Satellite-derived Land Surface Temperature Research Articles

Improving Mean Minimum and Maximum Month-to-Month Air Temperature Surfaces Using Satellite-Derived Land Surface Temperature

Month-to-month air temperature (Tair) surfaces are increasingly demanded to feed quantitative models related to a wide range of fields, such as hydrology, ecology or climate change studies. Geostatistical interpolation techniques provide such continuous and objective surfaces of climate variables, while the use of remote sensing data may improve the estimates, especially when temporal resolution is detailed enough. The main goal of this study is to propose an empirical methodology for improving the month-to-month Tair mapping (minimum and maximum) using satellite land surface temperatures (LST) besides of meteorological data and geographic information. The methodology consists on multiple regression analysis combined with the spatial interpolation of residual errors using the inverse distance weighting. A leave-one-out cross-validation procedure has been included in order to compare predicted with observed values. Different operational daytime and nighttime LST products corresponding to the four months more characteristic of the seasonal dynamics of a Mediterranean climate have been considered for a thirteen-year period. The results can be considered operational given the feasibility of the models employed (linear dependence on predictors that are nowadays easily available), the robustness of the leave-one-out cross-validation procedure and the improvement in accuracy achieved when compared to classical Tair modeling results. Unlike what is considered by most studies, it is shown that nighttime LST provides a good proxy not only for minimum Tair, but also for maximum Tair. The improvement achieved by the inclusion of remote sensing LST products was higher for minimum Tair (up to 0.35 K on December), especially over forests and rugged lands. Results are really encouraging, as there are generally few meteorological stations in zones with these characteristics, clearly showing the usefulness of remote sensing to improve information about areas that are difficult to access or simply with a poor availability of conventional meteorological data.

Geothermal Anomaly Mapping Using Landsat ETM+ Data in Ilan Plain, Northeastern Taiwan

Geothermal energy is an increasingly important component of green energy in the globe. A prerequisite for geothermal energy development is to acquire the local and regional geothermal prospects. Existing geophysical methods of estimating the geothermal potential are usually limited to the scope of prospecting because of the operation cost and site reachability in the field. Thus, explorations in a large-scale area such as the surface temperature and the thermal anomaly primarily rely on satellite thermal infrared imagery. This study aims to apply and integrate thermal infrared (TIR) remote sensing technology with existing geophysical methods for the geothermal exploration in Taiwan. Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) imagery is used to retrieve the land surface temperature (LST) in Ilan plain. Accuracy assessment of satellite-derived LST is conducted by comparing with the air temperature data from 11 permanent meteorological stations. The correlation coefficient of linear regression between air temperature and LST retrieval is 0.76. The MODIS LST product is used for the cross validation of Landsat derived LSTs. Furthermore, Landsat ETM+ multi-temporal brightness temperature imagery for the verification of the LST anomaly results were performed. LST Results indicate that thermal anomaly areas appear correlating with the development of faulted structure. Selected geothermal anomaly areas are validated in detail by field investigation of hot springs and geothermal drillings. It implies that occurrences of hot springs and geothermal drillings are in good spatial agreement with anomaly areas. In addition, the significant low-resistivity zones observed in the resistivity sections are echoed with the LST profiles when compared with in the Chingshui geothermal field. Despite limited to detecting the surficial and the shallow buried geothermal resources, this work suggests that TIR remote sensing is a valuable tool by providing an effective way of mapping and quantifying surface features to facilitate the exploration and assessment of geothermal resources in Taiwan.

Spatial variability of near-surface temperature over the coastal mountains in southern Chile (38\xb0S)

The spatial distribution of the near-surface air temperature over a coastal mountain range in southern Chile [Nahuelbuta Mountains (NM), 38°S, maximum height 1300-m ASL] is investigated using in situ measurements, satellite-derived land-surface temperature, and simulations during the austral winter of 2011. Based on a few selected but representative cases, we found that under rainy conditions—either at day or night—temperature decreases with height close to the moist adiabatic lapse rate (~6.5 °C/km). Likewise, the temperature tends to follow the dry adiabat (~9.8 °C/km) during daytime under dry- and clear-skies conditions. During clear-skies nights, the temperature also decreases with height over the southeastern side of NM, but it often increases (at about 8 °C/km) over the northwestern side of the mountains. This temperature inversion extends up to about 700-m ASL leading to an average temperature contrast of about 7 °C between the northwestern and southeastern sides of Nahuelbuta by the end of dry nights. These dawns also feature substantial temperature differences (>10 °C) among closely located stations at a same altitude. High-resolution numerical simulations suggest that upstream blocking of the prevailing SE flow, hydrostatic mountain waves, and strong downslope winds is responsible for such distinctive nocturnal temperature distribution.

