Land Surface Temperature Estimation Research Articles

Land Surface Temperature Retrieval Using Nighttime Mid-Infrared Channels Data From Airborne Hyperspectral Scanner

Compared with thermal infrared ( $8{-}14\;\upmu{\rm{m}}$ ) spectrum, mid-infrared (MIR) spectrum is less sensitive to land surface emissivity (LSE) for estimating land surface temperature (LST). This work addressed the retrieval of LST from two adjacent MIR ( $3{-}5\;\upmu{\rm{m}}$ ) night-time airborne hyperspectral imager (AHS) simulated data with a split-window method, which can be expressed as a linear combination of the brightness temperature measured in two adjacent MIR channels with coefficients depending on LSE, view zenith angle (VZA), and water vapor content (WVC). Meanwhile, the LST retrieval accuracy for various channel combination was investigated and it was noted that the AHS channels 66 ( $3.5 {-} 4.25\;\upmu{\rm{m}}$ ) and 68 ( $4.25 {-} 5.0\;\upmu{\rm{m}}$ ) were the optimal channels for LST retrieval with a root-mean-square error (RMSE) less than 0.4 K for dry atmosphere and less than 0.5 K for wet atmosphere. Finally, the sensitivity analysis in terms of the instrumental noise, the uncertainties of LSE, and WVC were performed. It is worth noting that the combination of CH66 and CH68 performed well, and the LST retrieval errors were less than 0.5, 0.2, and 0.3 K caused by an noise equivalent delta temperature ( ${\rm{NE}}\Delta {\rm{T}}$ ) of 0.33 K, WVC error of 20%, and LSE error of 0.01, respectively.

Estimation of Land Surface Temperature under Cloudy Skies Using Combined Diurnal Solar Radiation and Surface Temperature Evolution

Land surface temperature (LST) is a key parameter in the interaction of the land-atmosphere system. However, clouds affect the retrieval of LST data from thermal-infrared remote sensing data. Thus, it is important to determine a method for estimating LSTs at times when the sky is overcast. Based on a one-dimensional heat transfer equation and on the evolution of daily temperatures and net shortwave solar radiation (NSSR), a new method for estimating LSTs under cloudy skies (Tcloud) from diurnal NSSR and surface temperatures is proposed. Validation is performed against in situ measurements that were obtained at the ChangWu ecosystem experimental station in China. The results show that the root-mean-square error (RMSE) between the actual and estimated LSTs is as large as 1.23 K for cloudy data. A sensitivity analysis to the errors in the estimated LST under clear skies (Tclear) and in the estimated NSSR reveals that the RMSE of the obtained Tcloud is less than 1.5 K after adding a 0.5 K bias to the actual Tclear and 10 percent NSSR errors to the actual NSSR. Tcloud is estimated by the proposed method using Tclear and NSSR products of MSG-SEVIRI for southern Europe. The results indicate that the new algorithm is practical for retrieving the LST under cloudy sky conditions, although some uncertainty exists. Notably, the approach can only be used during the daytime due to the assumption of the variation in LST caused by variations in insolation. Further, if there are less than six Tclear observations on any given day, the method cannot be used.

A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures

This study investigates the properties of the diurnal cycle of microwave brightness temperatures (TB), namely the phase and the amplitude, and their variability in time and space over the globe to infer information on key land surface parameters like changes in soil texture spatial distribution, soil moisture conditions, and vegetation density. The phase corresponds to the lag between Land Surface Temperature (LST) and TB diurnal cycles. The amplitude is determined as the difference between the maximum and the minimum of TB diurnal cycle. The diurnal cycle of TB was constructed using observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I). The latter offer a series of sensors, namely, F13, F14, and F15 that were used in this study for a higher temporal coverage and more accurate diurnal cycle determination. LST estimates, which are available every 3h from the International Satellite Cloud Climatology Project (ISCCP) database were used to build the LST diurnal cycle. ISCCP LST data is an infrared-based temperature with almost no penetration and is the representative of top skin temperature.The analyses of the diurnal cycles showed that the diurnal amplitude of TB decreases as the vegetation density increases, especially in the case of low frequencies which penetrate deeper into the canopy which makes them more sensitive to changes in vegetation density. The interannual variations of TB diurnal amplitudes were also in agreement with the seasonality of the vegetation cover. Over desert and rain forest regions where surface conditions do not vary significantly throughout the year, the changes in diurnal amplitudes were the lowest. A relationship between phase and amplitude values was established. It was found that the amplitude of TB diurnal cycle decreases when the phase lag increases. The spatial distribution of the determined diurnal properties, namely, phase and amplitude of TB, showed an agreement with lithology maps in desert areas. Lower TB amplitudes were observed over regions with loose siliceous rocks. Phase lag values between 1.5 and 3h corresponded to 83% of the class “loose siliceous rocks” in the Sahara Desert, which corroborates the potential of using the diurnal properties of TB as an indicator of land surface parameters.

