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Remote Sensing
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Remote Sensing Monitoring of Land Surface Temperature (LST) II
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Remote Sensing Monitoring of Land Surface Temperature (LST) II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 15425

Special Issue Editors

Dr. Juan Manuel Sánchez

E-Mail Website
Guest Editor
Applied Physics Department, Regional Development Institute, University of Castilla-La Mancha, Campus Universtiario s/n, 02071 Albacete, Spain
Interests: earth observation in the thermal domain; land surface temperature and emissivity; land surface fluxes; evapotranspiration; disaggregation of thermal images; calibration/validation; micro-meteorology
Special Issues, Collections and Topics in MDPI journals
Dr. Raquel Niclòs

E-Mail Website1 Website2
Guest Editor
Department of Earth Physics and Thermodynamics, Faculty of Physics, University of Valencia, C/Dr. Moliner, 50, 46100 Burjassot, Valencia, Spain
Interests: earth observation in the thermal domain; land and sea surface temperature and emissivity; thermal ground measurements; calibration/validation; angular variation of emissivities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Enric Valor

E-Mail Website
Guest Editor
Department of Earth Physics and Thermodynamics, Faculty of Physics, University of Valencia. C/Dr. Moliner, 50, 46100 Burjassot, Valencia, Spain
Interests: thermal infrared remote sensing; emissivity measurements and modelling; emissivity-temperature separation methods; atmospheric and emissivity correction; energy flux assessment; environmental applications of TIR remote sensing
Dr. Joan Miquel Galve

E-Mail Website
Guest Editor
Applied Physics Department, Regional Development Institute, University of Castilla-La Mancha, Campus Universtiario s/n, 02071 Albacete, Spain
Interests: thermal infrared remote sensing; emissivity measurements and modelling; atmospheric correction algorithms; calibration/validation; environmental applications of TIR remote sensing

Special Issue Information

Dear Colleagues,

The combination of the state of the art in the thermal infrared (TIR) domain with the recent advances in the capabilities provided by new satellites, UAV-based or aerial remote sensing, is encouraging the use of land surface temperature (LST) in a variety of research fields beyond traditional uses. 

LST plays a key role in soil–vegetation–atmosphere processes. The estimation of surface energy flux exchanges, actual evapotranspiration, or vegetation and soil properties, as well as monitoring volcano or forest fire activities are among the traditional applications of LST.

The latest advances in data fusion, downscaling, and disaggregation techniques provide a new dimension to LST applications in water resource and agronomic management thanks to improvements in both temporal and spatial resolution of thermal products. Nevertheless, further research in LST estimation algorithms as well as continuous calibration/validation is still required to improve the accuracy of ground LST data and satellite LST products.

This Special Issue aims at collecting recent developments, methodologies, calibration/validation, and applications of thermal remote sensing data and derived products, from UAV-based remote sensing, aerial remote sensing, and satellite remote sensing. Applications of LST to water resources assessment, evapotranspiration estimation, and irrigation management in arid and semiarid regions are particularly encouraged.

We also expect papers that present novel methods, based on single or multi-sensor time series of LST, using Landsat TIRS, EOS ASTER, EOS MODIS, Sentinel-3A/B SLSTR, S-NPP/ NOAA-20 VIIRS, etc. Review papers on these topics are also welcome.

In short, this Special Issue intends to collect recent efforts and contributions of the thermal remote sensing community dealing with LST estimation and applications.

Dr. Juan Manuel Sánchez
Dr. Raquel Niclòs
Prof. Dr. Enric Valor
Dr. Joan Miquel Galve
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Thermal infrared remote sensing
  • Emissivity and atmospheric correction
  • LST algorithms
  • Land surface energy fluxes/evapotranspiration
  • Downscaling/disaggregation techniques
  • Calibration/validation
  • Ground measurements of LST and land surface emissivities
  • Assimilation of LST in hydrological, climatological, and agronomic models

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Published Papers (5 papers)

