Decrease In Land Surface Temperature Research Articles

Evaluating the Impact of Green Spaces on Urban Heat Reduction in Rajshahi, Bangladesh Using the InVEST Model

Urban heat poses significant challenges in rapidly developing cities, particularly in countries like Bangladesh. This study investigates the cooling effects of urban green spaces in Rajshahi city, addressing a critical research gap in developing urban contexts. We examined the relationships among urban vegetation, heat mitigation, and temperature variables using the InVEST Urban Cooling Model and spatial analysis techniques. This study focused on three key relationships: Normalized Difference Vegetation Index (NDVI) and Heat Mitigation Index (HMI), HMI and Land Sur face Temperature (LST), and HMI and Air Temperature (AT). Analysis revealed a strong positive correlation between NDVI and HMI, indicating the effectiveness of vegetation in enhancing urban cooling. A robust inverse relationship between HMI and LST was observed (R2 = 0.78, r = −0.88), with every 0.1 unit increase in HMI corresponding to a 0.53 °C decrease in LST. The HMI−AT relationship showed an even stronger correlation (R2 = 0.84, r = −0.87), with each unit increase in HMI associated with a 2.80 °C decrease in air temperature. These findings quantify the significant role of urban green spaces in mitigating heat and provide valuable insights for urban planning in developing cities, underscoring the importance of integrating green infrastructure into urban-development strategies to combat urban heat and improve livability.

Monitoring Land Surface Temperature Trends in Indonesia

Monitoring Land Surface Temperature (LST) trends helps identify provinces vulnerable to temperature changes and aids in developing appropriate adaptation and mitigation strategies. This research article aims to monitor LST trends in Indonesia from 2000 to 2022 using MODIS Aqua Land Surface Temperature and Emissivity (MYD11A1) imageries. The study utilizes LST Day and LST Night datasets obtained from MYD11A1 datasets to achieve its research objective. Sens’ Slope is employed to identify and measure the magnitude and direction of temperature changes, while the Mann-Kendall trend test detects significant LST trends. GEE is used to process and analyze MODIS LST data. The findings show a significant increase in LST Night during the June-August (JJA) period in most provinces of Indonesia. LST Day data also stresses that some provinces in Indonesia are experiencing a warming trend. Elevation and population increases may contribute to the observed increasing LST trend. Notably, a short-term decrease in LST may occur within the long-term increasing LST trend.

Tracking the Temporal Changes in Land Surface Temperature, Vegetation, and Built-up Patterns in Rizal Province, Philippines using Landsat Imagery

The Rizal Province was subjected to a series of natural and human-induced disturbances throughout the years. Currently, the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. This study uses remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped for areas caused by changes in land use and land cover from Landsat Collection 1- Level 1 (Landsat 4-5 ™ C1- Level 1 & Landsat 8 OLI/ TIRS C1 Level 1) and calculated three parameters namely, (i) Land surface temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), and (iii) the Normalized Difference Built-up Index (NDBI). The results showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in LST from 29.34°C to 24.03°C, (ii) the relationship between LST and NDBI is directly proportional, whereas an inversely proportional relationship can be observed between LST and NDVI, and (iii) there is a fluctuating LST due to the changes in the land cover of the study site for almost three decades. This implicates the extensive shift in the ambient temperature of Rizal which further emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and urban development. This highlights how human-induced and natural factors significantly contribute to the release of heat and ambient temperature, thus, accentuating the need for sustainable urban planning.

Long-term climate change and anthropogenic activities together with regional water resources and agricultural productivity in Uganda using Google Earth Engine

Uganda with its fragile ecosystem, large-scale human activities, and increasing population pressure, all of which combined, make this region increasingly susceptible to climate variation. This study examined the long-term trends of annual, seasonal, and monthly distributions of rainfall and temperature from 2001 to 2021 together with crop-wise agricultural productivity. For the analysis, we obtained CHIRPS-V2.0 (Climate Hazards Group InfraRed Precipitation with Station Data version 2.0) rainfall, Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), DMSP nighttime lights, ESA land cover attribution, and international crop production assessment records. Subsequently, several non-parametric statistical applications were applied to check the long-term spatio-temporal trends of climate parameters and their inter-relationship at higher significance using the Google Earth Engine platform. The investigation reveals an annual increase in LST, averaging 0.01 °C/year along with decreasing rainfall (1.89 mm/year). However, regional climate trends are largely elevation-dependent, which are predominantly subjected to the northern part of the study area witnessing a slight decrease in LST and thereby increased rainfall. Moreover, the long-term spatial nexus estimation divulges a potent inverse association between rainfall and temperature in the north and northeastern regions of the study area. Concurrently, changing patterns also have led to a decline in crop production and deterioration in water availability, which is accompanied by various other abnormalities, including the scarcity of water resources and anthropogenic activities. Changing climate has had significant implications on crop production, largely on corn and soybean as long-term shifts influence it in average rainfall and temperature, yearly fluctuations, and disturbances during various growth stages.

