Land Surface Temperature Anomalies Research Articles

Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate

The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity.

The Dominant Modes of Spring Land Surface Temperature Over Western Eurasia and Their Possible Linkages With Large‐Scale Atmospheric Teleconnection Patterns

AbstractPrevious studies suggest that the spring land surface temperature (LST) anomaly over Western Eurasia (WEA; 20°N–70°N, 0–70°E) is a potential driver for Asian even global climate. However, the interannual variability of LST over WEA together with its causes has not received enough attention. Based on multi‐source observations and reanalysis data, three dominant modes of spring LST over WEA were identified, and the roles of atmospheric teleconnection patterns in LST anomalies via altering cloud and water vapor were investigated from the perspective of surface energy budget. The first empirical orthogonal function (EOF1) mode exhibits a homogeneous warming pattern that is closely related to a negatively in‐phase combination of the Scandinavian and East Atlantic/Western Russia (EA/WR) patterns. Under the control of a predominant anomalous anticyclone, the warming is the result of enhanced downward longwave radiation due to more water vapor in a warmer atmosphere associated with anomalous southerly and more incident solar radiation due to reduced cloud cover. The EOF2 mode, which displays a meridional dipole structure, is dominated by the North Atlantic Oscillation. Coupled with an anomalous cyclone and anticyclone over western Europe and around the Caspian Sea, respectively, the former LST anomalies are primarily caused by cloud‐shortwave radiation processes, whereas the latter are dominated by downward longwave radiation due to water vapor. The EOF3 mode, characterized by a zonal dipole pattern, is dominated by the water vapor greenhouse effect related to the Eurasian pattern. The results of the study may provide a valuable signal for climate prediction.

Spatial effect of anti-COVID measures on land surface temperature (LST) in urban areas: A case study of a medium-sized city

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Târgu Mureș (Marosvásárhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 – May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that – contrary to previous studies carried out on cities in China and India – in most of the urban areas of Marosvásárhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000–2019. Remote sensing data from the MODIS and the Landsat satellites show, that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 °C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 °C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 °C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1–2 °C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons, and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvásárhely had many effects on LST in particular areas that have links to the local economy, trade, and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni

Land surface temperature (LST) and vegetation cover changes are two indicators of landscapes in a region. The relationship between LST anomalies, elevation, vegetation, and urban growth is significant to conservation. This study addresses this issue using night-time satellite imagery, kernel methods (points aggregation), and the trend analysis for a long-term period (2001–2017) in Iran. Variables for two seasons (summer and winter) in urban and natural land uses were derived using the Google Earth Engine (GEE) and NASA's Giovanni. Point data derived from raster maps were quantified using statistical kernel and trend analysis. As result, it was observed that LST rise in various elevations, seasons, and land uses. The LST was analyzed through kernels (point aggregation in scatter graphs), which shifted to the right. The LST anomaly in the daytime had the highest maximum value (>4 °C) and lowest minimum value (<-5 °C) in forests and mountains and metropolises with the highest population growth rate. Summer and winter seasons had positive trends in LST for forest and mountain land uses. All seasons had positive trends in EVI in the mountain, and desert land uses. This warming and increasing LST can increase vulnerability to drought, dust storms, floods, avalanches, and natural fires. The EVI is increasing over the years due to government projects in green spaces and urban parks. There is a need to protect urban and natural environments to prevent natural disasters and unplanned population growth.