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Research

27 pages, 16224 KiB  
Article
Generating Daily Land Surface Temperature Downscaling Data Based on Sentinel-3 Images
by Zhoujin Wang, Lichun Sui and Shiqi Zhang
Remote Sens. 2022, 14(22), 5752; https://doi.org/10.3390/rs14225752 - 14 Nov 2022
Cited by 2 | Viewed by 3563
Abstract
The land surface temperature (LST) images obtained by thermal infrared remote sensing sensors are of great significance for numerous fields of research. However, the low spatial resolution is a drawback of LST images. Downscaling is an effective way to solve this problem. The [...] Read more.
The land surface temperature (LST) images obtained by thermal infrared remote sensing sensors are of great significance for numerous fields of research. However, the low spatial resolution is a drawback of LST images. Downscaling is an effective way to solve this problem. The traditional downscaling methods, however, have various drawbacks, including their low temporal and spectral resolutions, difficult processes, numerous errors, and single downscaling factor. They also rely on two or more separate satellite platforms. These drawbacks can be partially compensated for by the Sentinel-3 satellite’s ability to acquire LST and multispectral images simultaneously. This paper proposes a downscaling model based on Sentinel-3 satellite and ASTER GDEM images—D-DisTrad—and compares the effects of the D-DisTrad model with DisTrad model and TsHARP model over four sites and four seasons. The mean bias (MB) range of the D-DisTrad model is −0.001–0.017 K, the mean absolute error (MAE) range is 0.103–0.891 K, and the root mean square error (RMSE) range is 0.220–1.235 K. The Pearson correlation coefficient (PCC) and R2 ranges are 0.938–0.994 and 0.889–0.989, respectively. The D-DisTrad model has the smallest error, the highest correlation, and the best visual effect, and can eliminate some “mosaic” effects in the original image. This paper shows that the D-DisTrad model can improve the spatial resolution and visual effects of LST images while maintaining high temporal resolution, and discusses the influence of the terrain and land cover on LST data. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)
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Graphical abstract

21 pages, 7826 KiB  
Article
An Analysis of Spatio-Temporal Relationship between Satellite-Based Land Surface Temperature and Station-Based Near-Surface Air Temperature over Brazil
by Jiang Liu, Daniel Fiifi Tawia Hagan, Thomas R. Holmes and Yi Liu
Remote Sens. 2022, 14(17), 4420; https://doi.org/10.3390/rs14174420 - 5 Sep 2022
Cited by 7 | Viewed by 2643
Abstract
A better understanding of the relationship between land surface temperature (Ts) and near-surface air temperature (Ta) is crucial for improving the simulation accuracy of climate models, developing retrieval schemes for soil and vegetation moisture, and estimating large-scale Ta from satellite-based Ts observations. In [...] Read more.
A better understanding of the relationship between land surface temperature (Ts) and near-surface air temperature (Ta) is crucial for improving the simulation accuracy of climate models, developing retrieval schemes for soil and vegetation moisture, and estimating large-scale Ta from satellite-based Ts observations. In this study, we investigated the relationship between multiple satellite-based Ts products, derived from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite, and Ta from 204 meteorological stations over Brazil during 2003–2016. Monthly satellite-based Ts products used in this study include: (1) AIRS Version 6 with 1° spatial resolution, (2) AIRS Version 7 with 1° spatial resolution, (3) MODIS Collection 6 with 0.05° spatial resolution, and (4) MODIS Collection 6 with 1° spatial resolution re-sampled from (3) for a direct comparison with AIRS products. We found that satellite-based Ts is lower than Ta over the forest area, but higher than Ta over the non-forest area. Nevertheless, the correlation coefficients (R) between monthly Ta and four Ts products during 2003–2016 are greater than 0.8 over most stations. The long-term trend analysis shows a general warming trend in temperatures, particularly over the central and eastern parts of Brazil. The satellite products could also observe the increasing Ts over the deforestation region. Furthermore, we examined the temperature anomalies during three drought events in the dry season of 2005, 2010, and 2015. All products show similar spatio-temporal patterns, with positive temperature anomalies expanding in areal coverage and magnitude from the 2005 to 2015 event. The above results show that satellite-based Ts is sensitive in reflecting environmental changes such as deforestation and extreme climatic events, and can be used as an alternative to Ta for climatological studies. Moreover, the observed differences between Ts and Ta may inform how thermal assumptions can be improved in satellite-based retrievals of soil and vegetation moisture or evapotranspiration. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)
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Graphical abstract