Measuring the relationships between various urban green spaces and local climate zones

Urban green spaces (UGS) improve living conditions in cities by mitigating the Urban Heat Island effect. While the cooling effect of UGS seems unequivocal, the relationship between the types of UGS and types of residential areas has not yet been well explored. In this study, we systematically analysed the cooling effect of 71 UGS in Prague, a central European city, on residential areas within 400 m of the UGS. The UGS are classified according to their spatial characteristics (size, shape, and tree density), and the residential areas according to three Local Climate Zones (LCZ 2, 5, 6) typical for European cities. The cooling effect is evaluated using a regression model of the Land Surface Temperature (LST) in residential zones according to the LCZ type and distance from the various UGS. The results show that compact UGS of 10–25 ha with dense trees have the most pronounced cooling effect. This type of UGS was associated with a mean decrease in LST within 400 m of 2.3 °C compared to the least effective UGS type (long with sparse trees) across LCZs. The results of the presented study can be applied in urban planning and urban design to improve microclimates in cities.

Impacts of Green Fraction Changes on Surface Temperature and Carbon Emissions: Comparison under Forestation and Urbanization Reshaping Scenarios

Global land cover dynamics alter energy, water, and greenhouse gas exchange between land and atmosphere, affecting local to global weather and climate change. Although reforestation can provide localized cooling, ongoing land use land cover (LULC) shifts are expected to exacerbate urban heat island impacts. In this study, we monitored spatiotemporal changes in green cover in response to land use transformation associated with the Khyber Pakhtunkhwa (KPK) provincial government’s Billion Tree Tsunami Project (BTTP) and the Ravi Urban Development Plan (RUDP) initiated by the provincial government of Punjab, both in Pakistan. The land change modeler (LCM) was used to assess the land cover changes and transformations between 2000 and 2020 across Punjab and KPK. Furthermore, a curve fit linear regression model (CFLRM) and sensitivity analysis were employed to analyze the impacts of land cover dynamics on land surface temperature (LST) and carbon emissions (CE). Results indicated a significant increase in green fraction of +5.35% under the BTTP, achieved by utilizing the bare land with an effective transition of 4375.87 km2. However, across the Punjab province, an alarming reduction in green fraction cover by −1.77% and increase in artificial surfaces by +1.26% was noted. A significant decrease in mean monthly LST by −4.3 °C was noted in response to the BTTP policy, while an increase of 5.3 °C was observed associated with the RUDP. A substantial increase in LST by 0.17 °C was observed associated with transformation of vegetation to artificial surfaces. An effective decrease in LST by −0.21 °C was observed over the opposite transition. Furthermore, sensitivity analysis suggested that LST fluctuations are affecting the % of CO2 emission. The current findings can assist policymakers in revisiting their policies to promote ecological conservation and sustainability in urban planning.

Underestimation of the impact of land cover change on the biophysical environment of the Arctic and boreal region of North America