Dust storm index anomaly for sand-dust events monitoring in western Iran and its association with the NDVI and LST anomalies

Sand-dust events (SDE) are an increasing concern in many arid and semi-arid regions of the world, which have severely damaged air quality and human health in recent years. This study was conducted to monitor the SDE in western Iran using the dust storm index anomaly (DSIA) during 2000-2018. The spatio-temporal change detection and statistical analysis were used to understand the impacts of normalized difference vegetation cover anomaly (NDVIA) and land surface temperature anomaly (LSTA) on the SDE activities. The area has suffered from the highest dust pollution in 2004, 2009, and 2012 (DSIA>+40) while it experienced the lowest dust pollution in 2002 and 2017 (DSIA<-40). Approximately 48% of western Iran experienced decreasing changes and 52% of the total area experienced increasing changes in dust pollution during 2010-2018 compared to the previous years. Incremental changes in NDVIA and LSTA were observed in 73.2% and 7.5% of the study area while their decreasing changes were observed in 26.8% and 92.5% of the total area, respectively. Spatially, regions affected by the increase in dust pollution are mainly distributed in the eastern and southern regions of the study area. Significant effects of changes in anomalies of both terrestrial parameters on DSIA were observed throughout the study period ((RLSTA-DSIA= +0.52; RNDVIA-DSIA= -0.41); P<0.05). It was also found that spatial correlation between LSTA and DSIA, as well as NDVIA and DSIA in many parts of the study area, was significant at the 95% confidence level (|R| > 0.45). These findings can be useful for decision-makers to assess the risks of dust pollution and reduce its negative consequences in western Iran.

Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

Accuracy assessment and scale effect investigation of UAV thermography for underground coal fire surface temperature monitoring

Coal fire is a worldwide disaster that wastes massive energy and seriously pollutes the environment. Accurate acquisition of abnormal LST (land surface temperature) caused by underground coal fire is essential for coal fire monitoring and extinguishing. As a remote sensing technique, UAV (unmanned aerial vehicle) thermography can obtain LST images with very high spatial resolution and it has been used for coal fire monitoring. However, the accuracy of the UAV thermography obtained LST images (i.e., UAV LST images) has not yet been well studied, and the scale effect of UAV thermography for coal fire monitoring has not been discussed in previous studies. To this end, this study evaluates the accuracy of UAV LST images of coal fire areas based on the corresponding ground measurements. After that, the acquired UAV LST images are upscaled to different resolutions to simulate the LST images obtained at different observation scales. Finally, the local variance and Shannon entropy are employed to determine the optimal LST anomaly observation scale and coal fire area extraction scale. Baoan coalfield fire area, which is in Xinjiang province of China, is selected as the study area. The results show that the linear regression correlations R2 between UAV LST images and the LST values measured by thermal imaging camera and the infrared thermometer are both higher than 0.99. RMSE (Root Mean Square Error) between the thermal imaging camera LST measurements and that of UAV is 2.1 °C. When UAV LST images’ resolutions are better than 7.5 m, most of the LST anomalies can be detected, and the LST anomaly information loss is relatively small (less than 17%). The resolution of 4 m is the required lowest resolution to accurately extract the areas of coal fire. When the resolution is lower than 4 m, the high-temperature abnormal boundaries caused by coal fire are being blurred, making the extraction of coal fire combustion areas unreliable.

Evaluating the role of urban fabric on surface urban heat island: The case of Istanbul

Urban heat islands, one of the fundamental anthropogenic impacts on local climates, have been a growing concern especially for high-density urban areas such as Istanbul. This paper outlines the use of a supervised machine learning technique to understand the effects of the urban fabric on surface urban heat island (SUHI) formation in Istanbul, and identify effective variables to provide a basis for research and practice focusing on SUHI mitigation. An analysis using the Ridge Regression Model found that 71% of land surface temperature anomalies in Istanbul are linked to building coverage ratio (BCR), surface/volume ratio (SVR), sky-view factor (SVF), canyon geometry factor (CGF), and vegetation index (NDVI). NDVI and BCR were the urban fabric components with the highest contribution to SUHI formation, while the effects of SVF and CGF remained relatively low. This research can help planners and designers gauge the contribution of the urban fabric to micro-climate issues and adapt SUHI mitigation strategies for projects aiming to build climate-sensitive urban environments. It also provides insight into and improves knowledge of supervised machine learning approaches to the urban planning and design disciplines.

