Land Surface Temperature Estimation Research Articles

Exploration and monitoring geothermal activity using Landsat ETM + images: A case study at Aso volcanic area in Japan

Thermal activity monitoring in and around active volcanic areas using remote sensing is an essential part of volcanology nowadays. Three identical approaches were used for thermal activity exploration at Aso volcanic area in Japan using Landsat ETM+images. First, the conventional methods for hydrothermal alteration mapping were applied to find the most active thermal region after exploring geothermal indicator minerals. Second, we found some thermally highly anomalous regions around Nakadake crater using land surface temperature estimation. Then, the Stefan-Boltzmann equation was used for estimating and also monitoring radiative heat flux (RHF) from the most active region of about 8km2 in and around Nakadake crater in the central part of the Aso volcano. To fulfill the required parameter in the Stefan-Boltzmann equation for radiative heat flux, the NDVI (Normalized differential vegetation index) method was used for spectral emissivity, and the mono-window algorithm was used for land surface temperature of this study area. The NDVI value was used to divide land-cover in the study area into four types: water, bare ground, mixed and vegetated land. The bare land was found within the most active region. Vegetation coverage area showed an inverse relationship with total RHF in this study as health of thermally stressed vegetation supports this relationship. The spatial distribution of spectral emissivity ranged from 0.94 to 0.99 in our study. Land surface temperature was estimated using a mono-window algorithm and was highest LST in 2008 and lowest in 2011. The results of RHF showed that the highest pixel RHF was found to be about 296W/m2 in 2008. Total RHF was obtained of about 607MW in 2002 and the lowest was about 354MW in 2008. The RHF anomaly area was found the highest in 2002 and was lowest in 2011. The highest total heat discharge rate (HDR) obtained about 3918MW in 2002 and lowest total HDR about 2289MW in 2008 from this study area. But in the case of Nakadake crater alone, the higher thermal activity was observed in 2008 and was less in 2004. The study showed that Landsat thermal infrared is the best option for thermal activity exploration and monitoring at Aso volcano as well as in any active volcano region considering high efficiency and low cost.

Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data

Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000–2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of ±2 °C than MODIS with an error of ±3 °C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 °C & for MODIS data is 3.7 °C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m2 and 62 W/m2 respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect.

A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate regional soil moisture. However, the application of the retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, many methods were proposed to downscale microwave soil moisture data. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature (LST), vegetation index and surface albedo are key parameters. However, due to the uncertainty in absolute LST estimation, this approach is partly dependent on the accuracy of LST estimation. To eliminate the impacts of LST estimation, an improved downscaling method is proposed in this study to downscale Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product with visible and thermal data of Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI). Two temperature temporal variation parameters related to soil moisture, including mid-morning rising rate and daily maximum temperature time, are introduced in the proposed method to replace LST. The proposed method and the traditional method are both applied to the Iberian Peninsula area for July and August 2007. Comparison of the two results shows that the coefficient of determination (R-squared) has an average improvement of 0.08 and the root mean square error has a systematic decrease. The downscaled soil moisture by the proposed method was validated by REMEDHUS soil moisture network in the study area, and site specific validation gets poor correlation between the two datasets because of the low spatial representativeness of site measurement for one MSG-SEVIRI pixel. Although the comparisons at 15 km and network scale show an improvement over the site specific comparison, it is found that the downscaling method systematically degrades the accuracy in soil moisture data, with a R-squared of 0–0.4 and 0.218 for the downscaled data set against 0.7–0.8 and 0.571 for AMSR-E data at 15 km scale and the network scale respectively.

