Land Surface Temperature Trends Research Articles

The spatial and temporal evolution of surface temperature in the main urban area of Hangzhou from 1990 to 2020 based on GEE

In this study, remote sensing (RS), geographic information system (GIS), Google Earth Engine (GEE), and mathematical analysis methods are applied to study the evolution and development trend of land surface temperature in Hangzhou. The GEE platform used in this study, which can fit remote sensing images of multiple regions under different spatial and temporal conditions, has the advantages of wide range and timeliness, and enables the study area to cover the entire Hangzhou city. The purpose of this study is to study the evolution of surface temperature in Hangzhou from 1990 to 2020 using a combination of remote sensing inversion and GIS spatial analysis, and to combine a variety of geostatistical analysis methods to specifically study the trend of surface temperature change and urban heat island development in Hangzhou in order to improve the local people’s life and local environment.

Trading greens for heated surfaces: Land surface temperature and perceived health risk in Greater Accra Metropolitan Area, Ghana

The unsustainable expansion of cities is generating urban heat islands(UHIs) by exchanging (trading) vegetation cover (green) for built impervious surfaces which is associated with heat-related health risks, globally. This phenomenon is exacerbated by climate change and anthropogenic activities like urban population growth, particularly in African cities. This study explores the spatio-temporal trends of land surface temperature (LST), land use land cover (LULC) and their economic and health risks in the Greater Accra Metropolitan Area (GAMA) of Ghana, from 1991 to 2021. We extracted LST/LULC information from Landsat datasets to perform change analysis, alongside an online survey across 56 communities on how LST relatesto economic and human health risks perceptions of residents. The results show urbanization of GAMA is trading greens for heated surfaces, impacting communities’ health risks. While the built environment grew (8.6%), the vegetation cover declined (2.5%) and the mean LST rose (0.8⁰C) in 25 years. A 30⁰C LST corresponds to the point of inflexion of exchanging green vegetative cover for heated built surfaces. The forest community of Kisseman, the populous community of Dansoman and the harbour city of Tema corresponded to the first, fourth and fifth LST quintiles, changing at −0.05⁰C, 0.06⁰C and 0.164⁰C per year. The common health risks include discomfort from heavy sweating, headaches, dehydration, thirst and skin rashes. These results call for climate action and green spatial planning through urban forestry and environmentalism in GAMA. For urban resilience and sustainable cities, we advocate green-cooling multi-purpose housing, roads, and industrial infrastructure.

Analyzing the correlation of forest and wetland with land surface temperature by using geospatial technology: a case of Yayo district, Southwestern Ethiopia

Forests and wetlands played a crucial role in minimizing the increasing trends of land surface temperature (LST). This study aimed to analyze the contribution of forests and wetlands on LST in Yayo district using multi-spectral and multi-temporal satellite. Landsat images of 1986, 2003 and 2021 were downloaded from United State Geological Survey website with 30 m spatial resolution. Results show that the mean LST was increased from 22.8 °C in 1986 to 27 °C in 2003 over cultivated land. As the results of forest conservation and wetland protection, the LST in Yayo district is experencing a declining trend in the recent year. Result reveal that the normalized difference vegetation index and Normalized Difference Water Index show a strong negative relationship with LST. Over the last two decades, the LST was declined by 1.5 °C due to the protection of forest and wetland ecosystem. Thus, governmental and non-governmental organization should encourage the conservation and protection of forest and wetland ecosystems.

Assessment of Land Surface Temperature Variations and Implications of Land Use/Land Cover Changes: A Case of Malappuram Urban Agglomeration Region, Kerala, India

Urbanization is taking place faster, and urban air temperatures are gradually rising in all cities across the world. Uncontrolled and unplanned urbanization leads to constant environmental threats and can alter local and regional climates. According to the survey published by Economist Intelligence Unit, in India, Kerala's Malappuram district ranks first among the fastest-growing urban areas globally, with a 44.05% growth rate. Hence, the present study aims to identify the hotspot regions of extreme heat within the Malappuram urban agglomeration region and suggest strategies for its improvement. The split-window algorithm retrieved land surface temperature (LST) for 1991, 1998, 2014, and 2020 using Landsat 5 ETM and Landsat 8 satellite imageries. A rising trend in LST has been detected in the last 30 years, and the mean value has increased by 1.70°C within the region. Among the selected hotspots, an LST increase of 1.84°C was observed for those areas with the highest increase in urban density with decreased vegetation. The increasing impact of urbanization and the subsequent change in land use patterns at the cost of greenery have caused a substantial effect on the local climate. Accordingly, planning and policy directions are proposed for the local government that can help provide awareness to the people through the effective implementation of mitigation measures.