Artificial Neural Network based thermal anomalies associated with earthquakes in Pakistan from MODIS LST

There are several reports about thermal anomalies associated with the impending earthquakes (EQs); among which the underlying mechanism is linked with the main shock with long, intermediate and short-term precursors. In this paper, we analyzed thermal anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) based Land Surface Temperature (LST) of three different magnitudes and shallow depth EQs in Pakistan. Our focus is to investigate the thermal anomalies by the statistical approach and Artificial Neural Network (ANN) in spatial and temporal LST values within 10 days before and after the main shock as short-term precursors. After implementing statistical and the ANN approach, LST revealed that thermal anomalies occurred within 1–10 days before the main shock of Mw > 6.0 EQs. However, a low intensity LST anomaly is also recorded within 20–25 days before the main shock of Mw 5.2 EQ. We study that LST anomalies are magnitude and depth dependent and it is more likely to occur before EQ of Mw > 6.0 and shallow depth within 10 days before/after the main shock day. The results depict that Mw 5.2 EQ anomaly is not clearly associated to the main shock, as it locates outside the window of 1–10 days before/after the main shock. Similarly, two out of three events caused post-seismic thermal anomalies of less magnitude as compared to the pre-seismic thermal anomalies. The pre-seismic LST anomalies occur with high intensity before shallow depth and large magnitude EQs than post seismic LST anomalies. The LST anomalies occurred before all the EQs suggesting it to be a reliable precursor of short to intermediate interval associated with an impending EQ.

Satellite-based hydrocarbon exploration employing ASTER and fuzzy logic

Abstract Despite the known practical applications of remote sensing in a wide range of industries and situations, it has not been used extensively in petroleum exploration, which has relied mostly on geological and / or geophysical surveys. With the advances made in sensing equipment since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) became operational, extraction of potential indicators such as: surface emissivity, surface kinetic temperature, brightness temperature and surface radiance need to be re-evaluated in the context of petroleum exploration. Land Surface Temperature (LST) anomalies associated with geothermal activity was used, to determine if petroleum in place, can be the source of thermal anomalies detected at surface, by ASTER. A study area, in onshore Iran, consisting of 53 existing petroleum-rich areas was selected. Twenty (20) ASTER scenes (Granules) covering the study area were provided by National American Space Agency (NASA). In order to highlight subsurface contributors to geothermal anomalies detected at surface, assumed to be due to underlying hydrocarbons, ASTER images were processed to minimize temperature variations caused by any sources other than the underlying hydrocarbons. In order to assign weight to the role-playing variables according to their degree of influence over the final LST, fuzzy logic was employed, as the main processing approach. In the resulting processed maps, following the removal of all the contributors to LST, the remaining thermal anomalies, which were reduced up to 80% in some areas of the study area, could then be linked to the underlying hydrocarbons. The final results indicate that, even after the influence of all the contributors to LST has been removed, still 67.9% (36 out of 53 petroleum containing areas) of the pixels within the buffer zones of petroleum reservoirs of the study area are considered to be thermally-anomalous. Continuity of thermal anomalies, in different directions, was also investigated within the buffer zones of each of the petroleum reservoirs, by Variograms. The results of the Variogram analysis indicate that even if the detected thermal anomalies within the buffer zones of some of the reservoir of the study area were not high, the direction of thermal anomalies strongly follow the NW-SE direction, which is the direction along the trend line of elongation axes of petroleum reservoirs. Details of the spatial analysis post LST processing will be reported. By employing GIS and fuzzy logic, a dynamic model was developed, with variable input data from geology and ASTER, which could be customised and changed according to target area's thermal specifications and characteristics.

