Annual Land Surface Temperature Research Articles

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.

Monitoring land surface thermal environments under the background of landscape patterns in arid regions: A case study in Aksu river basin

Land surface temperature (LST) is defined as an important indicator in the formation and evolution of climate. In some cases, changes in landscape patterns affect LST, even more than the contribution of greenhouse gases. Although much work has been done with respect to the correlations between urban development and thermal environment dynamics, the related questions regarding relationships between LST and landscape patterns in arid regions are not thoroughly considered. Understanding these questions is important in climate change and land planning. The objective of this study was to explore the spatiotemporal variations of LST by distribution index (DI) and Mann-Kendall mutation analysis method and to quantify the relationships between landscape patterns, climatic factors, topographic factors, and the land surface thermal environment (LSTE) by the ordinary linear regressions (OLS) model. The landscape patterns dataset, which was validated by a field trip, was extracted from the Land satellite (Landsat) TM/OLI images by the Random Forest methodology in ArcGIS software. The MODIS/LST product was validated by the “Monthly dataset of China's surface climate” and a field trip. Annual LST increased by 0.54 °C (23.15 °C in 2000 and 23.79 °C in 2015). In different landscape patterns, the percentage of areas with a high level of LST showed a significant difference. In barren land, the highest area proportion for the high LST level was larger than in other landscape patterns. Meanwhile, the area of low LST was mainly concentrated in water bodies. Considerable changes have occurred in landscape patterns, in which the most noteworthy was cultivated land encroaching on grass land (3708.44 km2). The composition of landscape patterns was more important than distribution in determining the region's LST. These findings provide valuable information for land planners dealing with climate change and ecosystem conservation in arid regions.

Land surface temperature variability across India: a remote sensing satellite perspective

Land surface temperature (LST) plays a key role in the surface energy budget computation and land surface process studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Aqua and Terra satellites provide comprehensive global LST estimates at a fine spatial resolution. The MODIS products were recently upgraded to Collection 6, and shown to be more accurate than its predecessor Collection 5 products. In this study, LST and its variability have been examined across India from Collection 6 of the Aqua MODIS data at 0.05° spatial resolution for the period of 2003 to 2017. All-India mean LST characteristics show distinctive features as compared to the well-documented mean characteristics of near-surface air temperature. All land cover types except permanent snow and ice, and cold desert areas exhibit bimodal peaks in seasonal variations of daytime LST. The daytime LST over the coldest and high-altitude regions of northern India shows anomalous positive linear relationship with NDVI at a monthly scale. However, monthly domain-mean daytime LST of cropland regions is largely negatively correlated with NDVI as compared to other land cover types. Results reveal that about 17% of the Indian landmass received its hottest LST during 2010 followed by 2016. Linear trend analysis for the 15-year period of mean annual LST shows a decrease in diurnal temperature range over most parts of the country due to rather rapid increase in nighttime LST than daytime LST, similar as changes in near-surface air temperature across the country.

Monitoring the Interannual Spatiotemporal Changes in the Land Surface Thermal Environment in Both Urban and Rural Regions from 2003 to 2013 in China Based on Remote Sensing

The thermal environment is closely related to human well-being. Diurnal and seasonal variations in surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, interannual changes in SUHIs as well as in land surface temperatures (LSTs) in cities and their corresponding villages remain poorly understood, particularly using data from several continuous years to analyse change rates and corresponding significance levels. Using Aqua/Terra moderate resolution imaging spectroradiometer (MODIS) data for 2003–2013, we explored not only the interannual changes in annual and seasonal mean LSTs in rural and urban regions which were identified based on modified criteria, but also the SUHI intensities (SUHIIs) for these cities. The results showed that most of LSTs and SUHIIs did not change significantly (p≥0.05). Their changes exhibited clear spatiotemporal agglomeration and variation laws. The rural region LST change rates, which exhibited significant changes, were generally highest in the summer, with most of values of 0.1–0.5°C (yr−1) during the daytime across China, except for the Xinjiang autonomous regions, and 0.1–0.2°C (yr−1) during the night-time. The rates were lowest in the winter, with most of values of −0.4 to −0.1°C (yr−1). The rates of daytime SUHIIs with significant changes were generally highest in the summer, with most of values of 0.1–0.3°C (yr−1), and lowest in the winter, even with most of values of −0.4 to −0.1°C (yr−1) in northern central China. During the night-time, most of rates were 0.0–0.1°C (yr−1). In China, most of the changes in the surface thermal environment were harmful to humans at both large national and local urban scales. The changes could lower thermal comfort levels, harm human health, affect human reproduction rates and lives, and increase the energy consumed for refrigeration or heating, thereby increase emissions of greenhouse gases.

Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

Application of Thermal and Phenological Land Surface Parameters for Improving Ecological Niche Models of Betula utilis in the Himalayan Region

Modelling ecological niches across vast distribution ranges in remote, high mountain regions like the Himalayas faces several data limitations, in particular nonavailability of species occurrence data and fine-scale environmental information of sufficiently high quality. Remotely sensed data provide key advantages such as frequent, complete, and long-term observations of land surface parameters with full spatial coverage. The objective of this study is to evaluate modelled climate data as well as remotely sensed data for modelling the ecological niche of Betula utilis in the subalpine and alpine belts of the Himalayan region covering the entire Himalayan arc. Using generalized linear models (GLM), we aim at testing factors controlling the species distribution under current climate conditions. We evaluate the additional predictive capacity of remotely sensed variables, namely remotely sensed topography and vegetation phenology data (phenological traits), as well as the capability to substitute bioclimatic variables from downscaled numerical models by remotely sensed annual land surface temperature parameters. The best performing model utilized bioclimatic variables, topography, and phenological traits, and explained over 69% of variance, while models exclusively based on remotely sensed data reached 65% of explained variance. In summary, models based on bioclimatic variables and topography combined with phenological traits led to a refined prediction of the current niche of B. utilis, whereas models using solely climate data consistently resulted in overpredictions. Our results suggest that remotely sensed phenological traits can be applied beneficially as supplements to improve model accuracy and to refine the prediction of the species niche. We conclude that the combination of remotely sensed land surface temperature parameters is promising, in particular in regions where sufficient fine-scale climate data are not available.

SPATIAL AND TEMPORAL VARIATION OF LAND SURFACE TEMPERATURE IN FUJIAN PROVINCE FROM 2001 TO 2015

Abstract. Land surface temperature (LST) is an essential parameter in the physics of land surface processes. The spatiotemporal variations of LST on the Fujian province were studied using AQUA Moderate Resolution Imaging Spectroradiometer LST data. Considering the data gaps in remotely sensed LST products caused by cloud contamination, the Savitzky-Golay (S-G) filter method was used to eliminate the influence of cloud cover and to describe the periodical signals of LST. Observed air temperature data from 27 weather stations were employed to evaluate the fitting performance of the S-G filter method. Results indicate that S-G can effectively fit the LST time series and remove the influence of cloud cover. Based on the S-G-derived result, Spatial and temporal Variations of LST in Fujian province from 2001 to 2015 are analysed through slope analysis. The results show that: 1) the spatial distribution of annual mean LST generally exhibits consistency with altitude in the study area and the average of LST was much higher in the east than in the west. 2) The annual mean temperature of LST declines slightly among 15 years in Fujian. 3) Slope analysis reflects the spatial distribution characteristics of LST changing trend in Fujian.Improvement areas of LST are mainly concentrated in the urban areas of Fujian, especially in the eastern urban areas. Apparent descent areas are mainly distributed in the area of Zhangzhou and eastern mountain area.

Mapping the Spatiotemporal Dynamics of Europe’s Land Surface Temperatures

The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth’s diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface’s thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe’s annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009–2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe’s biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution.