Regional climate trend analyses for Aqua MODIS land surface temperatures

ABSTRACT Land surface temperature (LST) is the skin surface temperature of the Earth’s land surface and is an essential climate variable (ECV) that critically characterizes the Earth’s climate and surface energy processes. This is the first regional trend analysis for LST with uncertainties, using a stable LST climate data record suitable for climate trend analyses: the Aqua Moderate Imaging Spectroradiometer (MODIS) dataset (MYDCCI) produced in the European Space Agency (ESA) LST Climate Change Initiative (LST_cci) Project. The quality of the trends is verified here for Western Europe, for which consistent trends per decade of 0.5 K and 0.57 K across daytime and night-time data, respectively. Eight regions representative of a range of biomes and, including the Amazon, Western U.S.A., Greenland, the Sahel, Siberia, China, India, and Australia, have been analysed. Our most significant findings show substantial LST increases for Siberia and the Amazon, as well as evidence of recent increasing trends for the Western U.S.A. Siberia showed the most substantial daytime and night-time changes per decade of +0.87 K and +0.93 K, respectively. Furthermore, the Amazon and Western U.S.A. showed significant daytime LST trend increases of +0.82 K and +0.51 K, respectively. Western Europe and Western U.S.A. show the largest night-time change of +0.57 K and +0.54 K, respectively. India presents an atypical time series with a decreasing daytime and increasing night-time trend per decade of −0.62 K and +0.44 K, respectively, consistent with reported air temperatures. Overall the results show significant positive regional LST trends, against a background of both strong seasonal cycles and intermittent disruptive events, which demonstrate the importance of continued LST observations. The results also incentivize remote sensing science to derive ever more rigorous LST datasets and to continue to investigate emissivity and error correlations to allow improved trend analyses for smaller regions and individual locations.

Time-series analyses of land surface temperature changes with Google Earth Engine in a mountainous region

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Impacts of forest cover change on carbon stock, carbon emission and land surface temperature in Sor watershed, Baro Akobo Basin, Western Ethiopia

Abstract Human-induced actions aggravate forest degradation and result in carbon emissions. Increment of carbon emission raises land surface temperature (LST) and contributes to climate change. The aim of this study was to assess the impacts of forest cover change on carbon stocks, carbon emissions and LST over the period 1992–2022 using geospatial techniques in the Sor watershed, Western Ethiopia. The results revealed that forest land declined by 336.6 km2 due to the expansion of agricultural land with an area of 274.9 km2. Results show a decline in carbon stock of 58,883.4 tons/km2 while annual carbon emission exhibited an increasing trend of 2,418,083.91 tons to the atmosphere over the past three decades. As vegetation declined, LST increased by an average of 3.7 °C over the past three decades. All actors, including government and non-governmental organizations, should contribute to tree planting and reafforestation programmes to minimize the increasing trend of LST and carbon emissions. Furthermore, we need to build a climate-resilient green economy to protect people from the negative impacts of climate change.

AGRO-TOURISM IMPACT ANALYSIS OF CLIMATE CHANGE USING GOOGLE EARTH ENGINE IN THE RAHOVEC WINE REGION OF KOSOVO

Abstract. Climate change is a global problem and has a major impact on the economy of the developing world. The use of open data and modeling provides a considerable resource for governments, municipalities, and NGOs to develop an understanding of how climate change will impact their communities. This paper discusses the statistical concepts used to model MODIS Land Surface Temperature dataset and calculation of water balance with interpretations of the results. The results of this paper will utilize open datasets, an analysis of the impacts of temperature changes on the developing tourist economy in the Republic of Kosovo, and the knowledge of the capacity for leveraging large geographic datasets for open climate change research. The data from this paper shows a high level of vulnerability to viticulture in Kosovo due to significant trends in water balance and land surface temperature trends within the Drini I Bardhë basin and the future impacts of this on the regions wine agro-tourism by understanding the development of climate change in the region.