Enhanced drought detection and monitoring using sun-induced chlorophyll fluorescence over Hulun Buir Grassland, China

Drought is one of the most damaging events in the grassland ecosystem. The detection and monitoring of drought are very important to maintain the balance of the grassland ecosystem. The potential of Sun-induced Chlorophyll Fluorescence (SIF) for drought detection and monitoring were explored in this study. Based on significant negative anomalies of self-calibrating Palmer drought severity index (scPDSI), precipitation (PPT), soil moisture (SM)，surface water storage (SWS), and a significant positive anomaly of land surface temperature (LST), a severe drought event was accurately detected from June to August in 2016 over Hulun Buir Grassland. The far-red SIF was decomposed into its mechanical parts such as SIF, absorbed photosynthetically active radiation (APAR), normalized by APAR (SIFyield), physiological SIF emission yield (SIFpey), and total emitted SIF (SIFte), which were more sensitive to drought than the vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), modified soil adjusted vegetation index (MSAVI2), and near-infrared reflectance of vegetation (NIRV). SIF and NIRV represented the SIF indicators and the VIs, respectively, which were most affected by drought, with a decrease of −2.67% and 4.19% in June, 50.93% and 31.76% in July, and 55.58% and 39.44% in August. The correlations between anomalies of SIF indicators, VIs, and anomalies of LST, wind speed (WS) were a strong negative correlation, indicating that their reduction was caused by the anomalies of LST and WS. Moreover, the SIF indicators had a shorter lag time in response to meteorological drought than VIs. Besides, the correlations between SIF-based drought indices such as drought fluorescence monitoring index (DFMI), SIF health index (SHI), and SM were − 0.709 and − 0.783 (P < 0.01), respectively, higher than the conventional drought indices. Moreover, DFMI and SHI could reflect the changes of SM in advance, while the conventional drought indices mostly lagged behind the changes of SM. This study shows that SIF can enhance drought detection, and the SIF-based drought index can be well suitable for drought monitoring.

Sensitivity evaluation of the different physical parameterizations schemes in regional climate model RegCM4.5 for simulation of air temperature and precipitation over North and West of Iran

In this research the dynamic downscaling method by Regional Climate Model (RegCM4.5) was used to assess the performance and sensitivity of seasonal simulated North and West of Iran (NI&WI) climate factors to different convection schemes, and transforms the large-scale simulated climate variables into land surface states over the North of Iran (NI) and West of Iran (WI). A 30-year (1986–2015) numerical integration simulation of climate over NI&WI was conducted using the regional climate model RegCM4.5 nested in one-way ERA-Interim reanalysis data. The Grell, Kuo and MIT-Emanuel cumulus convection with Holtslag and University of Washington (UW) planetary boundary layer (PBL) parameterization schemes were applied in the running of RegCM4.5 to test their capability in simulating precipitation and temperature in winter-spring (January–April) over NI and WI. The results demonstrated that the RegCM4.5 model has a good potential for simulating the variables and trend of surface temperature over the NI and WI region. Magnitude of the model bias for land surface temperature over different regions of Iran varies by convection parameterization schemes. In most cases, the root mean square error between post-processed simulated seasonal average temperature and observation value was less than 1 °C, but there is a systematic “cold bias”. In general, with respect to land surface temperature simulations, a better performance is obtained when using post-processing model’s data with Holtslag PBL-Grell and Holtslag PBL-Kuo configuration schemes, compared to the other simulations, over the NI&WI region. Also, the UW PBL convection schemes show a relatively excellent spatial correlations and normalized standard deviations closer to 1 for thirty-year seasonal land surface temperature anomalies over the entire NI&WI region. However, the simulation accuracy of model for precipitation is not as optimal as for temperature. The dominant feature in model simulations is a dry bias with the largest average value (∼1.04 mm/day) over NI region, while the lowest mean bias precipitation (∼−0.47 mm/day), mainly located in WI region. In the comparison of six configuration convection schemes, the Emanuel scheme has been proven to be the most accurate for simulating winter-spring seasonal mean precipitation over NI&WI region. The accuracy of the scheme also showed great difference in simulated station interpolation of precipitation, which urges the improvement for the simulation capability of spatial distribution of precipitation. In general, for seasonal variation of precipitation, the Emanuel convection with two (Holtslag, UW) PBL configuration schemes outperforms with a good correlation score between 0.7−0.8 and normalized standard deviations closer to 1.

Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC

Identifying atmospheric and ionospheric anomalies based on remote sensing satellites has contributed highly to develop the hypothesis of lithosphere-atmosphere-ionosphere coupling over the earthquake (EQ) epicenter during the seismic preparation period. This article has investigated the variations of potential EQ precursor in daytime and nighttime land surface temperature (LST) before and after the 2019 Pakistan EQ from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The nighttime LST values of MODIS exhibit temporal anomalies during nighttime period within a time window of five days before and after the main shock day. Furthermore, the LST values predicted by artificial neural network (ANN) validate the significant enhancement in nighttime time series of MODIS. The nighttime LST anomalies obtained from the observation and ANN prediction are more than 20% and 7% of normal distribution beyond the confidence bounds, respectively, within five days after the main shock. Likewise, the ionospheric anomaly from daily total electron content (TEC) values at Sukkur Global Positioning System (GPS) station confirms the EQ associated ionospheric perturbations on the day after the main shock. The Global Ionospheric Maps (GIMs) also show the TEC anomalies during 1000–1400 LT on September 25, 2019.

Monitoring coal seam fires in Xinjiang using comprehensive thermal infrared and time series InSAR detection

ABSTRACT The combustion of underground coal seams has important effects on the national economy and strategic energy security of countries, and damages the environment, ecology, vegetation, and water resources. Xinjiang, China, is one of the most serious areas of coalfield fires in the world, and it is difficult for any single remote sensing detection method to meet the practical requirements for accurate detection of fire areas. In order to detect coalfield areas in Xinjiang more accurately and reliably, a detection method that integrates thermal infrared and radar remote sensing anomaly is proposed: The mono-window algorithm is used to retrieve the Land Surface Temperature (LST) using images from the Land Remote-Sensing Satellite System (Landsat) 8, the Adaptive Edge Threshold Algorithm (AETA) is used to extract the data characterized by LST anomalies, and Small Baseline Subsets Interferometric Synthetic Aperture Radar (SBAS-InSAR) is used to obtain the land surface subsidence information from Sentinel-1A images. Through the overlay analysis of the above-mentioned LST and subsidence information, the extraction of possible coal fire areas in the Jiangjun Gobi area of Xinjiang is realized. As well as according to the characteristic that underground coal fires melt snow in winter, areas where coal fires are unlikely to occur are excluded using the natural, true-colour images of Landsat 8. Finally, the proposed method is verified using the characteristics of the time series cumulative subsidence of the land surface in areas where coal fires are likely to occur, and by examining the coupling between subsidence velocity and anomalies in LST data. The results show that the proposed method can overcome the shortcomings of single-factor remote sensing detection to a certain extent, and has the ability to detect fire areas more accurately.

Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale

ABSTRACT Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning.

Land Surface Temperature Anomalies Detection for the Strong Earthquakes in 2018

Earthquake every year leads to human and material losses and unpredictability of it by now makes this natural disaster worsen. The objective of the current study was to determine the anomalies in land surface temperature (LST) in areas affected by earthquakes. In this research, three earthquakes (M &gt;6) were studied. Moderate Resolution Imaging Spectroradiometer Aqua and Terra day and night LST data used from 2003 to 2018. The interquartile range (IQR) and mean ± 2σ methods utilized to select anomalies. As a result, based on the IQR method, no prior and after anomaly detected in selected cases and data. Based on mean ± 2σ, usually positive anomaly occurred during daytime. However, negative (or positive) anomaly occurred during the nighttime before the Mexico and Bolivia earthquakes. During 10 days after the earthquake, sometimes a negative anomaly detected.