Global patterns of shallow groundwater temperatures

Only meters below our feet, shallow aquifers serve as sustainable energy source and provide freshwater storage and ecological habitats. All of these aspects are crucially impacted by the thermal regime of the subsurface. Due to the limited accessibility of aquifers however, temperature measurements are scarce. Most commonly, shallow groundwater temperatures are approximated by adding an offset to annual mean surface air temperatures. Yet, the value of this offset is not well defined, often arbitrarily set, and rarely validated. Here, we propose the usage of satellite-derived land surface temperatures instead of surface air temperatures. 2 548 measurement points in 29 countries are compiled, revealing characteristic trends in the offset between shallow groundwater temperatures and land surface temperatures. Here it is shown that evapotranspiration and snow cover impact on this offset globally, through latent heat flow and insulation. Considering these two processes only, global shallow groundwater temperatures are estimated in a resolution of approximately 1 km × 1 km. When comparing these estimated groundwater temperatures with measured ones a coefficient of determination of 0.95 and a root mean square error of 1.4 K is found.

Identifying anthropogenic anomalies in air, surface and groundwater temperatures in Germany.

Human activity directly influences ambient air, surface and groundwater temperatures. The most prominent phenomenon is the urban heat island effect, which has been investigated particularly in large and densely populated cities. This study explores the anthropogenic impact on the thermal regime not only in selected urban areas, but on a countrywide scale for mean annual temperature datasets in Germany in three different compartments: measured surface air temperature, measured groundwater temperature, and satellite-derived land surface temperature. Taking nighttime lights as an indicator of rural areas, the anthropogenic heat intensity is introduced. It is applicable to each data set and provides the difference between measured local temperature and median rural background temperature. This concept is analogous to the well-established urban heat island intensity, but applicable to each measurement point or pixel of a large, even global, study area. For all three analyzed temperature datasets, anthropogenic heat intensity grows with increasing nighttime lights and declines with increasing vegetation, whereas population density has only minor effects. While surface anthropogenic heat intensity cannot be linked to specific land cover types in the studied resolution (1km×1km) and classification system, both air and groundwater show increased heat intensities for artificial surfaces. Overall, groundwater temperature appears most vulnerable to human activity, albeit the different compartments are partially influenced through unrelated processes; unlike land surface temperature and surface air temperature, groundwater temperatures are elevated in cultivated areas as well. At the surface of Germany, the highest anthropogenic heat intensity with 4.5K is found at an open-pit lignite mine near Jülich, followed by three large cities (Munich, Düsseldorf and Nuremberg) with annual mean anthropogenic heat intensities >4K. Overall, surface anthropogenic heat intensities >0K and therefore urban heat islands are observed in communities down to a population of 5000.

Intraurban Temperature Variability in Baltimore

AbstractHow much does minimum daily air temperature vary within neighborhoods exhibiting high land surface temperature (LST), and does this variability affect agreement with the nearest weather station? To answer these questions, a low-cost sensor network of 135 “iButton” thermometers was deployed for summer 2015 in Baltimore, Maryland (a midsized American city with a temperate climate), focusing on an underserved area that exhibits high LST from satellite imagery. The sensors were evaluated against commercial and NOAA/NWS stations and showed good agreement for daily minimum temperatures. Variability within the study site was small: mean minimum daily temperatures have a spatial standard deviation of 0.9°C, much smaller than the same measure for satellite-derived LST. The sensor-measured temperatures agree well with the NWS weather station in downtown Baltimore, with a mean difference for all measurements in time and space of 0.00°C; this agreement with the station is not found to be correlated with any meteorological variables with the exception of radiation. Surface properties are found to be important in determining spatial variability: vegetated or green spaces are observed to be 0.5°C cooler than areas dominated by impervious surfaces, and the presence of green space is found to be a more significant predictor of temperature than surface properties such as elevation. Other surface properties—albedo, tree-canopy cover, and distance to the nearest park—are not found to correlate significantly with air temperatures.

Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains

Satellite-derived land surface temperature (skin temperature) provides invaluable information for data-sparse high elevation and Arctic regions. However, the relationship between satellite-derived clear-sky skin temperature and various downscaled air temperature products for snow covered sub-Arctic alpine regions remain poorly understood, such that trend analysis or air temperature product integration is difficult. We compared monthly average air temperatures from two independent downscaled temperature products to MODIS Land Surface Temperature (LST) and air temperature at nine meteorological stations situated above tree-line in the southwest Yukon, Canada, between May and August 2008 for a full range of snow cover fractions. We found that both downscaled products generally agreed with LST for the low elevation, snow-free, vegetation classes. However, a systematic cold bias in Average LST emerged for snow fractions greater than approximately 40%, and this bias increased in magnitude as snow cover increased. In these situations the downscaled air temperatures were 5–7°C warmer than Average LST for snow fractions of >90%, and this pattern was largely independent of the number of measurements of LST within a month. Maximum LST was similar to average air temperatures for high snow fractions, but Minimum LST was colder by 10°C or more for all snow fractions. Consequently, the average of Maximum and Minimum LST produces the cold bias, compared to air temperature, for high snow cover fractions. Air temperature measured at nine meteorological monitoring stations located between elevations of 1408–2690m, on land cover classes Barren, Sparsely Vegetated or Permanent Snow and Ice, confirmed the cold bias results when incorporating Minimum LST in monthly averages. For snow fractions of <40% the RMSE for all of the temperature products was <2.5°C when compared to station air temperature and all biases were positive and <2.0°C. For snow fractions of >40%, the average LST bias became strongly negative at −4.5°C, and the RMSE increased to 6.1°C, whereas the downscaled products bias and RMSE were similar to those from snow fractions of <40%. A weak warm bias for all the temperature products occurred for small snow fractions over non-forested land cover classes. Downscaled air temperature fields show physically real differences from Average LST in spring and summer, caused by snow cover and the interplay of Maximum and Minimum LST. These findings indicate that the integration of MODIS LST with downscaled air temperature products or local air temperature requires the incorporation of snow cover.

Microscale mobile monitoring of urban air temperature

BackgroundMobile air temperature monitoring is a promising method to better understand temperature distributions at fine spatial resolutions across urban areas. The study objectives were to collect microscale measurements for evaluate different data sources used to assess heat exposure in greater Vancouver, Canada. MethodsMobile air temperature monitoring was conducted on foot at least twice for each of 20 routes. First, the mobile data were compared with 1-minute measurements from the nearest fixed site. Second, the mobile data from runs corresponding with Landsat overpass days were compared with satellite-derived land surface temperature (LST). Third, the mobile data were compared with estimates from a previously developed heat map for the region. ResultsMobile measurements were typically higher and more variable than simultaneous fixed site measurements. Correlations between mobile measurements and LST were weak and highly variable (r2=0.04–0.38). The z-score differentials between mobile measurements and the heat map suggested that spatial variability in temperatures is captured by the heat map. ConclusionMicroscale measurements confirm that fixed sites do not characterize the variability in thermal conditions within nearby streetscapes. Microscale monitoring of air temperatures is a valuable tool for temporally and spatially evaluating other high resolution temperature data within small areas.

A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation

The estimation of spatially-variable actual evapotranspiration (AET) is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE), to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges). Compared to traditional triangle methods, TAVE introduces three unique features: (i) the discretization of the domain as overlapping elevation zones; (ii) a variable wet edge that is a function of elevation zone; and (iii) variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability) along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA) and a global AET product (MOD16) over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%), in contrast to substantial overestimation by TA (+234%) and underestimation by MOD16 (−50%). In forested (non-irrigated, water consuming) regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan.

Elevation-dependent changes in n-alkane δD and soil GDGTs across the South Central Andes