A Study on the Spatial and Temporal Variation of Summer Surface Temperature in the Bosten Lake Basin and Its Influencing Factors

The land surface temperature (LST) is an important indicator reflecting the ecological environment condition. As a sensitive area to climate change, mastering the spatial and temporal changes of summer LST in the Bosten Lake basin (BLB) helps gain insight into the evolution of the thermal environment in the Bosten Lake basin and for long-term monitoring of the basic ecological changes in the basin. Based on MOD11A1 data from 2005 to 2020, this paper investigates the diurnal LST spatiotemporal series variation and its influencing factors in the Bosten Lake basin by using surface temperature class classification, trending analysis, the Hurst index, and geographic probes. The results show that (1) the wetland grasslands in and around the Bayinbruck steppe in the northwestern part of the study area exhibit a heat island effect during the day, while the opposite is true at night. In terms of temporal changes, LST changes in the BLB fluctuate widely, having a general rising and then decreasing trend. (2) The decreasing trend of LST from 2005 to 2020 is significant during the daytime and vice versa at night, and the change at night is greater than during the day. The areas with significantly higher diurnal LST in the future have all expanded compared to the area occupied by them now, with an overall trend of a steady increase. (3) The dominant factor of LST variation has the strongest explanatory power when altitude and NDVI are combined during the daytime and the strongest explanatory power when NPP and temperature are combined at night.

Monitoring spatiotemporal impacts of changes in land surface temperature on near eastern fire salamander (Salamandra infraimmaculata) in the Middle East

Persistence and coexistence of many pond-breeding amphibians depend on seasonality. Temperature, as a seasonal climate component, affects numerous physical and biological processes of pond-breeding amphibians. Satellite-derived land surface temperature (LST) is the radiative skin temperature of the land surface, which has received less attention in spatiotemporal seasonal habitat monitoring. The present study aims to evaluate the increasing and decreasing effects of LST trends at two levels: (1) habitat suitability and connectivity; (2) individual population sites and their longitudinal distribution (with increasing longitude).Habitat suitability modeling was conducted based on an ensemble species distribution model (eSDM). Using electrical circuit theory, the connectivity of interior and intact habitat cores was investigated. An average seasonal LST was prepared separately for each season from 2003 to 2021 and entered into Mann-Kendall (MK) analysis to determine the spatiotemporal effects of LST changes using the Z-Score (ZMK) at two confidence levels of 95 and 99%. Based on the results, in winter, 28.12% and 70.70% of the suitable habitat were affected by an increasing trend of LST at 95% and 99% confidence levels, respectively. The highest spatial overlap of the decreasing trend of LST with the suitable habitat occurred in summer and was 6.4% at the 95% confidence level and 4.2% at the 99% confidence level. Considering population site at 95% confidence interval, the increasing trend of LST was calculated to be 20.2%, 9.5%, 4.2%, and 6.3% of localities in winter, spring, summer, and autumn, respectively. At the 99% confidence level, these percentages reduced to 8.5%, 3.1%, 1%, and 1%, respectively. During winter and summer, based on the results of the longitudinal trend, an increasing trend of LST was observed in sites. Localities of Hatay and Iica village in Turkey experienced seasonally asynchronous climate change regimes. The approach used in this study allowed us to create a link between the life cycle and seasonal changes on a micro-scale (breeding sites) and macro-scale (distribution and connectivity). Findings of this paper can be effectively used by conservation managers to preserve S. infraimmaculata's metapopulation.