The Arctic and Boreal Region (ABR) is subject to extensive land cover change (LCC) due to elements such as wildfire, permafrost thaw, and shrubification. The natural and anthropogenic ecosystem transitions (i.e. LCC) alter key ecosystem characteristics including land surface temperature (LST), albedo, and evapotranspiration (ET). These biophysical variables are important in controlling surface energy balance, water exchange, and carbon uptake which are important factors influencing the warming trend over the ABR. However, to what extent these variables are sensitive to various LCC in heterogeneous systems such as ABR is still an open question. In this study, we use a novel data-driven approach based on high-resolution land cover data (2003 and 2013) over four million km2 to estimate the impact of multiple types of ecosystem transitions on LST, albedo, and ET. We also disentangle the contribution of LCC vs. natural variability of the system in changes in biophysical variables. Our results indicate that from 2003 to 2013 about 46% (∼2 million km2) of the region experienced LCC, which drove measurable changes to the biophysical environment across ABR over the study period. In almost half of the cases, LCC imposes a change in biophysical variables against the natural variability of the system. For example, in ∼35% of cases, natural variability led to −1.4 ± 0.9 K annual LST reduction, while LCC resulted in a 0.9 ± 0.6 K LST increase, which dampened the decrease in LST due to natural variability. In some cases, the impact of LCC was strong enough to reverse the sign of the overall change. Our results further demonstrate the contrasting sensitivity of biophysical variables to specific LCC. For instance, conversion of sparsely vegetated land to a shrub (i.e. shrubification) significantly decreased annual LST (−2.2 ± 0.1 K); whereas sparsely vegetated land to bare ground increased annual LST (1.6 ± 0.06 K). We additionally highlight the interplay between albedo and ET in driving changes in annual and seasonal LST. Whether our findings are generalizable to the spatial and temporal domain outside of our data used here is unknown, but merits future research due to the importance of the interactions between LCC and biophysical variables.

Effect of landscape pattern changes and environmental indices on land surface temperature in a fragile ecosystem in southeastern Iran.

Climate change and urbanization along with uncontrolled development in less developed countries have led to an increased ecosystems' thermal environment. Some factors such as environmental indices and landscape pattern changes can alter Land Surface Temperature (LST). Thus, the accurate evaluation of the relationship between these factors and LST is considered important for managing ecosystems, especially fragile ones under high stress. The southeast of Iran has witnessed many destructions in the environmental dimension in the past years. Moreover, this region has a low socio-economic situation, which increases the need to study in this region. In the present study, we used Landsat TM5 satellite images (1989), Landsat 8 OLI/TIRS ones (2019), and Google Earth Engine (GEE) system to prepare the maps of temporal-spatial LST changes, Land Use/Land Cover (LULC), and selected environmental indices including Normalized Difference Vegetation (NDVI), Built-up (NDBI), Water Indices (NDWI), Land Surface Moisture (LSM) and albedo. Then, the correlation levels of LST with the aforementioned indices were assessed by using Geographically Weighted Regression (GWR), as well as assessing LST variation following LULC change. In addition, the Moran index was used to analyze global and local spatial autocorrelation. The results represented an 8.67-degree increase in the mean LST during 1989-2019. Urban and built-up areas had a significant effect on increasing the temperature of the region. Additionally, water bodies and vegetation cover in the region were the most crucial parameters in LST reduction. All of the applied indices were strongly related to LST (>0.70), while some exhibited more correlation in each year. Further, the highest correlation of LST was observed with LSM and NDBI in 1989, as well as with NDVI and NDWI during 2019. In addition, the Moran index value reduced from 1989 to 2019 (from 0.93 to 0.89). Finally, the region rehabilitation based on sustainable development principles played an important role in the direct and indirect decrease in LST.

Reprint of: Influence of trees on landscape temperature in semi-arid agro-ecosystems of East Africa

In the Dodoma region of Tanzania, overexploitation of natural resources has led to a severe land degradation and, consequently, to the need to restore local ecosystems. Within the possible strategies, the farmer managed natural regeneration agroforestry is strongly being promoted in the area. To validate the utilisation of this practise, this research aims (i) to assess the benefits of trees on microclimate studying the relationship between land surface temperature (LST) and tree canopy cover (TCC) in farmlands and (ii) to propose an operative tree density threshold to be set as objective by restoration activities in the area. Data collected with an unmanned aerial vehicle were integrated with Landsat 8 and Sentinel 2 satellite imagery. LST was retrieved through the practical single channel algorithm and TCC was calculated with a supervised classification. Pixel-based analyses at a resolution of 30 m and 10 m were performed, and the resulting data statistically analysed through the coefficient of determination, the Kruskal–Wallis test, and the Dunn test. Results showed that the TCC of the surveyed areas was 5.1%, and a significant decrease in LST of 1.32 °C (p < 0.01) was found in the areas with the highest TCC, during the late dry season. From these preliminary analyses, a threshold of 10% TCC is suggested for farmlands in this agroecological zone to bring beneficial microclimate changes in terms of temperature. Considering the average plant phenotype characteristic of the area, it corresponds to a tree density of 50 trees ha−1.

