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Special Issue "Remote Sensing for Land Surface Temperature (LST) Estimation, Generation, and Analysis"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 October 2017

Special Issue Editors

Guest Editor
Dr. Zhaoliang Li

Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Website | E-Mail
Phone: +(86) 10 82 10 50 77
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; evapotranspiration; scaling problem; hyperspectral analysis; radiative transfer modelling
Guest Editor
Dr. Bo-Hui Tang

State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
E-Mail
Interests: retrieval and validation of land surface temperature/emissivity; land surface net radiation

Special Issue Information

Dear Colleagues,

As the direct driving force in the exchange of long-wave radiation and turbulent heat fluxes at the surface–atmosphere interface, land surface temperature (LST) is one of the most important parameters in the physical processes of surface energy and water balance at local to global scales. Knowledge of reliable estimates of LST is crucial because many applications such as evapotranspiration, climate change, hydrological cycle, vegetation monitoring, urban climate and environmental studies, etc., rely on it.

With the development of remote sensing from space, satellite data offer the only possibility for measuring LST over the entire globe with sufficiently high temporal resolution and with complete spatially averaged rather than ground point-based values. Consequently, many efforts have been carried out to estimate LST from satellite thermal infrared (TIR) data. Up to now, many methods have been developed for retrieving LST from polar-orbit and geostationary satellite TIR data, and several methods are used to generate global LST products with fine spatial resolution, such as MODIS and ASTER LST products. However, there is still no “best method” for retrieving LST from space. All of the methods either rely on statistical relationships or assumptions and constraints to solve the inherent, ill-posed retrieval problem. Currently, TIR remote sensing measurements have been greatly improved in terms of spectral, spatial, and temporal resolution. These improvements will soon produce a clearer picture of the land surface than ever before. This is a good opportunity and also a big challenge to solve the inherent, ill-posed problem of retrieving LST from satellite data.

On the other hand, TIR data lose efficiency when the land surface is fully or partly covered by clouds. The passive microwave can observe the Earth’s surface under all-weather conditions but with a coarser spatial resolution. Its measurements are proposed to retrieve LST over cloudy skies and an effective model of combining LSTs retrieved from TIR and passive microwave satellite data is attempted to generate an all-weather high spatial LST product. This Special Issue plans to demonstrate the state-of-the-art reflecting the retrieval of LST from space measurements and the growing interest in generation and analyses of this parameter.

Related References
  1. Becker, F.; Li, Z.L. Temperature-independent spectral indices in thermal infrared bands. Remote Sens. Environ. 1990a, 32, 17–33.
  2. Becker, F.; Li, Z.L. Towards a local split window method over land surfaces. J. Remote Sens. 1990b, 11, 369–393.
  3. Wan, Z.; Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Remote Sens. 1996, 34, 892–905.
  4. Wan, Z.; Li, L. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens. 1997, 35, 980–996.
  5. Gillespie, A.R.; Rokugawa, S.; Matsunaga, T.; Cothern, J.S.; Hook, S; Kahle, A.B. A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1113–1126.
  6. Sobrino, J.A.; Sòria, G.; Prata, A.J. Surface temperature retrieval from along track scanning radiometer 2 data: Algorithms and validation. Geophys. Res. 2004, 109, D11101.
  7. Wan, Z.; Li, L. Radiance-based validation of the V5 MODIS land-surface temperature product. Int. J. Remote Sens. 2008, 29, 5373–5395.
  8. Tang, H.; Bi, Y.; Li, Z.L.*; Xia, J. Generalized split-window algorithm for estimate of land surface temperature from Chinese geostationary FengYun meteorological satellite (Fy-2C) data. Sensors 2008, 8, 933–951. doi:10.3390/s8020933.
  9. Hulley, G.C.; Hook, S.J. Generating consistent land surface temperature and emissivity products between ASTER and MODIS data for Earth science research. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1304–1315.
  10. Li, Z.L.*; Tang, H.; Wu, H.; Ren, H.; Yan, G.J.; Wan, Z.; Trigo, I.F.; Sobrino, J. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. doi:10.1016/j.rse.2012.12.008.
  11. Li, Z.L.*; Wu, H.; Wang, N.; Shi, Q.; Sobrino, J.A.; Wan, Z.; Tang, H.; Yan, G.J. Land surface emissivity retrieval from satellite data. Int. J. Remote Sens. 2013, 34, 3084–3127. doi:10.1080/01431161.2012.716540.
  12. Tang, H.; Li, L. Quantitative remote sensing in thermal infrared: Theory and applications. Springer Remote Sens./Photogramm. 2014, doi:10.1007/978-3-642-42027-6.
  13. Tang, H.; Shao, K.; Li, Z.L.*; Wu, H.; Nerry, F.; Zhou, G. Estimation and validation of land surface temperature from Chinese second generation polar-orbiting FY-3A VIRR data. Remote Sens. 2015, 7, 3250–3273, doi:10.3390/rs70303250.
  14. Tang, H.; Shao, K.; Li, Z.L.*; Wu, H.; Tang, R. An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data. Int. J. Remote Sens. 2015, 36, 4864–4878. doi:10.1080/01431161.2015.1040132.
  15. Tang, B.H.*; Wang, J. A physics-based method to retrieve land surface temperature from MODIS daytime mid-infrared data. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4672–4679. doi:10.1109/TGRS.2016.2548500.
Dr. Zhao-Liang Li
Dr. Bo-Hui Tang
Guest Editors