Surface uplift of large plateaus may significantly influence regional climate and more specifically precipitation patterns and temperature, sometimes complicating paleoaltimetry interpretations. Thus, understanding the topographic evolution of tectonically active mountain belts benefits from continued development of reliable proxies to reduce uncertainties in paleoaltimetry reconstructions. Lipid biomarker-based proxies provide a novel approach to stable isotope paleoaltimetry and complement authigenic or pedogenic mineral proxy materials, in particular outside semi-arid climate zones where soil carbonates are not abundant but (soil) organic matter has a high preservation potential. Here we present δD values of soil-derived n-alkanes and mean annual air temperature (MAT) estimates based on branched glycerol dialkyl glycerol tetraether (brGDGT) distributions to assess their potential for paleoelevation reconstructions in the southern central Andes. We analyzed soil samples across two environmental and hydrological gradients that include a hillslope (26–28°S) and a valley (22–24°S) transect on the windward flanks of Central Andean Eastern Cordillera in NW Argentina. Our results show that present-day n-alkane δD values and brGDGT-based MAT estimates are both linearly related with elevation and in good agreement with present-day climate conditions. Soil n-alkanes show a δD lapse rate (Δ(δD)) of −1.64‰/100 m (R2=0.91, p<0.01) at the hillslope transect, within the range of δD lapse rates from precipitation and surface waters in other tropical regions in the Andes like the Eastern Cordillera in Colombia and Bolivia and the Equatorial and Peruvian Andes. BrGDGT-derived soil temperatures are similar to monitored winter temperatures in the region and show a lapse rate of ΔT=−0.51°C/100 m (R2=0.91, p<0.01), comparable with lapse rates from in situ soil temperature measurements, satellite-derived land-surface temperatures at this transect, and weather stations from the Eastern Cordillera at similar latitude. As a result of an increasing leeward sampling position along the valley transect lapse rates are biased towards lower values and display higher scatter (Δ(δD)=−0.95‰/100 m, R2=0.76, p<0.01 and ΔT=−0.19°C/100 m, R2=0.48, p<0.05). Despite this higher complexity, they are in line with lapse rates from stream-water samples and in situ soil temperature measurements along the same transect. Our results demonstrate that both soil n-alkane δD values and MAT reconstructions based on brGDGTs distributions from the hillslope transect (Δ(δD)=−1.64‰/100 m, R2=0.91, p<0.01 and ΔT=−0.51°C/100 m, R2=0.91, p<0.01) track the direct effects of orography on precipitation and temperature and hence the combined effects of local and regional hydrology as well as elevation.

Estimating Understory Temperatures Using MODIS LST in Mixed Cordilleran Forests

Satellite remote sensing provides a rapid and broad-scale means for monitoring vegetation phenology and its relationship with fluctuations in air temperature. Investigating the response of plant communities to climate change is needed to gain insight into the potentially detrimental effects on ecosystem processes. While many studies have used satellite-derived land surface temperature (LST) as a proxy for air temperature, few studies have attempted to create and validate models of forest understory temperature (Tust), as it is obscured from these space-borne observations. This study worked to predict instantaneous values of Tust using daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST data over a 99,000 km2 study area located in the Rocky Mountains of western Alberta, Canada. Specifically, we aimed to identify the forest characteristics that improve estimates of Tust over using LST alone. Our top model predicted Tust to within a mean absolute error (MAE) of 1.4 °C with an overall model fit of R2 = 0.89 over two growing seasons. Canopy closure and the LiDAR-derived standard deviation of canopy height metric were found to significantly improve estimations of Tust over MODIS LST alone. These findings demonstrate that canopy structure and forest stand-type function to differentiate understory air temperatures from ambient canopy temperature as seen by the sensor overhead.

Temporal upscaling of surface urban heat island by incorporating an annual temperature cycle model: A tale of two cities

Abstract Satellite thermal remote sensing potentially provides a new way to monitor local climate change due to urbanization, especially changes in surface temperatures that result in the surface urban heat island (SUHI). However, this technique is restricted to clear-sky conditions. Because of this limitation, satellite-derived land surface temperature (LST) records are frequently interrupted, sometimes even becoming temporally sparse and, accordingly, climatically less representative. Given this challenge, we propose a strategy that incorporates an annual temperature cycle (ATC) model to perform temporal upscaling of the SUHI from a climatic perspective. Using two megacities (Beijing and Shanghai) as case studies, our major findings include: (1) urbanization tends to enlarge the amplitude of annual daytime LST series for both cities; (2) urbanization in Beijing narrows the diurnal LST range on annual average but broadens it in Shanghai; (3) within an annual cycle, the daytime SUHI intensity (SUHII) reaches its maximum one month later than the daytime LST maximum for Beijing, whereas this time difference is negligible for Shanghai; and (4) compared with the observation-based and moving-window-based temporal aggregations, the ATC-based temporal aggregation allows to produce a clear-sky SUHI climatology that is more representative and becomes potentially valuable for prediction or application purposes. From a climatic perspective, the temporal upscaling of the SUHI, therefore, provides insights into the impacts on local thermal environments that are induced by urbanization.