Thermal Anomalies Detect Critical Global Land Surface Changes

AbstractMeasurements that link surface conditions and climate can provide critical information on important biospheric changes occurring in the Earth system. As the direct driving force of energy and water fluxes at the surface–atmosphere interface, land surface temperature (LST) provides information on physical processes of land-cover change and energy-balance changes that air temperature cannot provide. Annual maximum LST (LSTmax) is especially powerful at minimizing synoptic and seasonal variability and highlighting changes associated with extreme climatic events and significant land-cover changes. The authors investigate whether maximum thermal anomalies from satellite observations could detect heat waves and droughts, a melting cryosphere, and disturbances in the tropical forest from 2003 to 2014. The 1-km2 LSTmax anomalies peaked in 2010 when 20% of the global land area experienced anomalies of greater than 1 standard deviation and over 4% of the global land area was subject to positive anomalies exceeding 2 standard deviations. Positive LSTmax anomalies display complex spatial patterns associated with heat waves and droughts across the global land area. The findings presented herein show that entire biomes are experiencing shifts in their LSTmax distributions driven by extreme climatic events and large-scale land surface changes, such as melting of ice sheets, severe droughts, and the incremental effects of forest loss in tropical forests. As climate warming and land-cover changes continue, it is likely that Earth’s maximum surface temperatures will experience greater and more frequent directional shifts, increasing the possibility that critical thresholds in Earth’s ecosystems and climate system will be surpassed, resulting in profound and irreversible changes.

Annual Seasonality Extraction Using the Cubic Spline Function and Decadal Trend in Temporal Daytime MODIS LST Data

Examining climate-related satellite data that strongly relate to seasonal phenomena requires appropriate methods for detecting the seasonality to accommodate different temporal resolutions, high signal variability and consecutive missing values in the data series. Detection of satellite-based Land Surface Temperature (LST) seasonality is essential and challenging due to missing data and noise in time series data, particularly in tropical regions with heavy cloud cover and rainy seasons. We used a semi-parametric approach, involving the cubic spline function with the annual periodic boundary condition and weighted least square (WLS) regression, to extract annual LST seasonal pattern without attempting to estimate the missing values. The time series from daytime Aqua eight-day MODIS LST located on Phuket Island, southern Thailand, was selected for seasonal extraction modelling across three different land cover types. The spline-based technique with appropriate number and placement of knots produces an acceptable seasonal pattern of surface temperature time series that reflects the actual local season and weather. Finally, the approach was applied to the morning and afternoon MODIS LST datasets (MOD11A2 and MYD11A2) to demonstrate its application on seasonally-adjusted long-term LST time series. The surface temperature trend in both space and time was examined to reveal the overall 10-year period trend of LST in the study area. The result of decadal trend analysis shows that various Land Use and Land Cover (LULC) types have increasing, but variable surface temperature trends.

Determination of annual and seasonal daytime and nighttime trends of MODIS LST over Greece - climate change implications

Climate change is one of the most challenging research topics during the last few decades, as temperature rise has already posed a significant impact on the earth's functions thus affecting all life of the planet. Land Surface Temperature (LST) is identified as a key variable in environmental and climate studies. The present study investigates the distribution of daytime and nighttime LST trends over Greece, a country in the Mediterranean area which is identified as one of the main “hot-spots” of climate change projections. Remotely sensed LST data were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) sensor in the form of 8-day composites of day and night values at a resolution of 1km for a 17-year period, i.e. from 2000 to 2017. Spatial aggregates of 10km×10km were computed and the annual and seasonal temporal trends were determined for each one of those sub-areas. Results showed that annual trends of daily LST in the majority of areas demonstrated decrease ranging from −1∗10–2°C to −1.3∗10−3°C, with some sporadic parts showing a slight increase. A totally different outcome is observed in the fate of night LST, with all areas over Greece demonstrating increasing annual trends ranging from 4.6∗10−5°C to 3.1∗10–3°C, with highest values in the South-East parts of the country. Seasonal trends in day and night LST showed the same pattern, i.e., a general decrease in the day LST and a definite increase in night. An interesting finding is the increase in winter LST trends observed both for day and night LST, indicating that the absolute minimum annual LST observed during winter in Greece increases. Our results also indicate that the annual diurnal LST range is decreasing.