Estimating LST Using a Vegetation-Cover-Based Thermal Sharpening Technique

Vegetation-cover-based thermal sharpening techniques have mostly been developed and tested in agricultural areas. Overlooking the impact of soil moisture on surface temperatures is a common problem in these algorithms. This letter developed a vegetation thermal sharpening method for the City of Indianapolis, Indiana, USA, and estimated land surface temperature by disaggregated Landsat Thematic Mapper thermal infrared data from 120 to 30 m. The root-mean-square error was yielded at 1.90°C and 1.91°C using NDVI and fractional vegetation cover as predictors, respectively. The error of the estimation was overlaid with a soil moisture map, which was derived based on the surface energy balance modeling. The pixels with large errors were largely distributed in the areas with low soil moisture. These areas were covered by impervious surfaces such as major roads, commercial land, and the airport. This result suggested that in the urban areas, besides vegetation cover and soil moisture, impervious surfaces must be incorporated in developing any future thermal sharpening techniques. The incorporation of population density and per capita consumption of energy may provide further improvements in the estimation.

Assimilation of surface‐sensitive infrared radiances over land: Estimation of land surface temperature and emissivity

AbstractWhilst the assimilation of radiances from infrared sounding spectrometers such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS) over the sea is well established, observations over land have, until recently, only been assimilated at wavelengths insensitive to the surface. This was due to uncertainties in the infrared emissivity and skin temperature of the land. A method is described for analyzing the land surface emissivity spectrum in terms of its principal components in order to allow the assimilation of land‐sensitive IASI channels and the results of the implementation of this scheme in an operational numerical weather prediction (NWP) system are presented. The use of additional IASI data over land resulted in small improvements in global NWP skill, mainly affecting near‐surface temperature and mid‐tropospheric water vapour fields.

Development of Split-Window Algorithm for Land Surface Temperature Estimation From the VIRR/FY-3A Measurements

This letter addressed the development of split-window algorithm to estimate land surface temperature (LST) from the measurements acquired by the Visible and Infrared Radiometer on FengYun 3A using radiative transfer modeling experiment with the moderate spectral resolution atmospheric transmittance algorithm and computer model and the SeeBor V5.0 database. To improve the accuracy, the total precipitable water and the mean of land surface emissivities (LSEs) and LST were divided into several subranges. The split-window algorithm was applied to the Northeastern China area (115°E-135°E, 40°N-55°N), and then, the estimated LSTs were cross-validated with the Terra Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) LST and Emissivity (LST/E) V5 products: MOD11C1 V5 and MOD11_L2 V5. The results show that the LSTs in this work are averagely consistent with the MODIS/Terra LST/E V5 products with accuracy better than 1.0 K: The errors are 0.5 ± 0.9 K and 0.0 ± 0.9 K for daytime and nighttime, respectively, when the retrieved LSTs were compared to the MOD11C1 V5 product, while the errors are 0.6 ± 0.9 K and 0.0 ± 0.9 K for daytime and nighttime, respectively, when the results were compared to the MOD11_L2 V5 product.

Atmospheric corrections of passive microwave data for estimating land surface temperature

Quantitative analysis of the atmospheric effects on observations made by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) has been performed. The differences between observed brightness temperatures at the top of the atmosphere and at the bottom of the atmosphere were analyzed using a database of simulated observations, which were configured to replicate AMSR-E data. The differences between observed brightness temperatures at the top of the atmosphere and land surface-emitted brightness temperatures were also computed. Quantitative results show that the atmosphere has different effects on brightness temperatures in different AMSR-E channels. Atmospheric effects can be neglected at 6.925 and 10.65 GHz, when the standard deviation is less than 1 K. However, at other frequencies and polarizations, atmospheric effects on observations should not be neglected. An atmospheric correction algorithm was developed at 18.7 GHz vertical polarization, based on the classic split-window algorithm used in thermal remote sensing. Land surface emission can be estimated with RMSE = 0.99 K using the proposed method. Using the known land surface emissivity, Land Surface Temperature (LST) can be retrieved. The RMSE of retrieved LST is 1.17 K using the simulated data.