Estimation of Air Surface Temperature From Remote Sensing Images and Pixelwise Modeling of the Estimation Uncertainty Through Support Vector Machines

The knowledge of air temperature near the Earth's surface plays a relevant role in weather and climate studies as well as in the framework of solar energy management; e.g., for identifying the most suitable locations for a new solar installation or monitoring the performance of existing systems. Remote sensing allows air temperature to be estimated on a spatially distributed basis, thus complementing the spatially sparse observations collected by ground micro-meteorological stations. In this paper, a novel approach to periodic (e.g., daily or monthly) air temperature estimation from satellite images based on support vector machines (SVMs) is proposed. A recently developed SVM-based approach to supervised land and sea surface temperature estimation using satellite images is generalized to the case of air temperature and integrated with case-specific techniques aimed at computing periodic statistics of air temperature using the expectation-maximization algorithm. The method is fully automated and allows the statistics of the estimation error to be modeled on a pixelwise basis. This last result is accomplished by combining nonstationary multidimensional stochastic processes and Clark's variance approximation. The method is experimentally validated with MSG-SEVIRI data acquired over Provence-Alpes-Côte d'Azur (France).

Derivation of split-window algorithm to retrieve land surface temperature from MSG-1 thermal infrared data

One of the most important parameters in the exchange of energy between the ground and the atmosphere is the land surface temperature (LST). In this work, a split-window algorithm for deriving LST from the first Meteosat Second Generation satellite (MSG-1) data using the two thermal infrared channels IR10.8 and IR12.0 is proposed and validated with in-situ measured temperatures. This algorithm is obtained starting from the radiance transfer equation and requires the knowledge of total atmospheric water vapor content (TAWV) and the surface emissivity (ε10.8 and ε12). First, we have simplified the Planck function to retrieve the radiance at MSG-1 from the temperature. Then, the Roberts model was used to create a relationship between atmospheric transmittance and TAWV. Next, sensitivity analysis of the algorithm indicates that the possible error of viewing angle has relatively insignificant impact on the probable LST estimation error, which is sensible to the possible error of ground emissivity and TAWV. Finally, two methods have been used to validate the proposed algorithm. On the one hand, the results show a root mean square error (RMSE) equals 0.79 K and an average accuracy equals 0.45 K for the comparison with another algorithm. On the other hand, the results show a RMSE equals 2.75 K and an average accuracy equals 1.96 K for the comparison with in-situ measurements. We conclude that the proposed algorithm is able to provide an accurate LST.

A new single-channel method for estimating land surface temperature based on the image inherent information: The HJ-1B case

At present, several algorithms have been developed to retrieve land surface temperature (LST) from remotely sensed imageries. Single-channel algorithms to estimate LST from one thermal band have advantage over other methods when the imageries with only one thermal band are used. However, accurate inputs including atmospheric parameters and land surface emissivity, which are always inaccessible to general users, are indispensable to the applications of the current single-channel algorithms. To solve this problem, a new single-channel method (an image based single-channel method called IBSC) based on the information inherent within the image itself, is proposed in this paper. The IBSC method is intended to retrieve LST directly from the sensor detected radiance, even with no accurate information about land surface emissivity and atmospheric parameters. Several HJ-1B imageries covering the Pearl River Delta and Wuhan City, China, were selected for LST estimation to assess the capability of the IBSC method. Results show that, by means of the IBSC method, sea/water surface temperature was retrieved with general bias about 1K compared with the MODIS products and the in situ measurements. Meanwhile, the traditional single-channel methods provided less accurate LST results when the plausibly estimated atmospheric parameters and surface emissivity were used as inputs. The inherent uncertainties and errors in the MODIS atmospheric products (e.g. MOD 05) are largely attributed to the incorrectness or insufficient improvement of the final LST results. Therefore, when there is no accurate information about atmospheric parameters and surface emissivity, the IBSC method is a good alternative for general users to make full use of the remotely sensed imageries with only one thermal band. Due mainly to the limited access to abundantly valid field data over land, in this paper, the 33 in situ measures over lakes as well as the sea/water surface temperature from MODIS products were only used to assess the retrieved HJ-1B results. The preliminary assessment positively suggests the applicability of the new method. However, to verify the accuracy and robustness of the IBSC method, more experiments and abundant reference data are required, especially for surface temperature over land. This innovative methodology may also be suitable for other sensors, such as Landsat 5/7/8.

Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India).

Over the past five decades, the fragile wetland ecosystem surrounding the city of Kolkata has witnessed extensive changes in the name of urban development. In this study, we elaborate relationships among biophysical parameters and land surface temperature (LST) in Kolkata city and nearby surrounding areas where rapid urbanization has occurred. LST and associated surface physical characteristics were assessed using Landsat images acquired for the years 1989, 2006, and 2010. The satellite data was used to study the spatiotemporal urban footprint and correlation among normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and LST. Land use land cover (LULC) maps prepared using supervised classification had overall accuracy of 90, 88, and 86% and kappa coefficient of 0.8726, 0.8455, and 0.8212 for 1989, 2006, and 2010, respectively. The spatial expansion as a consequence of increasing urban population is 108.94km(2) over past two decades. The urban built-up in and around the city extends up to 88.71km(2) in 1989, 144.64km(2) in 2006, and 197.65km(2) in 2010. These changes have attributed in elevating surface temperature in the study region. Analysis of biophysical parameters shows LST and NDBI having a positive correlation, LST and NDVI having negative correlation, while NDBI and NDWI having a perfectly negative correlation. Satellite estimated temperatures of the surface show a warming trend evident from increasing mean surface temperature values from 27.36°C in 1989 to 30.025°C in 2006 and 33.023°C in 2010. The magnitude and extent of the estimates of LST are consistent with the urbanization pattern throughout the city and adjoining areas.

High-resolution land surface modeling utilizing remote sensing parameters and the Noah UCM: a case study in the Los Angeles Basin

Abstract. In the current work we investigate the utility of remote-sensing-based surface parameters in the Noah UCM (urban canopy model) over a highly developed urban area. Landsat and fused Landsat–MODIS data are utilized to generate high-resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter data sets are directly substituted for the land-use/lookup-table-based values in the Noah-UCM modeling framework. Model performance in reproducing ET (evapotranspiration) and LST (land surface temperature) fields is evaluated utilizing Landsat-based LST and ET estimates from CIMIS (California Irrigation Management Information System) stations as well as in situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE = 22.06 mm month−1). Results indicate that implemented satellite-derived parameter maps, particularly GVF, enhance the capability of the Noah UCM to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE = 11.77 mm month−1). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model's capability to predict the LST differences between fully vegetated pixels and highly developed areas.

Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution

Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008–2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes.

Developing a temporally land cover-based look-up table (TL-LUT) method for estimating land surface temperature based on AMSR-E data over the Chinese landmass

The land surface temperature (LST) is an important parameter when studying the interface between the atmosphere and the Earth's surface. Compared to satellite thermal infrared (TIR) remote sensing, passive microwave (PMW) remote sensing is better able to overcome atmospheric influences and to estimate the LST, especially in cloudy regions. However, methods for estimating PMW LSTs at the country and continental scales are still rare. The necessity of training such methods from a temporally dynamic perspective also needs further investigations. Here, a temporally land cover based look-up table (TL-LUT) method is proposed to estimate the LSTs from AMSR-E data over the Chinese landmass. In this method, the synergies between observations from MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-E (Advanced Microwave Scanning Radiometer for EOS), which are onboard the same Aqua satellite, are explored. Validation with the synchronous MODIS LSTs demonstrates that the TL-LUT method has better performances in retrieving LSTs with AMSR-E data than the method that uses a single brightness temperature in 36.5GHz vertical polarization channel. The accuracy of the TL-LUT method is better than 2.7K for forest and 3.2K for cropland. Its accuracy varies according to land cover type, time of day, and season. When compared with the in-situ measured LSTs at four sites without urban warming in the Tibet Plateau, the standard errors of estimation between the estimated AMSR-E LST and in-situ measured LST are from 5.1K to 6.0K in the daytime and 3.1K to 4.5K in the nighttime. Further comparison with the in-situ measured air temperatures at 24 meteorological stations confirms the good performance of the TL-LUT method. The feasibility of PMW remote sensing in estimating the LST for China can complement the TIR data and can, therefore, aid in the generation of daily LST maps for the entire country. Further study of the penetration of PMW radiation would benefit the LST estimations in barren and other sparsely vegetated environments.