An Analyze of Urban Temperature Using Energy Balance Algorithm for Land (SEBAL) in Yogyakarta City

This study examines the Land Surface Temperature (LST) using the Surface Energy Balance Algorithm for Land (SEBAL) model in Yogyakarta. SEBAL is relied upon for its accurate LST predictions because it takes into account the influence of vegetation and soil. This study identified LST in various land cover/land use (LULC) types extracted from Landsat 8 remote sensing images recorded in April 2019 (wet day) and June 2019 (dry day). The LULC classification results in the study area show that built-up land is the dominant land use, with 93.30% of the total area, and the rest is non-developed land (vegetation, open land, and water body). The average LST value on a wet day is 26.79 °C, while on a dry day, it is 30°C. The highest temperature occurs on the dry day, 35.17 °C, and the lowest on the wet day, which is 13.63°C. The correlation between LST and LULC shows the same pattern on the two different days, in which the value of vegetation temperature is lower than that of open and developed land. This research proves that vegetation influences a decrease in land surface temperature. Judging from the dominant land use being the built-up area in Yogyakarta, urban planners need to consider increasing green open spaces.

Effects of heavy grazing on the microclimate of a humid grassland mountain ecosystem: Insights from a biomass removal experiment

In high-altitude Andean grasslands (páramo), overgrazing leads to alterations in both vegetation and microclimate. These alterations need to be identified to devise land management strategies that will preserve and enhance ecosystem processes. To elucidate this issue, we designed an overgrazing experiment: we selected two plots covered with native grass (pajonal), in one of which we mowed to the ground surface. We left the second plot undisturbed to serve as a control. For both plots, we continuously monitored albedo and ancillary energetic components to generate quarterly and yearly comparisons for the following parameters: (a) impacts on albedo and resilience of grass; (b) radiative forcing of albedo; and (c) land surface temperature feedback during the recovery period. In the first quarter following removal, when the soil was covered with light litter, median albedo increased 38.81% (0.16 ± 0.02), then began a gradual decrease, which continued until its full recovery 1.75 years later (0.10 ± 0.01). During the first year of the experiment, a strong mean negative instantaneous radiative forcing was observed (−7.08 ± 6.03 Wm−2), signifying a reduction in net shortwave energy. This forcing returned to normal, pre-intervention conditions (−0.55 ± 0.97 Wm−2) after 1.75 years, equal to the energetic recovery period of the grass. Both the amount (from 133.0 ± 44.72 to 119.67 ± 39.30 Wm−2) and the partitioning (net shortwave decreased 5%; net longwave increased 9.7%) of net energy were altered after removal, evidence of cooling feedback during the recovery period. This feedback indicated that the decrease in albedo (1.25%) or instantaneous radiative forcing (−4.67 Wm−2) resulted in a decrease in land surface temperature of 1 °C. Thus, our overgrazing experiment without soil destruction followed by a natural recovery time has identified the energetic recovery period for grass in the páramos; suggesting the albedo as a good indicator of grass resilience.

Influence of trees on landscape temperature in semi-arid agro-ecosystems of East Africa

In the Dodoma region of Tanzania, overexploitation of natural resources has led to a severe land degradation and, consequently, to the need to restore local ecosystems. Within the possible strategies, the farmer managed natural regeneration agroforestry is strongly being promoted in the area. To validate the utilisation of this practise, this research aims (i) to assess the benefits of trees on microclimate studying the relationship between land surface temperature (LST) and tree canopy cover (TCC) in farmlands and (ii) to propose an operative tree density threshold to be set as objective by restoration activities in the area. Data collected with an unmanned aerial vehicle were integrated with Landsat 8 and Sentinel 2 satellite imagery. LST was retrieved through the practical single channel algorithm and TCC was calculated with a supervised classification. Pixel-based analyses at a resolution of 30 m and 10 m were performed, and the resulting data statistically analysed through the coefficient of determination, the Kruskal–Wallis test, and the Dunn test. Results showed that the TCC of the surveyed areas was 5.1%, and a significant decrease in LST of 1.32 °C (p < 0.01) was found in the areas with the highest TCC, during the late dry season. From these preliminary analyses, a threshold of 10% TCC is suggested for farmlands in this agroecological zone to bring beneficial microclimate changes in terms of temperature. Considering the average plant phenotype characteristic of the area, it corresponds to a tree density of 50 trees ha −1 . • LST decrease of 1.32 °C found in areas with high TCC in the late dry season. • 10% TCC threshold, or 50 trees ha −1 should be reached using restoration. • Integration of Landsat 8 with Sentinel 2 data gave improved analysis.