Landsat 위성영상을 이용한 지표온도차 추정기법

Difficulties of emissivity determination and atmospheric correction degrade the estimation accuracy of land surface temperature (LST). That is, since the emissivity determination of land surface material and the correction of atmospheric effect are not perfect, it is very difficult to estimate the precise LST from a thermal infrared image such as Landsat TM and ETM+, ASTER, etc. In this study, we propose an efficient method to estimate land surface temperature difference (LSTD) rather than LST from Landsat thermal band images. This method is based on the assumptions that 1) atmospheric effects are same over a image and 2) the emissivity of vegetation region is 0.99. To validate the performance of the proposed method, error sensitive analysis according to error variations of reference land surface temperature and the water vapor is performed. The results show that the estimated LSTD have respectively the errors of ±0.06K, ±0.15K and ±0.30K when the water vapor error of ±0.302g/cm2 and the radiance differences of 0.2, 0.5 and 1.0Wm-2sr-1μm are considered. And also the errors of the LSTD estimation are respectively ±0.037K, ±0.089K, ±0.168K in the reference land surface temperature error of ±2.41K. Therefore, the proposed method enables to estimate the LSTD with the accuracy of less than 0.5K.

Development and verification of a non-linear disaggregation method (NL-DisTrad) to downscale MODIS land surface temperature to the spatial scale of Landsat thermal data to estimate evapotranspiration

A nonlinear method (NL-DisTrad) was developed and tested to disaggregate satellite-derived estimates of land surface temperature of MODIS (Moderate Resolution Imaging Spectrometer) with a resolution of 960m to the scale of Landsat 7 ETM+ (Enhanced Thematic Mapper Plus) at 60m. This method uses the relationship that is captured at the hot edge pixels in the feature space between the Normalised Difference Vegetation Index (NDVI) and the land surface temperature (LST) at a coarse resolution to disaggregate the LST to a finer resolution. The residuals that are generated at the coarse resolution are modelled using an Artificial Neural Network model (ANN), and the resulting residuals are added to the disaggregated LST at a fine resolution. The ANN model was built using the NDVI from the neighbourhood pixels. The hypothesis is that the LST of a pixel will not only be affected by the vegetation within the pixel but also by the vegetation of surrounding pixels. The performance of this hybrid model NL-DisTrad (Hot edge model+ANN model) is assessed by comparing the results to the existing disaggregation method, TsHARP, and the observed Landsat LST. The NL-DisTrad disaggregation results were comparable to the observed Landsat LST even for pixels with non-uniform vegetation. The statistical analysis indicated that the proposed model disaggregates the LST better than TsHARP, based on the high Nash Sutcliffe Efficiency (NSE>0.80) and low root mean square error value (RMSE<0.96K). Furthermore, using SEBAL (Surface Energy Balance Algorithm for Land), it was found that the estimates of daily evapotranspiration (ET) from the LST that were disaggregated using NL-DisTrad were comparable to the ET estimates from the observed Landsat LST data. As the disaggregation method NL-DisTrad only needs the LST–NDVI relationship at the coarse resolution, the model could be used to disaggregate the coarse resolution MODIS temperature data to the fine resolution of satellites such as IRS-P6 or SPOT-5 that do not carry any thermal sensors.

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm−2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil.

Surface Temperature Downscaling From Multiresolution Instruments Based on Markov Models