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

Studies of land surface temperature distribution using multispectral image Landsat

Land surface temperature is one of the most important factors in urban climatology studies and human - environmentinteractions. Warmer air in urban area - urban heat island is a pressing issue for all big cities. Besides, land surfacetemperature is also an important factor when monitoring soil moisture. Ground-based observations reflect only thermalcondition of local area around the station and in fact cannot establish the number of meteorological stations withexpected density due to the high cost. Remote sensing technology with advantages such as wide area coverage andshort revisit interval has been used effectively in the study of land surface temperature distribution. The study indicateshow to estimate surface temperature using LANDSAT satellite data. With 120m (TM), 60m (ETM+) and 100m(LANDSAT 8) spatial resolution, thermal infrared image LANDSAT performance applications in the region study. Thisarticle also considers the method determining surface emissivity and building program LST for calculating land surfacetemperature. The land surface temperature distribution map and the analyses of thermal - land cover relationships canbe used as the reference for urban planning and the solution to the reduction of heat island effect.ReferencesAlipour T., Sarajian M.R., Esmaseily A., 2004. Land surface temperature estimation from thermal band of LANDSAT sensor, case study: Alashtar city. The international archives of the Photogrammetry, Remote sensing and Spatial information sciences, Vol. XXXVIII-4/C7. Balling R.C., Brazel S.W., 1988. High - resolution surface temperature patterns in a complex urban Terrain, Photogrametric engineering and Remote sensing, Vol. 54, No.9, pp. 1289 - 1293. Carnahan W.H., Larson R.C., 1990. An analysis of an urban heat sink. Remote sensing of Environment, 33:65 - 71. Fei Yuan, Marvin E. Bauer, 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in LANDSAT imagery. Remote sensing of Environment 106:375 - 386. Gallo K.P., Owen T.W., 1998. Satellite - Based adjustments for the urban heat island temperature bias, Journal of applied meteorology, Vol. 38, pp. 806 - 813. Garcia Cueto O.R., Jauregui Ostos E., Toudert D., Tejeda Martinez A., 2007. Detection of the urban heat island in Mexicali and its relationship with land use. Atmosfera 20(2), pp. 111 - 131. Holben B.N., 1986. Characteristics of maximum value composite image from temporal AVHRR data. International Journal of Remote sensing, 7:1417 - 1434. Hyung Moo Kim, Beob Kyun Kim, Kang Soo You, 2005. A statistic correlation analysis algorithm between land surface temperature and vegetation index, International journal of information processing systems, Vol. 1, No. 1, pp. 102 - 106. Javed Maltick, Yogesh Kant, D.B. Bharath, 2008. Estimation of land surface temperature -over Delhi using LANDSAT-7 ETM+”, Journal Ind. Geophys. Union, Vol. 12, No. 3, pp. 131 - 140. Lê Đình Quang, 2005. Sự hình thành đảo nhiệt ở nội thành thành phố Hà Nội. Tạp chí Khí tượng Thủy văn, 530, trang 44 - 46. Lo C.P., D.A. Quattochi, J.C. Luvall, 1997. Application of high resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International journal of Remote sensing, 18:287 - 304. Luke Howard, 1833. The climate of London. International association for urban climate (IAUC), 285p. Oke T.R., 1979. Technical note No. 169: Review of urban climatology. World meteorological organization, Geneva, Switzerland, 43p. Sundara Kumar K., Udaya Bhaskar P., Padmakumari K., 2012. Estimation of land surface temperature to study urban heat island effect using LANDSAT ETM+ image”, International journal of Engineering Science and technology, Vol. 4, No. 2, pp. 771 - 778. Tran H., Yasuoka Y., 2004. Surface Climatic impacts of Urbanization in the Hochiminh city, Vietnam: An intergrated study with remote sensing and modeling. Proceedings of the 3rd ICUS international symposium “New technologies for urban safety of megacities in Asia”, Agra, India, October 18 - 19. Tran Hung, Daisuke Uchihama, Shiro Ochi, Yoshifumi Yasuoka, 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. International journal of applied Earth observation and Geoinformation, Vol. 8, pp. 34 - 48. Trần Thị Vân, Hoàng Thái Lan, Lê Văn Trung, 2009. Phương pháp viễn thám nhiệt trong nghiên cứu phân bố nhiệt độ đô thị. Tạp chí Các Khoa học về Trái Đất, T.31, (2), trang 168 - 177. Valor E., Caselles V., 1996. Mapping land surface emissivity from NDVI. Application to European African and South American areas. Remote sensing of Environment, 57, pp. 167 - 184. Van de Griend A.A., Owen M., 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface. International journal of remote sensing 14, pp. 1119 - 1131. LANDSAT Conversion to Radiance, Reflectance and At-Satellite Brightness Temperature (NASA).

Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product

Water vapour is the most variable constituent in the atmosphere which is responsible for serious noise in the optical satellite images. This research is focused on the vertical distribution of water vapour and deducing its possible effects on the atmospheric correction process. The vertical distribution of precipitable water vapour, water vapour mixing ratio with geopotential height and pressure were estimated through the weather research and forecasting (WRF) model by downscaling the National Center for Environmental Prediction (NCEP) global reanalysis product. In addition, the most widely used LANDSAT TM satellite image has been used for this assessment. The WRF model was applied with three domains centred on a LANDSAT captured image over the area. The 6S atmospheric correction code was utilised for viewing the effect of precipitable water vapour on satellite image correction. The analysis was conducted on two pressure levels (1,000 and 100 hPa) representing the troposphere and stratosphere, respectively. The validation of the atmospheric correction has been performed by estimating the land surface temperature (LST) over the Walnut Creek region and its comparison with the Soil Moisture Experiments in 2002 (SMEX02) LST field validation datasets. The overall analyses indicate a higher accuracy of LST repossession with 100 hPa corrected image.

Evaluating the impact of urban morphology configurations on the accuracy of urban canopy model temperature simulations with MODIS

AbstractSimulations of the urban environment contribute to assessments of current and future urban vulnerabilities to extreme heat events. The accuracy of simulations of the urban canopy can be degraded by inaccurate or oversimplified representations of the urban‐built environment within models. Using a 10 year (2003–2012) series of offline 1 km simulations over Greater Houston with the High‐Resolution Land Data Assimilation System (HRLDAS), this study explores the model accuracy gained by progressively increasing the complexity of the urban morphology representation in an urban canopy model. The fidelity of the simulations is primarily assessed by a spatiotemporally consistent comparison of a newly developed HRLDAS radiative temperature variable with remotely sensed estimates of land surface temperature from the Moderate Resolution Imaging Spectroradiometer. The most accurate urban simulations of radiative temperature are yielded from experiments that (1) explicitly specify the urban fraction in each pixel and (2) include irrigation. The former modification yields a gain in accuracy that is larger than for other changes, such as increasing the number of urban land use types. The latter modification (irrigation) substantially reduces simulated temperature biases and increases model precision compared to model configurations that lack irrigation, presumably because watering of lawns, parks, etc. is a common activity that should be represented in urban canopy models (although it is generally not). Ongoing and future efforts to improve urban canopy model simulations may achieve important gains through better representations of urban morphology, as well as processes that affect near‐surface energy partitioning within cities, such as irrigation.

Estimation of Land Surface Temperature for Different Regions in Iraq Using Remote Sensing Technique (ETM+)

Estimation of Land Surface Temperature for Different Regions in Iraq Using Remote Sensing Technique (ETM+)

Minimum configuration of thermal infrared bands for land surface temperature and emissivity estimation in the context of potential future missions

A number of applications rely on the availability of high resolution TIR data, both in terms of spatial and temporal resolution, as well as in terms of spectral configuration. A consensus exists among the thermal community regarding the lack of such a kind of data, since only one operational sensor is currently providing multispectral TIR data at high spatial resolution (the case of ASTER, at 90m spatial resolution and a 16-day revisit time). In this paper we propose a minimum multispectral configuration to allow the retrieval of both land surface temperature (LST) and emissivity (ε) from thermal infra-red (TIR) data. For this purpose, we propose a minimum configuration based on three TIR bands at around 8.9, 10.6 and 12.0μm, which allows the application of two-channel (or split-window) algorithms as well as the application of a temperature and emissivity separation (TES) method (or a combination of both) for LST retrieval. Results presented in this paper are based on studies performed for different candidate missions, but most of the presented results can be also valid in other contexts.