The Social Restrictions Impact on Urban Heat Island Phenomena (Case Study: Cities in Java Island)

The Coronavirus Disease 2019 (Covid-19) pandemic has hit Indonesia since March 14, 2020. The rapid spread of the virus has caused the central and regional governments to implement community activity policies. Some terms and methods used by local governments such as PSBB (the Large-Scale Social Restrictions) are applied in Special Capital Region of Jakarta and Surabaya City, in Semarang City has PKM (Restrictions on Community Activities). This study aims to analyze the impact of the social restrictions on Urban Heat Island (UHI) in the Java Island big city. This research was conducted in big cities on Java Island that apply social restrictions, namely Special Capital Region of Jakarta, Bandung, Semarang, Yogyakarta, Surakarta, Surabaya, and Malang. The data used are Landsat 8 satellite imagery in 2019 and 2020. The method used is to compare the magnitude of the Land Surface Temperature (LST) and UHI before and after social restrictions. The results of the analysis explain that there is a decrease in LST and changes in UHI in the cities of Special Capital Region of Jakarta, Bandung, Semarang, Surakarta, and Yogyakarta. However, in Surabaya and Malang, there was an increase in LST. This study concludes that the implementation of social restrictions affects changes in UHI and decreases LST.

Modeling Spatio-Temporal Land Transformation and Its Associated Impacts on land Surface Temperature (LST)

Land use land cover (LULC) of city regions is strongly affected by urbanization and affects the thermal environment of urban centers by influencing the surface temperature of core city areas and their surroundings. These issues are addressed in the current study, which focuses on two provincial capitals in Pakistan, i.e., Lahore and Peshawar. Using Landsat data, LULC is determined with the aim to (a) examine the spatio-temporal changes in LULC over a period of 20 years from 1998 to 2018 using a CA-Markov model, (b) predict the future scenarios of LULC changes for the years 2023 and 2028, and (c) study the evolution of different LULC categories and investigate its impacts on land surface temperature (LST). The results for Peshawar city indicate the significant expansion in vegetation and built-up area replacing barren land. The vegetation cover and urban area of Peshawar have increased by 25.6%, and 16.3% respectively. In contrast, Lahore city urban land has expanded by 11.2% while vegetation cover decreased by (22.6%). These transitions between LULC classes also affect the LST in the study areas. Transformation of vegetation cover and water surface into built-up areas or barren land results in the increase in the LST. In contrast, the transformation of urban areas and barren land into vegetation cover or water results in the decrease in LST. The different LULC evolutions in Lahore and Peshawar clearly indicate their effects on the thermal environment, with an increasing LST trend in Lahore and a decrease in Peshawar. This study provides a baseline reference to urban planners and policymakers for informed decisions.

Investigating the Effect of Lockdown During COVID-19 on Land Surface Temperature: Study of Dehradun City, India

Urban environment imposes challenges due to its dynamics and thermodynamic characteristics of the built environment. The present study aims to study the effect of lockdown during COVID-19 on the spatio-temporal land surface temperature (LST) patterns in Dehradun city. The TIRS sensor data of 14 April 2020 (post-lockdown), 28 April 2019, 25 April 2018 and 08 May 2017 were downloaded, and LST was retrieved using radiative transfer equation. The wardwise change in LST, urban hot spots and thermal comfort was studied as a function of built-up density. It was observed that there was an overall decrease in LST values in Dehradun city in post-COVID lockdown period. Wards with high built-up density had minimum decrease in LST; on the contrary, wards with large proportion of open spaces and having low, medium built-up density had the maximum decrease in LST. Hot spot analysis was carried out using Getis Ord GI* statistic, and the level of thermal comfort was found using the urban thermal field variance index. It was observed that there was an increase in number of hot spots accompanied by a decrease in thermal comfort level post-lockdown. The methodology proposed in the present study can be applied to other Indian cities which exhibit similar growth patterns and will provide a tool for rational decision making.