Consistent land surface temperature data generation from irregularly spaced Landsat imagery

Abstract Land surface temperature (LST), derived from satellite thermal infrared (TIR) sensors, is a key variable for characterization of urban heat island, modeling of surface energy balance, estimation of evapotranspiration and soil moisture, and retrieval of air temperature. Among the satellite TIR sensors in operation, Landsat TIR sensor provides the only feasibility for long-term reconstruction of a LST dataset for environmental applications. However, a holistic technique is not currently available to generate spatially and temporally continuous LSTs from Landsat due to its 16-day revisit frequency, impact of atmospheric conditions and the SLC (Scan Line Corrector) -off gap. Previous algorithms had been developed to overcome these limitations, it is still not possible to generate LSTs at any desired date with consistent accuracy and corrections. Therefore, this study aimed to devise an algorithm to reconstruct consistent, daily LSTs at Landsat spatial resolution based solely on Landsat imagery. By selecting Beijing, China, as the study area, a total of 512 images from 1984 to 2011 were downloaded from the USGS online portal and were consistently calibrated to surface reflectance and brightness temperature. The cloud-, cloud shadow-, and snow-contaminated pixels were excluded according to quality flags; and a further screening procedure based on temporal information of Landsat spectral bands 2, 4, and 5 was conducted. Brightness temperatures were converted to LSTs through the single channel algorithm with input of water vapor from the NCEP Reanalysis dataset. Field LSTs were collected from 11 weather stations in Beijing in the year of 2008, 2009, and 2010. The proposed algorithm included four modules: Data filtEr, temporaL segmentation, periodic and trend modeling, and GAussian process (DELTA). Accuracy assessment showed that, compared with the in situ LSTs from weather stations, satellite-derived LSTs inverted through the single channel algorithm had an average accuracy of 2.3 K. Further comparison between LSTs reconstructed from the DELTA algorithm and those collected from weather stations in the year 2008 yielded a mean error of 3.5 K. Twelve LST maps reconstructed from the DELTA in 2000 showed that LSTs of different land covers exhibited similar seasonal patterns and reached their maximal values in June/July. Using LST of every August 15th as an example, the SUHI (surface urban heat island) intensity of Beijing was computed, which ranged from 3.3 K to 5.3 K from 1984 to 2011, with an increase pattern of LST in both rural and urban areas.

Modelling spatio-temporally resolved air temperature across the complex geo-climate area of France using satellite-derived land surface temperature data

Climate change has focused attention on the effects of changing temperature, particularly the effect on human health. Thus, robust and accurate spatially and temporally resolved air temperature (Ta) data are of particular importance in the field of epidemiology and public health. However, most health studies to date have matched people to the nearest monitor. In this study, we aimed to develop a robust satellite-based spatio-temporally resolved Ta estimation model across the complex geo-climatic regions of France resulting in daily high-resolution 1 km predicted air temperature (Tap) estimations. We use a daily calibration approach using a series of processes to generate daily Tap for every day across the entire study area and period. First, we start by calibrating MODIS (Moderate Resolution Imaging Spectroradiometer) satellite-gridded surface temperature (Ts) data against Ta collected within 1 km of the Ts centroid. The calibration stage adjusted for spatio-temporal predictors, as done in environmental exposure assessment methods such as land use regressions. Second, to estimate Tap when no Ts data are available we fit a second model which uses the association of predicted grid cells Tap values (based on satellite Ts) with surrounding Ta monitors and the association with values in neighbouring grid cells. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (overall mean out-of-sample R2 = 0.95 for both stages). In conclusion, we demonstrate how Ts can be used reliably to predict daily Tap at high-resolution across France for use in studies looking at the effects of fine resolution Ta exposure on various health outcomes.