The spatial resolution of thermal infrared (TIR) instruments is often not sufficient for many applications, but this low resolution is counterbalanced by the high temporal resolution (for example the SEVIRI instrument onboard the European Meteosat 8 and 9 presents a spatial resolution of 3 km <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \times$</tex></formula> 3 km at nadir and a temporal resolution of 15 mn). At kilometric scales, the observed pixel is generally heterogeneous in terms of land cover, and the temperatures of the different components may present large discrepancies. This paper presents a methodology to infer the temperatures of the various land cover/use classes composing a mixed pixel, from a whole pixel measurement. To infer intra-pixel temperature, information on the mixture within each low resolution pixel, e.g., the proportions of the land cover types derived from high spatial resolution imaging, account for a first constraint. However, in the absence of supplementary constraints, the number of unknown variables is greater than the number of measurements, and there is not uniqueness of the solution. Thus, we propose to take advantage of a priori knowledge provided by a land surface model (LSM), and of the temporal and spatial correlation features of the surface temperature. We propose a new downscaling method for estimating sub pixel signal. It applies to TIR data and: the inversion procedure provides as a result, the land surface temperature (LST) temporal series of each land cover/use class (called endmember) constituting the coarse resolution pixel. Three kinds of a priori information have been introduced, namely 1) a first guess subpixel temperature derived from the SEtHyS LSM; 2) a Markov Random Chain model of the surface temperature temporal dependencies from times <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$t$</tex></formula> to <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$t + 1$</tex></formula> ; 3) a Markov Random Field model of the spatial dependencies between endmember temperatures. Then, the “Maximum A Posteriori” estimator provides the most likely endmember temperatures, given 1) the observed coarse resolution temperatures, 2) the composition of the pixels in terms of “land cover/land use,” and 3) the LSM first guess subpixel temperature values, 4) the a priori spatial and temporal Markov models. The performance of this new method has been first evaluated on simulated data (random Gaussian variables with means equal to endmember temperatures simulated using LSM). The method accuracy versus the observation errors and the number of endmembers was analyzed. The algorithm was then run on actual data, namely Meteosat SEVIRI Land Surface products acquired over an agricultural region in southeastern France. The performance evaluation was done by comparing the subpixel LST estimations to the high-resolution temperatures provided by the Terra/ASTER instrument. Due to the huge bias between sensors ( <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$ \sim$</tex></formula> 4 K), an intercalibration preprocessing between SEVIRI and ASTER was done. In this case, the achieved RMSE is lower than 2 K.

Development and validation of a model to estimate postharvest losses during transport of tomatoes in West Africa

In an effort to better understand postharvest losses associated with low-cost tomato transport in West Africa we present a spatial–temporal simulation model that links the prevailing outside weather conditions, estimated using satellite meteorology, to the microclimate observed inside truck trailers (cryptoclimate) to determine the deterioration in tomato quality during transport. Tomatoes from Burkina Faso are transported under sub-optimal circumstances to important Ghanaian markets; during a number of these transports conditions for the tomato cargo inside trucks were measured while conditions outside the trucks were monitored by means of weather satellites. The presented tomato quality model analytically combines cryptoclimate, duration since harvest, and kinetic modelling to arrive at estimated firmness. Firmness of tomatoes in transport was monitored with a portable penetrometer in selected trucks, augmented with additional (acoustic firmness) data collected in a climate chamber. Half of these observations were used to calibrate a firmness loss model and the other half to validate the simulation results.Our results indicate that outside weather during transport can be reasonably well estimated using satellite meteorology. The model performance for the estimation of outside global radiation (Rg) and land-surface temperature (LST) were found to be satisfactory, with a RMSE=87.98Wm−2; bias=57.39Wm−2 and RMSE=2.95°C; bias=0.91°C, respectively. Results for the cryptoclimate estimation (conditions inside the trucks) for temperature, relative humidity, and light intensity were as follows: R2=0.77, RMSE=4.18°C (Tincargo); R2=0.84, RMSE=19.59% (RHincargo); and R2=0.9, RMSE=137.31lx (LIincargo). The postharvest loss model that relies on these estimates as its input explained on average 77% of the variance in observed tomatoes firmness, with total product losses ranging from 30% to 50% when integrated over the entire transportation period.With the accuracy of the model quantified and the causality of losses partially demonstrated, we argue that the simulation model can be useful as an economic resistor in transport optimization studies to investigate the cost–benefit of various measures to reduce postharvest losses. Such studies could help to illustrate what net gains can be expected if delays along the transportation route are reduced, cargo conditions are semi-controlled (e.g. pre-cooling treatment), or if a different transport schedule is adopted. The model may also be used to show the impact of different climate change scenarios on postharvest losses.