Difference between near-surface air, land surface and ground surface temperatures and their influences on the frozen ground on the Qinghai-Tibet Plateau

Surface temperature is critical for the simulation of climate change impacts on the ecology, environment, and particularly permafrost in the cryosphere. Virtually, surface temperatures are different in the near-surface air temperature (Ta) measured at a screen-height of 1.5–2m, the land surface temperature (LST) on the top canopy layer, and the ground surface temperature (GST) 0–5 cm beneath the surface cover. However, not enough attention has been concentrated on the difference in these surface temperatures. This study aims at quantifying the distinction of surface temperatures by the comparisons and numerical simulations of observational field data collected in a discontinuous permafrost region on the northeastern Qinghai-Tibet Plateau (QTP). We compared the hourly, seasonal and yearly differences between Ta, LST, GST, and ground temperatures, as well as the freezing and thawing indices, the N-factors, and the surface and thermal offsets derived from these temperatures. The results showed that the peak hourly LST was reached earliest, closely followed by the hourly Ta. Mean annual LST (MALST) was moderately comparable to mean annual Ta (MAAT), and both were lower than mean annual GST (MAGST). Surface offsets (MAGST-MAAT) were all within 3.5 °C, which are somewhat consistent with other parts of the QTP but smaller than those in the Arctic and Subarctic regions with dense vegetation and thick, long-duration snow cover. Thermal offsets, the mean annual differences between the ground surface and the permafrost surface, were within −0.3°C, and one site was even reversed, which may be relevant to equally thawed to frozen thermal conductivities of the soils. Even with identical Ta (comparable to MAAT of −3.27 and −3.17°C), the freezing and thawing processes of the active layer were distinctly different, due to the complex influence of surface characteristics and soil textures. Furthermore, we employed the Geophysical Institute Permafrost Lab (GIPL) model to numerically simulate the dynamics of ground temperature driven by Ta, LST, and GST, respectively. Simulated results demonstrated that GST was a reliable driving indicator for the thermal regime of frozen ground, even if no thermal effects of surface characteristics were taken into account. However, great biases of mean annual ground temperatures, being as large as 3°C, were induced on the basis of simulations with LST and Ta when the thermal effect of surface characteristics was neglected. We conclude that quantitative calculation of the thermal effect of surface characteristics on GST is indispensable for the permafrost simulations based on the Ta datasets and the LST products that derived from thermal infrared remote sensing.

CONSTRUCTION AND ANALYSIS OF LONG-TERM SURFACE TEMPERATURE DATASET IN FUJIAN PROVINCE

Abstract. Land surface temperature (LST) is a key parameter of land surface physical processes on global and regional scales, linking the heat fluxes and interactions between the ground and atmosphere. Based on MODIS 8-day LST products (MOD11A2) from the split-window algorithms, we constructed and obtained the monthly and annual LST dataset of Fujian Province from 2000 to 2015. Then, we analyzed the monthly and yearly time series LST data and further investigated the LST distribution and its evolution features. The average LST of Fujian Province reached the highest in July, while the lowest in January. The monthly and annual LST time series present a significantly periodic features (annual and interannual) from 2000 to 2015. The spatial distribution showed that the LST in North and West was lower than South and East in Fujian Province. With the rapid development and urbanization of the coastal area in Fujian Province, the LST in coastal urban region was significantly higher than that in mountainous rural region. The LST distributions might affected by the climate, topography and land cover types. The spatio-temporal distribution characteristics of LST could provide good references for the agricultural layout and environment monitoring in Fujian Province.

Effect of Urbanization on Land-Surface Temperature at an Urban Climate Station in North China

While the land-surface temperature (LST) observed at meteorological stations has significantly increased over the previous few decades, it is still unclear to what extent urbanization has affected these positive trends. Based on the LST data recorded at an urban station in Shijiazhuang in North China, and two rural meteorological stations, the effect of urbanization at the Shijiazhuang station for the period 1965–2012 is examined. We find, (1) a statistically-significant linear trend in annual mean urban–rural LST difference of $$0.27\,^{\circ }\hbox {C}$$ $$\hbox {(10 year)}^{-1}$$ , with an urbanization contribution of 100% indicating that the increase in the annual mean LST at the urban station is entirely caused by urbanization. The urbanization effects in spring, summer and autumn on the trends of mean LST are also significant; (2) the urbanization effect is small for time series of the annual mean minimum LST, and statistically marginal for the trend in annual mean maximum LST [ $$0.19\,^{\circ }\hbox {C}\,\hbox {(10 year)}^{-1}$$ ]; (3) the urbanization effect on the annual mean diurnal LST range ( $$\Delta {LST}$$ ) at the urban station is a strongly significant trend of $$0.23\,^{\circ }\hbox {C (10\,year)}^{-1}$$ , with an urbanization contribution of 21%. The urbanization effects on trends in the spring and autumn mean $$\Delta {LST}$$ are also larger and more significant than for the other seasons; (4) the urbanization effects on the long-term LST trends are remarkably different from those on the near-surface air temperature at the same urban station. Nonetheless, the significant warming of the urban boundary layer is expected to affect the urban environment and ecosystems. However, the problem of data representativeness at an urban station for the monitoring and investigation of large-scale climate change remains.