Quantifying the Daytime and Night-Time Urban Heat Island in Birmingham, UK: A Comparison of Satellite Derived Land Surface Temperature and High Resolution Air Temperature Observations

The Urban Heat Island (UHI) is one of the most well documented phenomena in urban climatology. Although a range of measurements and modelling techniques can be used to assess the UHI, the paucity of traditional meteorological observations in urban areas has been an ongoing limitation for studies. The availability of remote sensing data has therefore helped fill a scientific need by providing high resolution temperature data of our cities. However, satellite-mounted sensors measure land surface temperatures (LST) and not canopy air temperatures with the latter being the key parameter in UHI investigations. Fortunately, such data is becoming increasingly available via urban meteorological networks, which now provide an opportunity to quantify and compare surface and canopy UHI on an unprecedented scale. For the first time, this study uses high resolution air temperature data from the Birmingham Urban Climate Laboratory urban meteorological network and MODIS LST to quantify and identify the spatial pattern of the daytime and night-time UHI in Birmingham, UK (a city with an approximate population of 1 million). This analysis is performed under a range of atmospheric stability classes and investigates the relationship between surface and canopy UHI in the city. A significant finding of this work is that it demonstrates, using observations, that the distribution of the surface UHI appears to be clearly linked to landuse, whereas for canopy UHI, advective processes appear to play an increasingly important role. Strong relationships were found between air temperatures and LST during both the day and night at a neighbourhood scale, but even with the use of higher resolution urban meteorological datasets, relationships at the city scale are still limited.

Satellite-Derived Temperature Data for Monitoring Water Status in a Floodplain Forest of the Upper Sabine River, Texas

Abstract Decreased water availability due to hydrologic modifications, groundwater withdrawal, and climate change threaten bottomland hardwood (BLH) forest communities. We used satellite-derived (MODIS) land-surface temperature (LST) data to investigate spatial heterogeneity of canopy temperature (an indicator of plant-water status) in a floodplain forest of the upper Sabine River for 2008–2014. High LST pixels were generally further from the river and at higher topographic locations, indicating lower water-availability. Increasing rainfall-derived soil moisture corresponded with decreased heterogeneity of LST between pixels but there was weaker association between Sabine River stage and heterogeneity. Stronger dependence of LST convergence on rainfall rather than river flow suggests that some regions are less hydrologically connected to the river, and vegetation may rely on local precipitation and other contributions to the riparian aquifer to replenish soil moisture. Observed LST variations associated w...

Linking Surface Urban Heat Islands with Groundwater Temperatures.

Urban temperatures are typically, but not necessarily, elevated compared to their rural surroundings. This phenomenon of urban heat islands (UHI) exists both above and below the ground. These zones are coupled through conductive heat transport. However, the precise process is not sufficiently understood. Using satellite-derived land surface temperature and interpolated groundwater temperature measurements, we compare the spatial properties of both kinds of heat islands in four German cities and find correlations of up to 80%. The best correlation is found in older, mature cities such as Cologne and Berlin. However, in 95% of the analyzed areas, groundwater temperatures are higher than land surface temperatures due to additional subsurface heat sources such as buildings and their basements. Local groundwater hot spots under city centers and under industrial areas are not revealed by satellite-derived land surface temperatures. Hence, we propose an estimation method that relates groundwater temperatures to mean annual land-surface temperatures, building density, and elevated basement temperatures. Using this method, we are able to accurately estimate regional groundwater temperatures with a mean absolute error of 0.9 K.

Intercomparison of Operational Land Surface Temperature Products Derived From MSG-SEVIRI and Terra/Aqua-MODIS Data

Accuracy assessment of land surface temperature (LST) products is critical to facilitate their use in various studies. As an alternative method for assessing the accuracy of LST products, a new satellite LST product is compared with a heritage LST product to validate and determine the uncertainties in the satellite-derived LST approach. In this study, we propose a method for the intercomparison of the Meteosat second generation-spinning enhanced visible and infrared imager (MSG-SEVIRI) and Terra/Aqua-moderate resolution imaging spectroradiometer (MODIS) LST products. The intercomparison was performed by verifying the collocation in space, temporal concurrence, viewing geometry alignment, and spatial homogeneity between the two LST products. The discrepancies between the SEVIRI and MODIS LST products were investigated over different seasons, times of day, and surface types. SEVIRI LST values are generally higher than MODIS LST values, with positive biases during daytime (approximately 2–4 K) and nighttime (approximately 1–2 K). Significant variability of the daytime LST discrepancies with season, time of day, and surface type is observed. Compared with the daytime LST discrepancies, the nighttime LST discrepancies are less dependent on season, time of day, and surface type.

Satellite-derived NDVI, LST, and climatic factors driving the distribution and abundance of Anopheles mosquitoes in a former malarious area in northwest Argentina.

Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector-borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas.

Enhancing the spatial resolution of satellite-derived land surface temperature mapping for urban areas

Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.