern mountainous region and higher temperatures in the urban center. Spatially explicit SHAP maps indicate that latitude and longitude (geographical locations) are the most important factors at the provincial level. In urban agglomerations, factors associated with elevation and nightlight are shown to positively impact daytime LST in lower altitude regions. In the urban centers, EVI and MNDWI are the most notable influencing factors on LST at night. Under different sampling strategies, EVI, MNDWI, NL, and NDBI affect LST more prominently at smaller spatial scales as compared to AOD, latitude and TOP. The SHAP method proposed in this paper offers a useful means for management authorities in addressing LST in a warming climate.

Time series analysis and impact assessment of the temperature changes on the vegetation and the water availability: A case study of Bakun-Murum Catchment Region in Malaysia

The Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for the past thirty years (1990–2020) were used. Remote sensing techniques for estimating the surface temperatures and variation within the vegetation and water bodies were utilized, and validation was done using on-ground weather stations. Google Earth Engine (GEE) and other RS & GIS tools were used for analyzing the time series trends of land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The results exposed an overall rise of 1.06 °C in the annual mean temperatures over the last thirty years. A maximum annual mean NDVI of 0.48 was recorded for 2018 and 2019. The lowest annual mean NDVI (0.27) was observed in 2005. The annual mean NDWI increased to 0.48 in 2018 and 2019, respectively. The statistical correlation results revealed the coefficient of determination (R2) of 0.09 and 0.13 for the annual mean LST and annual mean NDVI and the annual mean LST and annual mean NDWI, respectively. Moreover, the Mann-Kendall trend test for the annual mean temperature series indicates a slightly increasing trend with Sen's slope of 0.03 °C/year. It is found that there is a positive trend in the annual mean rainfall patterns, as Sen's slope indicates a yearly increase of 50.58 mm/year. This study found significant changes in the LST, NDVI, and NDWI of the BM catchment region during the last thirty years, demanding the concerned authorities' instant attention to alleviate the adverse effects of such changes to protect the ecosystem.

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.

Hanging glacier avalanche (Raunthigad–Rishiganga) and debris flow disaster on 7 February 2021, Uttarakhand, India: a preliminary assessment

A catastrophic debris flow in the Rishiganga and Dhauliganga rivers in Uttarakhand, India, on 7 February 2021 left a trail of disaster. Around 100–150 people lost their lives according to Uttarakhand Chief Secretary statement given to ANI news portal, two hydropower projects were badly damaged and a bridge across the Rishiganga River was washed off in the event. Study shows that the debris flow is caused due to detachment of 0.59 km2 right lobe of a hanging glacier and resultant ice-rock avalanche. This right lobe of the glacier was located over a mountain slope having an average slope of 35° at 4700–5555 m a.s.l. and travelled 12.4 km before hitting the infrastructure projects. Role of precipitation, snow cover, land surface temperature, and permafrost processes were investigated for identifying causes of the event. Since 2012, monsoon precipitation and mean annual land surface temperature (LST) showed significant increasing trend. Snow cover during monsoon months showed increasing trend and September, October and November experienced decreasing trend at glacier elevations. Mean annual LST increased from − 0.3 °C in 2012 to a peak of 0.4 °C in 2016. Central lobe of the glacier advanced during this period and eventually fell off in 2016 suggesting that the LST warming forced reduction of frictional drag at the interface facilitating it advancement and eventual dislodgement. Permafrost modelling suggests warm permafrost below 50 m and conditions favourable for intense frost cracking up to 10–15 m. At ~ 40 m depth, the delayed response of 2012–2016 warming produced peak positive temperature conditions by December and probably facilitated the formation of thin film of water at the deeper layers acting as a lubricant for glacier sliding. It is also suggested that the increase in summer precipitation might have forced thickening of the accumulation area and thereby increasing the shear stress for sliding of the glacier. It is proposed that the recent change in the weather conditions in the region is primarily responsible for this event through geological, glaciological, and permafrost processes. Flood modelling study suggests a flood volume of ~ 10 MCM generating 24.5 m flow depth at the bridge site with 12.7 m/s flow velocity. The event highlighted the need for improved monitoring of the cryosphere areas of the Himalaya to capture the early warning signs for better preparedness.

Intraurban heterogeneity of space-time land surface temperature trends in six climate-diverse cities

Surface urban heat islands (SUHIs) are present in all cities, derived from their thermal properties. While looking at the spatiotemporal variability of land surface temperature (LST), there is still a gap in understanding patterns of change. In this paper, we analysed diurnal and nocturnal annual mean LST trends in continental (Beijing), temperate (Mexico City and Santiago), and arid (Cairo, Hyderabad, and Riyadh) cities employing 1 km MODIS data (2003–2019). Each time-series was assessed with the structure of a space-time cube. Hot and cold spots were detected for each year and the LST trends were analysed. Each pixel was classified into different space-time LST trends and their SUHIs were estimated. Cities exhibit trends of increasing temperatures in cold and hot spots for diurnal and nocturnal data. Temperatures are increasing faster in hot spots for diurnal and in cold spots for nocturnal scenes. Steady hot spots and warming hot spots exhibit the highest SUHIs for day and night. Our approach provides a framework to empirically delineate the spatial intraurban heterogeneity of LST patterns over time. This spatially explicit information provides insights into urban areas requiring heat mitigation strategies and can be used to monitor the performance of measures already implemented for climate adaptation.