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Keywords

  • Land surface temperature
  • Land surface emissivity
  • Thermal infrared data
  • Passive microwave data
  • LST product generation
  • LST validation
  • LST analysis
  • Atmospheric corrections
  • Temperature and emissivity separation
  • Scaling

Published Papers (5 papers)

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Research

Open AccessArticle Sensitivity of Landsat 8 Surface Temperature Estimates to Atmospheric Profile Data: A Study Using MODTRAN in Dryland Irrigated Systems
Remote Sens. 2017, 9(10), 988; doi:10.3390/rs9100988
Received: 15 June 2017 / Revised: 8 September 2017 / Accepted: 13 September 2017 / Published: 23 September 2017
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Abstract
The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance
[...] Read more.
The land surface temperature (LST) represents a critical element in efforts to characterize global surface energy and water fluxes, as well as being an essential climate variable in its own right. Current satellite platforms provide a range of spatial and temporal resolution radiance data from which LST can be determined. One of the most complete records of data comes via the Landsat series of satellites, which provide a continuous sequence that extends back to 1982. However, for much of this time, Landsat thermal data were provided through a single broadband thermal channel, making surface temperature retrieval challenging. To fully exploit the valuable time-series of thermal information that is available from these satellites requires efforts to better describe and understand the accuracy of temperature retrievals. Here, we contribute to these efforts by examining the impact of atmospheric correction on the estimation of LST, using atmospheric profiles derived from a range of in-situ, reanalysis, and satellite data. Radiance data from the thermal infrared (TIR) sensor onboard Landsat 8 was converted to LST by using the MODTRAN version 5.2 radiative transfer model, allowing the production of an LST time series based upon 28 Landsat overpasses. LST retrievals were then evaluated against in-situ thermal measurements collected over an arid zone farmland comprising both bare soil and vegetated surface types. Atmospheric profiles derived from AIRS, MOD07, ECMWF, NCEP, and balloon-based radiosonde data were used to drive the MODTRAN simulations. In addition to examining the direct impact of using various profile data on LST retrievals, randomly distributed errors were introduced into a range of forcing variables to better understand retrieval uncertainty. Results indicated differences in LST of up to 1 K for perturbations in emissivity and profile measurements, with the analysis also highlighting the challenges in modeling aerosol optical depth (AOD) over arid lands and its impact on the TIR bands. Days with high AOD content (AOD > 0.5) in the evaluation study seem to consistently underestimate in-situ LSTs by 1–2 K, suggesting that MODTRAN is unable to accurately simulate the aerosol conditions for the TIR bands. Comparisons between available in-situ and Landsat 8 derived LST illustrate a range of seasonal and land surface dynamics and provide an assessment of retrieval accuracy throughout the nine-month long study period. In terms of the choice of atmospheric profile, when excluding the in-situ data, results show a mean absolute range of between 1.2 K to 1.8 K over bare soil and 3.3 K to 3.8 K over alfalfa for the different meteorological forcing, with the AIRS profile providing the best reproduction over the studied arid land irrigation region. Full article
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Open AccessArticle Downscaling Land Surface Temperature in an Arid Area by Using Multiple Remote Sensing Indices with Random Forest Regression
Remote Sens. 2017, 9(8), 789; doi:10.3390/rs9080789
Received: 12 May 2017 / Revised: 23 July 2017 / Accepted: 29 July 2017 / Published: 31 July 2017
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Abstract
Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products.
[...] Read more.
Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in arid regions (especially in deserts) because of inaccurate remote sensing LST products. In this study, LST was downscaled by a random forest model between LST and multiple remote sensing indices (such as soil-adjusted vegetation index, normalized multi-band drought index, modified normalized difference water index, and normalized difference building index) in an arid region with an oasis–desert ecotone. The proposed downscaling approach, which involves the selection of remote sensing indices, was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The spatial resolution of MODIS LST was downscaled from 1 km to 500 m. Results of visual and quantitative analyses show that the distribution of downscaled LST matched that of the oasis and desert ecosystem. The lowest (approximately 22 °C) and highest temperatures (higher than 37 °C) were detected in the middle oasis and desert regions, respectively. Furthermore, the proposed approach achieves relatively satisfactory downscaling results, with coefficient of determination and root mean square error of 0.84 and 2.42 °C, respectively. The proposed approach shows higher accuracy and minimization of the MODIS LST in the desert region compared with other methods. Optimal availability occurs in the vegetated region during summer and autumn. In addition, the approach is also efficient and reliable for LST downscaling of Landsat images. Future tasks include reliable LST downscaling in challenging regions. Full article