33 pages, 6146 KiB  
Article
Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016
by Carmen Recondo, Alejandro Corbea-Pérez, Juanjo Peón, Enrique Pendás, Miguel Ramos, Javier F. Calleja, Miguel Ángel de Pablo, Susana Fernández and José Antonio Corrales
Remote Sens. 2022, 14(13), 3206; https://doi.org/10.3390/rs14133206 - 4 Jul 2022
Cited by 8 | Viewed by 2678
Abstract
In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained [...] Read more.
In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)
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Graphical abstract

24 pages, 5588 KiB  
Article
A Comprehensive Clear-Sky Database for the Development of Land Surface Temperature Algorithms
by Sofia L. Ermida and Isabel F. Trigo
Remote Sens. 2022, 14(10), 2329; https://doi.org/10.3390/rs14102329 - 11 May 2022
Cited by 4 | Viewed by 2373
Abstract
Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of [...] Read more.
Land surface temperature is linked to a wide range of surface processes. Given the increased development of earth observation systems, a large effort has been put into advancing land surface temperature retrieval algorithms from remote sensors. Due to the very limited number of reliable in situ observations matching the spatial scales of satellite observations, algorithm development relies on synthetic databases, which then constitute a crucial part of algorithm development. Here we provide a database of atmospheric profiles and respective surface conditions that can be used to train and verify algorithms for land surface temperature retrieval, including machine learning techniques. The database was built from ERA5 data resampled through a dissimilarity criterion applied to the temperature and specific humidity profiles. This criterion aims to obtain regular distributions of these variables, ensuring a good representation of all atmospheric conditions. The corresponding vertical profiles of ozone and relevant surface and vertically integrated variables are also included in the dataset. Information on the surface conditions (i.e., temperature and emissivity) was complemented with data from a wide array of satellite products, enabling a more realistic surface representation. The dataset is freely available online at Zenodo. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)
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21 pages, 4058 KiB  
Article
Assessment of Land Surface Temperature Estimates from Landsat 8-TIRS in A High-Contrast Semiarid Agroecosystem. Algorithms Intercomparison
by Joan M. Galve, Juan M. Sánchez, Vicente García-Santos, José González-Piqueras, Alfonso Calera and Julio Villodre
Remote Sens. 2022, 14(8), 1843; https://doi.org/10.3390/rs14081843 - 12 Apr 2022
Cited by 7 | Viewed by 3289
Abstract
Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment [...] Read more.
Monitoring Land Surface Temperature (LST) from Landsat satellites has been shown to be effective in the estimation of crop water needs and modeling water use efficiency. Accurate LST estimation becomes critical in semiarid areas under water scarcity scenarios. This work shows the assessment of some well-known Single-Channel (SC) and Split-Window (SW) algorithms, adapted to Landsat 8/TIRS, under the conditions of a high-contrast semiarid agroecosystem. The recently released Landsat 8 Level-2 LST product (L8_ST) has also been included in the performance analysis. Ground measurements of surface temperature were taken for the evaluation during the summers of 2018–2019 in the cropland area of the Barrax test site, Spain. A dataset of 44 ground samples and 11 different L8/TIRS dates/scenes was gathered, covering a variety of crop fields and surface conditions. In addition, a simplified Single Band Atmospheric Correction (L-SBAC) was introduced based on a linearization of the atmospheric correction parameters with the water vapor content (w) and a redefinition of the emissivity threshold for the emissivity correction in the study site. The best results show differences within ±4.0 K for temperatures ranging 300–325 K. Statistics for the L-SBAC result in a RMSE of ±1.8 K with negligible systematic deviation. Similar results were obtained for the other SC and SW algorithms tested, whereas an overestimation of 1.0 K was observed for the L8_ST product because of inappropriate assignment of emissivity values. These results show the potential of the proposed linearization approach and set the uncertainty for LST estimates in high-contrast semiarid agroecosystems. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)
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