Validation and Analysis of Long-Term AATSR Land Surface Temperature Product in the Heihe River Basin, China

The Advanced Along-Track Scanning Radiometer (AATSR) land surface temperature (LST) product has a long-term time series of data from 20 May 2002 to 8 April 2012 and is a crucial dataset for global change studies. Accuracy and uncertainty assessment of satellite derived LST is important for its use in studying land–surface–atmosphere interactions. However, the validation of AATSR-derived LST products is scarce in China, especially in arid and semi-arid areas. In this study, we evaluated the accuracy of the AATSR LST product using ground-based measurements from 2007 to 2011 in the Heihe River Basin (HRB), China. The AATSR-derived LST results over Yingke site are closer to ground measurements than those over A’rou site for both daytime and nighttime temperatures. For nighttime, the averaged bias, STD, RMSE and R2 over both sites are 0.67 K, 3.03 K, 3.13 K and 0.93 K, respectively. Based on the accuracy assessment, we analyzed the AATSR-derived annual LST variations both in the HRB region and the two validation sites for the period of 2003 to 2011. The results at the A’rou site show an obvious increasing trend for daytime from 2003 to 2011. For the whole HRB region, the warming trend is clearly shown in the downstream of HRB.

Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach

Modifications in vegetation cover can have an impact on the climate through changes in biogeochemical and biogeophysical processes. In this paper, the tree canopy cover percentage of a savannah-like ecosystem (montado/dehesa) was estimated at Landsat pixel level for 2011, and the role of different canopy cover percentages on land surface albedo (LSA) and land surface temperature (LST) were analysed. A modelling procedure using a SGB machine-learning algorithm and Landsat 5-TM spectral bands and derived vegetation indices as explanatory variables, showed that the estimation of montado canopy cover was obtained with good agreement (R2 = 78.4%). Overall, montado canopy cover estimations showed that low canopy cover class (MT_1) is the most representative with 50.63% of total montado area. MODIS LSA and LST products were used to investigate the magnitude of differences in mean annual LSA and LST values between contrasting montado canopy cover percentages. As a result, it was found a significant statistical relationship between montado canopy cover percentage and mean annual surface albedo (R2 = 0.866, p < 0.001) and surface temperature (R2 = 0.942, p < 0.001). The comparisons between the four contrasting montado canopy cover classes showed marked differences in LSA (χ2 = 192.17, df = 3, p < 0.001) and LST (χ2 = 318.18, df = 3, p < 0.001). The highest montado canopy cover percentage (MT_4) generally had lower albedo than lowest canopy cover class, presenting a difference of −11.2% in mean annual albedo values. It was also showed that MT_4 and MT_3 are the cooler canopy cover classes, and MT_2 and MT_1 the warmer, where MT_1 class had a difference of 3.42 °C compared with MT_4 class. Overall, this research highlighted the role that potential changes in montado canopy cover may play in local land surface albedo and temperature variations, as an increase in these two biogeophysical parameters may potentially bring about, in the long term, local/regional climatic changes moving towards greater aridity.