Estimating very high resolution urban surface temperature using a spectral unmixing and thermal mixing approach

Land surface temperature (LST) plays a critical role in characterizing energy exchanges of the Earth's surface and atmosphere. Recent advances in thermal infrared (TIR) remote sensing technology enable the emergence of airborne very-high-resolution (VHR) TIR sensors to identify detailed LST distribution for environmental, geological and urban applications. However, the usage of airborne VHR TIR data may be limited by its high cost, long acquisition period, extensive data processing, etc. A cost-effective alternative could be VHR LST estimation. We proposed a physically based method, referred to as the VHR spectral unmixing and thermal mixing (VHR-SUTM) approach, to estimate LST at the meter level. Particularly, considering both spectral and thermal properties, spectral unmixing was employed to estimate fractional urban compositions for a comprehensive representation of heterogeneous urban surfaces. Further, VHR LST was modeled as a summation of the thermal features of representative urban compositions weighted by their respective abundances. Results suggest a high agreement between the resampled VHR LST estimates and the retrieved LSTs. With relatively high estimation accuracy (RMSE of 2.02K and MAE of 1.51K), the VHR-SUTM technique could serve as a promising and practical method for various applications in urban and environment studies.

A comparative study of land surface temperature retrieval methods from remote sensing data

The main purpose of this paper is to describe, compare, and analyze the various extraction methods for land surface temperature (LST) in terms of their computational algorithms, their different input parameters, and their relative accuracy to make them more readily usable by a broader cross-section of nontechnical practitioners. Due to the heterogeneity of most natural land surfaces, the atmospheric influence, and a wide variety of satellite sensors, the estimation and validation of LST can be difficult. Furthermore, the large number of algorithms developed to deal with this heterogeneity has led to widespread confusion on how and when to use one algorithm versus another. This paper provides a concise, but thorough, overview of the different algorithms used for the estimation of land surface temperature as well as a comparative list of methods and associated parameters that facilitate, to the general user, the selection and application of the most appropriate method for LST extraction given the situation at hand. We restricted our analysis for the single-channel algorithms to two models. We included a two-channel algorithm (or split-window when it is applied in the region 10–12.5 µm) according to the literature. The Temperature Emissivity Separation algorithm was also taken into account. The determination of the key parameters needed to execute these algorithms is presented.

Enhancing model‐based land surface temperature estimates using multiplatform microwave observations

Land surface temperature plays an important role in surface processes and is a key input for physically based retrieval algorithms of soil moisture and evaporation. This study presents a framework for using independent estimates of land surface temperature from five microwave satellite sensors to improve the accuracy of land surface temperature output from a numerical weather prediction system in an off‐line (postprocessing) analysis. First, structural differences in timing and amplitude of the temperature signal were addressed. Then, satellite observations were assimilated into an auto‐regressive error model, formulated to estimate errors in the numerical weather prediction output. Errors in daily minimum and amplitude were treated separately. Results of this study provide new insights about potential added benefits of preprocessing and off‐line assimilation of microwave remote sensing‐based and model‐based temperature retrievals. It is shown that the satellite observations may be used to reduce errors in surface temperature, particularly for day‐time hours. Preprocessing is responsible for the bulk of this reduction in temperature error; data assimilation is shown to further reduce the random temperature error by a few tenths of a Kelvin, accounting for a 10% reduction in RMSE.

Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications

Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications The effect of urban heating on estimates of global average land surface temperature is studied by applying an urban-rural classification based on MODIS satellite data to the Berkeley Earth temperature dataset compilation of 36,869 sites from 15 different publicly available sources. We compare the distribution of linear temperature trends for these sites to the distribution for a rural subset of 15,594 sites chosen to be distant from all MODISidentified urban areas. While the trend distributions are broad, with one-third of the stations in the US and worldwide having a negative trend, both distributions show significant warming. Time series of the Earth’s average land temperature are estimated using the Berkeley Earth methodology applied to the full dataset and the rural subset; the difference of these is consistent with no urban heating effect over the period 1950 to 2010, with a slope of -0.10 ± 0.24/100yr (95% confidence).

Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products

Accurate estimation of spatially distributed air temperature (Ta) is useful for a wide range of disciplines such as hydrology, meteorology and ecology. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (Ts) products hold the potential to get spatial estimates of land surface temperature at high temporal (daily) and spatial resolution (1km) across the world. In this paper, the possibility of retrieving high-resolution Ta data from MODIS Ts data on both Terra and Aqua satellites was investigated over Xiangride River basin in the north Tibetan Plateau. The results of statistical analysis show that during the night MODIS Ts data on Terra satellite provides a good estimation of daily minimum air temperature with RMSE=2.97°C, MAE=2.37°C, and r=0.94, while during the day the difference between Terra MODIS Ts data and daily maximum air temperature is large with RMSE=7.45°C, MAE=6.21°C, and r=0.83. In order to improve the accuracy of the estimation of daily maximum air temperature, the temperature-vegetation index (TVX) method was used in this paper. By lowering the threshold for the negative correlation coefficient between NDVI (Normalized Difference Vegetation Index) and Ts to −0.1, the TVX method was applied to MODIS land products to estimate daily maximum air temperature successfully for the first time. The validation results show that the TVX method has improved the accuracy of daily maximum air temperature significantly with RMSE=3.79°C, MAE=3.03°C, and r=0.83. Finally, the diurnal air temperature variation was simulated using a simple sine curve model for the period 21:00–9:00UCT. The simulation results were in good agreement with observed values with RMSE=3.43°C, MAE=2.54°C, and r=0.86. The results of this study show that daily maximum and minimum air temperature can be retrieved effectively from MODIS land surface products through the use of TVX method.

Generation of Emissivity and Land Surface Temperature Maps Using MODIS TIR Data for Lithological Mapping over the Singhbhum-Orissa Craton

Abstract The present study was undertaken with four fold objectives, namely, (i) to estimate land surface temperature using MODIS TIR data; (ii) to calculate relative emissivities from MODIS TIR data; (iii) to identify various lithologies based on relative emissivity and land surface temperature estimation; and finally, (iv) to carry out comparative assessment analysis between the prepared lithological map and the published lithological map. The land surface temperatures for different pixels were estimated using two methods, viz., Reference Channel and Emissivity Normalization; whereas, relative emissivities were calculated by applying three methods, viz., Reference Channel, Emissivity Normalization and Alpha Residual. Lithological maps were subsequently prepared based on the estimated land surface temperatures and relative emissivity values. The present study shows that the Emissivity Normalization method gives the best results for land surface temperature estimation and also for lithological discrimination based on emissivity estimation. Twenty-four lithounits demarcated by the present study match with those of the published map, while four lithounits of the published map could not be identified in the present study. On the other hand, six additional unclassified lithounits could be demarcated in the present study, which need to be crosschecked by field study.

Determination of Land Surface Temperature (LST) and Potential Urban Heat Island Effect in Parts of Lagos State using Satellite Imageries

Temperature is an important component of the climate. The temperature of a developing city or state is constantly changing. The trend in temperature change in Nigeria is not consistent. Changes in temperature appear to be closely related to concentrations of atmospheric carbon dioxide. The degree of concentration depends on human interventions and the amount of sunlight reaching the earth's surface. Lagos State particularly in recent time has experienced decrease in vegetation and water pervious surfaces, which reduces surface temperature through evapotranspiration. This is as a result of rapid urbanization arising from inundating rural-urban migration. One of the implications is that anthropogenic heat is released to the environment due to energy consumption and increased impervious surface coverage thereby increasing the surface and atmospheric temperatures. LandSat Satellite imageries have been used to estimate Land Surface Temperature (LST) and urban thermal conditions. The mean LST result shows that, there is a significant increase in the temperature values from 1984 to 2002 (28.40oC-28.86oC). However, in 2006 the temperature decreases significantly to 28.37oC below 2002 temperatures in all LGAs. This variation could be attributed to the economic crisis/power outage in Nigeria which left manufacturing companies out of production/manufacturing between 2002 and 2006. Findings from this study reveals that there is a relationship between the surface temperature and the various Land Cover types. It shows a broad classification of the Land Cover types into Water bodies, Vegetation and Built-up areas respectively. With the spatial resolution and temporal coverage of two Landsat data of the environment, the derivation of the temperature information was achieved.