The Effects of Historical Housing Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 US Urban Areas

The increasing intensity, duration, and frequency of heat waves due to human-caused climate change puts historically underserved populations in a heightened state of precarity, as studies observe that vulnerable communities—especially those within urban areas in the United States—are disproportionately exposed to extreme heat. Lacking, however, are insights into fundamental questions about the role of historical housing policies in cauterizing current exposure to climate inequities like intra-urban heat. Here, we explore the relationship between “redlining”, or the historical practice of refusing home loans or insurance to whole neighborhoods based on a racially motivated perception of safety for investment, with present-day summertime intra-urban land surface temperature anomalies. Through a spatial analysis of 108 urban areas in the United States, we ask two questions: (1) how do historically redlined neighborhoods relate to current patterns of intra-urban heat? and (2) do these patterns vary by US Census Bureau region? Our results reveal that 94% of studied areas display consistent city-scale patterns of elevated land surface temperatures in formerly redlined areas relative to their non-redlined neighbors by as much as 7 °C. Regionally, Southeast and Western cities display the greatest differences while Midwest cities display the least. Nationally, land surface temperatures in redlined areas are approximately 2.6 °C warmer than in non-redlined areas. While these trends are partly attributable to the relative preponderance of impervious land cover to tree canopy in these areas, which we also examine, other factors may also be driving these differences. This study reveals that historical housing policies may, in fact, be directly responsible for disproportionate exposure to current heat events.

An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data

Abstract. Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5∘×0.625∘) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

Land surface temperature anomalies in response to changes in forest cover

Land cover/use changes specially the forest cover changes affect the local surface temperature (LST) of the earth. In this study, a combination of remote sensing and GIS techniques was used to scrutinize the interactions between LST anomalies and deforestation in Sardasht County, NW Iran. The land cover/use change layers of the study area were extracted from Landsat satellite imagery based on Binary Encoding classification and change detection technique. The radiometric correction analysis were done for each Landsat image to derive LST map layers. According to the results, a descending trend in forest cover with a total 2560 ha decline in area and an ascending trend of about 4 degrees rise in surface temperature values on both forest and non-forest areas were detected in the study area from 1984 to 2017. The temporal and spatial analysis yielded high rates of reverse temporal correlation (-0.81) between forest areas and LST anomalies while the correlation value of 0.76 was found for non-forest areas and LST. The regression analysis of the values confirmed the correlation results to be trustable at 99 percent. It was also found that the deforested areas of the study area correlate with the LST rise spatially with a very high correlation (0.98) from which a tangible interaction of the parameters can be inferred.

Recent land surface temperature patterns in Antarctica using satellite and reanalysis data

Antarctica is among the most important regions when it comes to observing the effects of climate change. One important part of the variability of the land surface temperature (LST) observed on this continent is related to the sea surface temperature of the Tropical Pacific Ocean, which is associated with El Niño-Southern Oscillation (ENSO). This large-scale phenomenon is called tele-connection. In this study, we investigate the recent trends of LST in Antarctica over the last few decades and its relationship with one of the most intense El Niño events in 2015–2016. LST anomalies derived from satellite data (MODIS) and skin temperature from re-analysis (ERA-5 and ERA-Interim) were used, in addition to the Oceanic Niño Index (ONI) and Southern Annular Mode (SAM) time series. A non-parametric time series analysis was carried out to estimate LST trends over different zones of Antarctica. The results show a warming trend obtained from ERA-5 of about 0.9 K/decade in the interior of Antarctica, while MODIS showed the lowest temperature trends of −1 K/decade in East Antarctica. The analysis between LST and the SAM index shows a negative relationship in the extreme periods of ENSO 2015–2016, showing colder autumns and warmer springs than in previous years. These results aide in understanding the effects of extreme phases of ENSO on Antarctica, which are of great importance given the projection of more intense phases over the next century as foreseen in future scenarios of climate change.