Assessment of surface roughness and fractional vegetation coverage in the CoLM for modeling regional land surface temperature

The application of Land surface models (LSMs) at regional scales still faces challenges and large uncertainties exist in simulating regional energy fluxes and land surface temperature (LST). This is possibly caused by their poor representations of surface parameter properties (e.g., fractional vegetation coverage (FVC) and surface roughness length (z0m)) to effectively capture vegetation dynamics. In this study, satellite-based FVC and z0m schemes were incorporated into the Common Land Model (CoLM) and their impacts on modeling energy fluxes and associated LST were further assessed over grassland and forest areas in China. Our results show that the annual mean LST was obviously overestimated by the original CoLM for both grassland and forest. The overestimation was reduced by the revised CoLM when compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. Concerning the seasonal patterns, the revised model performed evidently better in simulating LST in summer than in winter. Additionally, the decreased simulation of LST was dominated by a reduction of the surface absorbed short-wave radiation when applying the revised FVC scheme over both grassland and forest, while the increased sensible/latent heat fluxes contributed most to the reduction of LST for the revised z0m scheme in grassland/forest, respectively, indicating that land surface energy partitioning is sensitive to the FVC and z0m parameterizations. Results from this study suggest that the use of satellite-based dynamics of FVC and z0m parameterizations in the CoLM provides a valuable improvement in regional applications.

Interannual Spatiotemporal Variations of Land Surface Temperature in China From 2003 to 2018

Land surface temperature (LST) is a comprehensive embodiment of surface energy balance and land surface processes. The spatial and temporal variation of LST is of great significance for studying surface characteristics and climate change. In this study, the spatiotemporal variations of LST in China from 2003 to 2018 is examined by using the continuous and derivable annual temperature cycle model. The trends of the annual mean and annual amplitude of LST is detected using the Mann–Kendall test and Theil–Sen estimator. In addition, we have further revealed the correlation between normalized difference vegetation index (NDVI) and LST in different land cover types. The results show that the annual mean LST presents a spatial distribution pattern of high values in the southern regions and low values in the northern regions and that the factors of altitude and land cover type also affect the LST's spatial distribution. The annual amplitude of the LST presents a spatial distribution pattern of high values in the northern regions and low values in the southern regions. In the majority of instances, the phase of LST in China was positioned between the 175th and 205th day of each year. Both the annual mean and annual amplitude of the LST have a mean increasing trend in China with a rate of 0.02 K/year, and the areas with large significant changes accounted for 8.6% out of the total area, with a mean rate of approximately 0.05 K/year, in this period. Significant changes in the annual mean LST were correlated with the change in vegetation coverage and the impact of land cover types on the interannual variations of LST is also determined in this study. While the increase in vegetation coverage in barren land exhibits a clearly recognizable upward trend in the annual mean LST, the improved vegetation coverage in the grassland region presents a downward trend in annual mean LST.

A combined Terra and Aqua MODIS land surface temperature and meteorological station data product for China from 2003 to 2017

Abstract. Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71, P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at https://doi.org/10.5281/zenodo.3528024 (Zhao et al., 2019).

Remote Sensing Assessment of the Impact of Land Use and Land Cover Change on the Environment of Barddhaman District, West Bengal, India

Land transformation is caused by natural as well as anthropogenic driving forces and its impact on the regional environment is a key issue in understanding the relationship between society and the environment. Here, we investigate Land Use Land Cover (LULC) change over four decades, based on Landsat satellite imagery for 1987, 1997, 2007 and 2017, for the Barddhaman district of West Bengal, India. In total, six land use and land cover types have been identified. Over the period in question, there are notable increases in the area under built-up land, plantations and water bodies, whereas there has been a marked decrease in forest cover, agricultural land, and in bare land. The diverse effects of land transformation on the natural environment have been assessed using Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI),Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Effective Roughness Length (ERL) and Surface Albedo (SA).Overall, mean annual LST increased by 2.91°C during the study period, while there were reduced values for vegetation indices and an increase in the water index over the period 1987 to 2017. LAI and ERL both exhibit notable decreases, although the pattern was not uniform across the study area. For example, LAI values increased over time in the Kalna, Memari, Jamalpur, Ausgram and Kanksa regions. In Faridpur-Durgapur, Raniganj, Asansol and Raina, increases in surface albedo and ERL were more marked than in other regions. Negative correlations are found between LST-NDVI and NDVI-NDWI, while there is no correlation between LST and NDWI. During the period 1987-2017, NDVI values have declined, although the NDWI shows no clear trend. Land use land cover change is shown to have had a series of negative impacts on the environment of the Barddhaman district. In response, technological, economic, policy, or legislation measures are needed to restore degraded ecosystem services in the district as well as other areas where similar impacts are experienced.

Lake area monitoring based on land surface temperature in the Tibetan Plateau from 2000 to 2018

Lake area change in the Tibetan plateau is an important indicator for climate change assessment. To overcome the temporal inconsistency of optical remote sensing-based lake area detections, a land surface temperature (LST)-based detection scheme was proposed by utilizing the big difference between land and water surface temperatures. A trend test conducted by the Mann–Kendall (MK) method was successfully applied to investigate lake area variation from 2000 to 2018 with the use of the annual mean temperature information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST daily product. A comparison with the monitoring results from Landsat images indicates the proposed method can provide spatial distributions of lake area change with high accuracy. More importantly, the temporal variation of annual mean LST provides a special way to detect the abrupt change year (ACY) in lake area.The ACYs of most lakes mainly occur from 2004 to 2012. For an individual lake, the ACY offers vital information about the lake area change process. In summary, this work demonstrates the good potential of the LST-based method for lake area monitoring and assessment.