City densification and temporal dynamics of traditional inner core of Ibadan, Nigeria

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island

Impact of land use dynamics on land surface temperature using optical remote sensing data integrated with statistical analysis in Riyadh, Saudi Arabia

Changes in land use and cover (LULC) are now considered as a significant concern because they have a great impact on biodiversity loss and climate changes. Therefore, the present study was applied to assess the effect of land use dynamic on land surface temperature (LST). LULC maps of years; 1991, 2001, 2014, and 2022 were applied using Maximum likelihood classification MLC) while the LST was developed using thermal bands of Landsat images. LULC maps showed a considerable development and improvement in vegetation cover and urbanization. Results showed a negative and positive correlation between LST with NDVI and NDBI respectively. It was revealed that the estimated and predicted LST confirm the reliability of the present model with R2 = 0.95. The study calculated that the maximum LST in 2022 to simulate current state of Riyadh, Saudi Arabia and the results were 32.1, 45.5, 38.7 and 41.3 in winter, summer, autumn and spring respectively. We are now sounding the alarm because the state must act quickly and decisively to stop intensive forestation if it is to avoid very high temperatures by 2030. Our findings point to the need for environmental protection in response to the study area's quick conversion of LULC and increasing trend of LST.

Annual and Seasonal Trends of Vegetation Responses and Feedback to Temperature on the Tibetan Plateau since the 1980s

The vegetation–temperature relationship is crucial in understanding land–atmosphere interactions on the Tibetan Plateau. Although many studies have investigated the connections between vegetation and climate variables in this region using remote sensing technology, there remain notable gaps in our understanding of vegetation–temperature interactions over different timescales. Here, we combined site-level air temperature observations, information from the global inventory modeling and mapping studies (GIMMS) dataset, and moderate-resolution imaging spectroradiometer (MODIS) products to analyze the spatial and temporal patterns of air temperature, vegetation, and land surface temperature (LST) on the Tibetan Plateau at annual and seasonal scales. We achieved these spatiotemporal patterns by using Sen’s slope, sequential Mann–Kendall tests, and partial correlation analysis. The timescale differences of vegetation-induced LST were subsequently discussed. Our results demonstrate that a breakpoint of air temperature change occurred on the Tibetan Plateau during 1994–1998, dividing the study period (1982–2013) into two phases. A more significant greening response of NDVI occurred in the spring and autumn with earlier breakpoints and a more sensitive NDVI response occurred in recent warming phase. Both MODIS and GIMMS data showed a common increase in the normalized difference vegetation index (NDVI) on the Tibetan Plateau for all timescales, while the former had a larger greening area since 2000. The most prominent trends in NDVI and LST were identified in spring and autumn, respectively, and the largest areas of significant variation in NDVI and LST mostly occurred in winter and autumn, respectively. The partial correlation analysis revealed a significant negative impact of NDVI on LST during the annual scale and autumn, and it had a significant positive impact during spring. Our findings improve the general understanding of vegetation–climate relationships at annual and seasonal scales.

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.

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq

BackgroundTemperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.ResultsIn the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.ConclusionThe increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.

The Impact of Land Cover Change on Surface Water Temperature of Small Lakes in Eastern Ontario from 1985 to 2020

Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST). However, the influence of LCC near waterbodies outside of an urban environment remain less understood. Waterbodies serve as local climate moderators where nearby LCC has the potential to decrease their cooling ability. Altered water surface temperatures can lead to altered species migration and distribution in aquatic species depending on a given species thermal boundary. In this study, using remotely sensed land cover and surface temperature data, we investigate the role that LCC around small lakes (500 m) plays on the surface water temperature change of nine small lakes in the Cataraqui Region Conservation Authority’s watershed, located in Eastern Ontario, from 1985 to 2020. The Continuous Change Detection Classification (CCDC) algorithm was used alongside the Statistical Mono-Window (SMW) algorithm to calculate LCC and LST, respectively. Results indicated a strong positive relationship (R2 = 0.81) between overall LCC and lake surface water temperature (LSWT) trends, where LSWT trends in all inland small lakes investigated were found to be positive. The land cover class sparse vegetation had a strong positive correlation with water temperature, whereas dense vegetation displayed a strong negative correlation. This 35-year study contributes to the broader understanding of the impact that LCC has on the surface water temperature trends of inland lakes.