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

Sand and dust storm sources identification: A remote sensing approach

Abstract In recent years, the sand and dust storm (SDS) events have become one of the most critical environmental challenges around the world. Identification of the sources not only helps to monitor and predict the processes of dust storms, but also it contributes to the reduction of its negative impacts and management of this phenomenon in a better manner. Identifying the sources based on field observation is impossible due to the vast extent of these lands, as well as the limitation of access to most of their areas, so remote sensing data can be used as a reliable alternative. The primary objective of this study is developing a comprehensive approach for sand and dust storm source identification and surveying their changing trend during a specific period via remotely sensed data. For this purpose, to generate wind erosion sensitivity maps based on the vegetation cover, soil moisture and land cover, Landsat8 data from 2013 to 2015 were acquired. After undergoing pre-processing on Landsat data with the help of spectral vegetation indexes and classification, wind erosion sensitivity maps were obtained for vegetation, soil moisture, and land use. To obtain a potential dust source map, the previously generated maps were integrated with geology and soil roughness information through multi-criteria evaluation to produce a wind erosion sensitivity map. Then, the synoptic data and air-quality information data were collected and using statistical analysis and MODIS data, local dust and sand events were identified, which were then validated based on HYSPLIT air flow simulation model to ensure that airflow trajectory and erodible lands are in physical contact. Based on the contact areas between airflow and the terrain besides applying non-erodible masks on them, the wind erosion risk was mapped. Next, the wind erosion risk map and the wind erosion sensitivity map via fuzzy multi-criteria assessment through linear weighted method were combined, then based on stratified-random sampling, probable SDS sources were identified. To validate the identified SDS sources and survey their trend, time series and synoptic data were utilized and vegetation cover, soil moisture and land surface temperature (LST) trend in specified areas for 15 years were monitored. Validation results showed high accuracy in identifying the areas; moreover, they confirmed the significant decrease in the vegetation cover, soil moisture and LST in the SDS sources during the study period (the last 15 years). Our results showed the high capability of time series analysis of remotely sensed data. LST as a climatic parameter has a crucial role to identify and validate the SDS sources. For instance, in areas with high SDS frequency, a significant decrease in LST is observable and confirmed by AOD analysis results. In this research, we integrated the most critical data that can be effective in identifying dust sources. To validate these sources, we used time series satellite data to measure the power of these data. Finally, we proposed the whole procedure as a complete process. Hence, the applied method in this research can be used as a comprehensive approach for future studies in SDS source identification by remotely sensed data.

Land surface temperature variations and their relationship to fractional vegetation coverage in subtropical regions: A case study in Fujian Province, China

ABSTRACTModerate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data of MODIS 11A2 and Normalized Difference Vegetation Index (NDVI) products of MODIS 13Q1 were used to analyse the spatial and temporal changes of LST and its relationship with vegetation from 2001 to 2015 in the subtropical region of Fujian Province, China. The LST and NDVI products were reconstructed by Savitzky–Golay (S-G) filtering for abnormal values or missing data due to the influence of cloud contamination. The LST of Fujian decreases from its southeast coast to its northwest mountain area, and the annual average highest LST is greater than 30°C while the lowest LST is about 15°C. In the past 15 years, the average LST in Fujian has declined gradually due to an increase in fractional vegetation coverage (FVC), but the highest LST increased from 30.95°C to 33.05°C across south-east coastal zone cities due to rapid urbanization. From 2001 to 2015, the LST of Fujian Province trended slightly downward; the LST area, however, significantly decreased by 18.85% of the total area of the province and was twice that of the (significant) increase in LST. The changing trend distributions of LST are opposite to those of FVC in general, but LST decreases to greater extent as FVC increases when FVC is above 0.2 – every 10% increase in FVC is accompanied by about a 1.1°C decrease in LST.

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

Land Surface Cooling Induced by Sulfate Geoengineering Constrained by Major Volcanic Eruptions

AbstractSolar radiation management by stratospheric aerosol injection (SRM‐SAI) has been proposed as a possible method to counteract anthropogenic global warming, with climate models suggesting it could reduce substantially global temperature and associated impacts. Its effectiveness as simulated by Earth system models exhibits, however, large uncertainties, implying high risks for natural and human ecosystems. Here we identify an emergent relationship linking the long‐term global land surface cooling due to SRM‐SAI and the short‐term cooling following the twentieth century major volcanic eruptions across an Earth system models ensemble. This emergent relationship, combined with observations and reanalysis data, is used to constrain the global land surface temperature (LT) response to reduced downward solar radiation. Based on these constraints, we find a mean decrease in land surface temperature of 0.44 K·W−1·m2, 20% smaller than the unconstrained multimodel mean. This new estimate may affect how trade‐offs between cost, risk, and effectiveness of SRM‐SAI might be considered.