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

THE CLIMATE OF THE CANARY ISLANDS BY ANNUAL CYCLE PARAMETERS

Abstract. Annual cycle parameters (ACP) provide a global climatology of annual land surface temperature (LST) based on daily 1 km MODIS observations. These are based on a simple model of the annual temperature cycle and allow estimating LST patterns under largely cloud-free conditions for every day of year. Further, they deliver measures for the LST variability and the frequency of cloud occurrence. It has been demonstrated, that they reproduce important surface climate characteristics at global and urban scale but their ability to reproduce topo-climates has yet to be studied in detail. In this paper their suitability to investigate climatic variability at km scale were studied at the case of the Canary Islands (Spain). This Archipelago, has a very stable climate dominated by the Azores high and the trade wind belt, but shows a large number of micro-climates ranging from arid hot climates to cold climates. It was found that ACPs are a relevant source of climatic information at km scale in complex orography. Specifically, known features such as subsidence inversion, the resulting sea of clouds, the strong differentiation in precipitation between the flat and high islands, as well as the northern and southern slopes at the latter were clearly visible in the parameters.

The impact of built-up surfaces on land surface temperatures in Italian urban areas

Urban areas are characterized by the very high degree of soil sealing and continuous built-up areas: Italy is one of the European countries with the highest artificial land cover rate, which causes a substantial spatial variation in the land surface temperature (LST), modifying the urban microclimate and contributing to the urban heat island effect. Nevertheless, quantitative data regarding the contribution of different densities of built-up surfaces in determining urban spatial LST changes is currently lacking in Italy. This study, which aimed to provide clear and quantitative city-specific information on annual and seasonal spatial LST modifications resulting from increased urban built-up coverage, was conducted generally throughout the whole year, and specifically in two different periods (cool/cold and warm/hot periods). Four cities (Milan, Rome, Bologna and Florence) were included in the study. The LST layer and the built-up-surface indicator were obtained via use of MODIS remote sensing data products (1km) and a very high-resolution map (5m) of built-up surfaces recently developed by the Italian National Institute for Environmental Protection and Research. The relationships between the dependent (mean daily, daytime and nighttime LST values) and independent (built-up surfaces) variables were investigated through linear regression analyses, and comprehensive built-up-surface-related LST maps were also developed. Statistically significant linear relationships (p<0.001) between built-up surfaces and spatial LST variations were observed in all the cities studied, with a higher impact during the warm/hot period than in the cool/cold ones. Daytime and nighttime LST slope patterns depend on the city size and relative urban morphology. If implemented in the existing city plan, the urban maps of built-up-surface-related LST developed in this study might be able to support more sustainable urban land management practices by identifying the critical areas (Hot-Spots) that would benefit most from mitigation actions by local authorities, land-use decision makers, and urban planners.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Abstract The relationship between land use and land cover (LULC) patterns and thermal characteristic has long been studied for examining the impact of urbanization on urban thermal environment. Previous studies were inclined to the use of one or a few images without fully considering the temporal domain of the reflective and thermal infrared data on board Landsat sensors. This paper took the Atlanta metropolitan area as a case study to illustrate LULC change and its impact on land surface temperature (LST) variations. The Landsat L1T (Standard terrain correction) images from TM/ETM + from 1984 to 2011 (507 in total) were downloaded through the USGS online portal and consistently calibrated to surface reflectance and brightness temperature (BT). The cloud-, cloud shadow-, and snow-contaminated pixels were excluded in the analysis according to the metadata, and a further screening procedure based on the RIRLS (Robust Iteratively Reweighted Least Squares) technique was implemented. The time series LSTs (TSLSTs) was derived using the single channel algorithm because it required only the parameters of water vapor and land surface emissivity and had a reported error close to 1 K. The LULC classification and change detection was accomplished by using the Continuous Change Detection and Classification (CCDC) algorithm. The TSLSTs were further decomposed into the seasonal and trend components by an additive model. Results showed that the overall LULC classification and change detection accuracies were 89% and 92%, respectively. Urban growth was mainly observed in Fulton County and Gwinnett County. High-intensity urban land had the largest mean LST value of 294.9 K and yearly amplitude of 17.4 K. A comparison of the trend component between urban and non-urban land covers showed a difference of 1.8 K per decade. Next, temporal thermal signatures were created to characterize and quantify the impact of urban LULC changes. Decomposition analysis showed that the conversion of evergreen forest to medium-intensity urban land generated the largest difference in annual LST variation (5.7 K) and the largest trend difference (0.0004 K/day).