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Open AccessArticle A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index
Remote Sens. 2017, 9(8), 780; doi:10.3390/rs9080780
Received: 12 June 2017 / Revised: 13 July 2017 / Accepted: 28 July 2017 / Published: 30 July 2017
Cited by 1 | PDF Full-text (2811 KB) | HTML Full-text | XML Full-text
Abstract
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping
[...] Read more.
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index. Full article
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Open AccessArticle Multiple Regression Analysis for Unmixing of Surface Temperature Data in an Urban Environment
Remote Sens. 2017, 9(7), 684; doi:10.3390/rs9070684
Received: 26 April 2017 / Revised: 16 June 2017 / Accepted: 30 June 2017 / Published: 4 July 2017
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Abstract
Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a
[...] Read more.
Global climate change and increasing urbanization worldwide intensify the need for a better understanding of human heat stress dynamics in urban systems. During heat waves, which are expected to increase in number and intensity, the development of urban cool islands could be a lifesaver for many elderly and vulnerable people. The use of remote sensing data offers the unique possibility to study these dynamics with spatially distributed large datasets during all seasons of the year and including day and night-time analysis. For the city of Basel 32 high-quality Landsat 8 (L8) scenes are available since 2013, enabling comprehensive statistical analysis. Therefore, land surface temperature (LST) is calculated using L8 thermal infrared (TIR) imagery (stray light corrected) applying improved emissivity and atmospheric corrections. The data are combined with a land use/land cover (LULC) map and evaluated using administrative residential units. The observed dependence of LST on LULC is analyzed using a thermal unmixing approach based on a multiple linear regression (MLR) model, which allows for quantifying the gradual influence of different LULC types on the LST precisely. Seasonal variations due to different solar irradiance and vegetation cover indicate a higher dependence of LST on the LULC during the warmer summer months and an increasing influence of the topography and albedo during the colder seasons. Furthermore, the MLR analysis allows creating predicted LST images, which can be used to fill data gaps like in SLC-off Landsat 7 ETM+ data. Full article
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Open AccessArticle Land Surface Temperature and Emissivity Retrieval from Field-Measured Hyperspectral Thermal Infrared Data Using Wavelet Transform
Remote Sens. 2017, 9(5), 454; doi:10.3390/rs9050454
Received: 9 March 2017 / Revised: 21 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
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Abstract
Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a
[...] Read more.
Currently, the main difficulty in separating the land surface temperature (LST) and land surface emissivity (LSE) from field-measured hyperspectral Thermal Infrared (TIR) data lies in solving the radiative transfer equation (RTE). Based on the theory of wavelet transform (WT), this paper proposes a method for accurately and effectively separating LSTs and LSEs from field-measured hyperspectral TIR data. We show that the number of unknowns in the RTE can be reduced by decomposing and reconstructing the LSE spectrum, thus making the RTE solvable. The final results show that the errors introduced by WT are negligible. In addition, the proposed method usually achieves a greater accuracy in a wet-warm atmosphere than that in a dry-cold atmosphere. For the results under instrument noise conditions (NE∆T = 0.2 K), the overall accuracy of the LST is approximately 0.1–0.3 K, while the Root Mean Square Error (RMSE) of the LSEs is less than 0.01. In contrast to the effects of instrument noise, our method is quite insensitive to noises from atmospheric downwelling radiance, and all the RMSEs of our method are approximately zero for both the LSTs and the LSEs. When we used field-measured data to better evaluate our method’s performance, the results showed that the RMSEs of the LSTs and LSEs were approximately 1.1 K and 0.01, respectively. The results from both simulated data and field-measured data demonstrate that our method is promising for decreasing the number of unknowns in the RTE. Furthermore, the proposed method overcomes some known limitations of current algorithms, such as singular values and the loss of continuity in the spectrum of the retrieved LSEs. Full article
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