Validation of remotely sensed surface temperature over an oak woodland landscape — The problem of viewing and illumination geometries

Satellite retrieved values of Land Surface Temperature (LST) over structured heterogeneous pixels generally depend on viewing and illumination angles as well as on the characteristics of the land cover. Here we present a method to quantify such dependencies on land surface characteristics, sun illumination and satellite position. The method uses a geometric model to describe the surface elements viewed by an air-borne sensor and relies on parallel-ray geometry to calculate the projections of tree canopies and sunlit and shaded ground: these are considered as basic surface elements responsible for most of the spatial variability of LST. For a woodland landscape we demonstrate that modeling the fractions of these basic surface elements within the sensor field-of-view allows us to quantify the directional effects observed on satellite LST with sufficient accuracy.Geometric models are an effective tool to upscale in situ measurements for the validation of LST over discontinuous canopies (e.g. forests). Here we present the application of a model to observations of brightness temperature from the LSA-SAF validation site in Évora (Portugal), an area of oak woodland, over the one-year period from October 2011 to September 2012. The resulting composite temperature is compared against LSA SAF LST products from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard Meteosat as well as against MYD11A1/MOD11A1 (collection 5) products from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA and TERRA. Comparisons with modeled ground LST show that SEVIRI LST has a bias of 0.26°C and a RMSE of 1.34°C, whereas MODIS LST (MYD11A1 and MOD11A1, collection 5) has a bias of −1.54°C and a RMSE of 2.37°C. Both MODIS and SEVIRI LST are closer to in situ values obtained with the geometric model than to those obtained when disregarding the effects of viewing and illumination geometry. These results demonstrate the need to consider the directional character of LST products, especially for validation purposes over heterogeneous land covers. For the new MODIS LST product (MOD21), which is based on the Temperature-Emissivity Separation (TES) algorithm, comparisons with in-situ LST show an improved bias of −0.81°C and a RMSE of 1.48°C (daytime values only). The TES based product presents lower emissivity values than those used for retrieving MYD11A1/MOD11A1 LST, which may partially explain the improved match with in-situ LST.Discrepancies between LST retrievals obtained from different sensors, especially those on different orbits can also be partly explained by their viewing/illumination geometries. In this study the geometric model is used to correct LST deviations between simultaneous MODIS and SEVIRI LST estimations related to those effects. When the model is used to correct the variable MODIS viewing geometry there is a reduction in standard deviation of about 0.5°C.

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.

A new method for retrieving land surface temperature from HJ-1B data and the temporal influences

According to radiation transfer equation (RTE), it is an ill-conditioned problem to obtain land surface temperature (LST) accurately from the HJ-1B thermal infrared channel. Several algorithms have been proposed to resolve this problem. However, some accurate inputs (e.g. atmospheric parameters and land surface emissivity) always inaccessible to common users are indispensable to their applications. An innovative approach (named MTSC method) based on multi-temporal data was described in this paper, by means of which the LSTs are able to be estimated readily and directly from the radiometrically corrected thermal images, even without any other accurate information. To demonstrate its capability, four HJ-1B images (acquired on Nov 28, Dec 10, Dec 18 and Dec 22, 2011, respectively) mainly covering the Pearl River Delta region were selected for LSTs estimation. The LSTs retrieved by the MTSC method were then compared with the near time MODIS surface temperature products, and the samples were collected through a proper procedure. The preliminary assessments demonstrated that accurate results were obtained by using this new method. For example, for the retrieved results of Dec 22, the systematic errors for land cover and sea area were approximate to 1K and 0.5K, respectively. Further comparisons show that the temporal influence was negligible in this experiment, mainly because of the moderate impacts arisen from the atmospheric variation on the surface thermal property, which was acceptable for the MTSC method. However, modifications and improvements are still necessary to enable the full usage of this new approach.