Effects of deforestation and forest degradation on ecosystem service indicators across the Southwestern Amazon

The Southwestern Amazon (SWA) is home to one of the Earth́s remaining intact ecosystems and a key provider of continental atmospheric moisture flows. However, the integrity of this region is threatened by global changes in climate and local to regional changes in land-use and land-cover. Here, unlike most Amazonia-land-change research which has focused primarily on deforestation, we evaluated the effects of both deforestation and forest degradation on three Ecosystem Service Indicators (ESIs) – evapotranspiration (ET), land surface temperature (LST), and precipitation (P) – at the local and watershed scale across the SWA between 2003 and 2020. We computed annual and monthly ESI differences over distinct forest conditions and buffers around disturbed areas (degraded or deforested). We also determined the influence of forest disturbance trends on ESI trends at the pixel and watershed level through a Partial Mann-Kendall approach. The results show that ESI differences among different forest conditions are statistically-significant and more pronounced during the dry season. In comparison with intact forest, monthly P rates were up to 25 % lower over any type of disturbance; whereas ET rates were up to 15 % and 48 % lower, and LST rates up to 1.6 °C and 4.4 °C higher, over degraded and deforested areas, respectively. ET and LST edge effects were only significant within buffers around some of the most heavily disturbed areas. At the pixel scale, negative trends in ET and positive trends in both LST and P were more frequently explained by forest disturbances as these trends themselves become more pronounced. ET and LST trends determined by disturbances were generally located near roads, rivers, and human settlements; and surprisingly, we found that degradation more often influences these trends than deforestation, which we attribute to the practice of converting deforested areas into crops whose growing season ET and LST rates are similar to those in natural vegetation. At the watershed scale, the analysis suggested that the climate implications of degradation and deforestation have not scaled up to this level yet; however, literature has shown that even the local impacts we report here can have important implications for areas outside of the SWA, which emphasizes the importance of continuing to conserve this remote region.

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

Land Surface Temperature (LST) is an important factor in ground surface energy balance and in universal climatology studies. Elevation, Land Cover (LC), and vegetation index are three factors that influence ground surface variation, and their influences vary depending on geography. This study aimed to: (i) investigate the seasonal patterns and trends of daytime LST, and (ii) examine the influence of elevation, LC, and vegetation index on daytime LST increase in Taiwan from 2000 to 2021. LST, vegetation, and LC data were downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS) website, and elevation data were downloaded from the United States Geological Survey (USGS) website. The natural cubic spline method was applied to investigate annual seasonal patterns and trends in daytime LST. Linear regression modeling was applied to investigate the influence of elevation, LC, and vegetation index on daytime LST increases. The results showed that the average increase in daytime LST per decade in Taiwan was 0.021 °C. Elevation, LC, and vegetation had significantly affected the daytime LST increase, with R2 of 32.5% and 28.1% for the North and South parts of the country, respectively. The daytime LST increase in the North at elevations higher than 1000 m had an increasing trend, while in the South the increasing trend was found at elevations higher than 350 m above sea level. All types of forest and urban areas in the North had a higher daytime LST increase than the average, while in the South, the areas with water, closed shrubland, and urban parts had a higher daytime LST increase than the average.

Evaluation of Consistency Among MODIS Land Surface Temperature Products for Monitoring Surface Warming Trend Over the Tibetan Plateau

AbstractModerate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily land surface temperature (LST) products are important data sets for global warming monitoring. To reveal the consistency and differences between MODIS Terra and Aqua daily LST products for monitoring LST trends, this study used these two products to extract the mean annual surface temperature (MAST) of the Tibetan Plateau from 2003 to 2020. Then, nine MAST combinations were constructed for daytime, nighttime, and daily average conditions separately with Terra and Aqua MAST series, including Terra only, Aqua only, and the integrated one. Finally, the Mann‐Kendall trend test was applied to these series and obtained the spatiotemporal variability. The results indicated that there was high spatial consistency of the multi‐year averaged MAST of different combinations at daytime/nighttime/daily average scales. Meanwhile, there are quite similar temporal trends in terms of spatial distribution and change rate. The MAST changes are dominated by warming trends, and the areas and change rates of the significant changing pixels at different elevations, vegetation cover ranges, and land cover types presented similar distribution patterns. Although there are some discrepancies in the detailed location for pixels with significant trends among different combinations, the proportion of the pixels with the same trends of daytime/nighttime/daily‐average groups is as high as 68.88%, 66.75%, and 71.76%, respectively. These findings reveal that different MODIS LST products have similar performance in monitoring LST trends, indicating that the detection can well reflect the variation of surface thermal environment regardless of which MODIS LST product is used.