Special Issue "Remote Sensing Monitoring of Land Surface Temperature (LST)"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Juan Manuel Sánchez Tomás
E-Mail Website1 Website2
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
Dr. César Coll
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: Earth Observation in the thermal domain; land surface temperature and emissivity; thermal ground measurements; directional effects in land surface temperature; calibration/validation
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 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 satellite, UAV-based, or aerial remote sensing is encouraging the use of Land Surface Temperature (LST) in a variety of research fields beyond the traditional uses.

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

Latest advances in data fusion, downscaling, and disaggregation techniques provide a new dimension to LST applications in water resource and agronomic management thanks to the improvement in both the temporal and spatial resolution of the thermal products. Nevertheless, further research into 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 to collect 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. Papers on the application of LST to water resources assessment, evapotranspiration estimation, or irrigation management in arid and semiarid regions are particularly encouraged.

We also encourage you to submit 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
Dr. César Coll
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 papers will be 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 1800 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 of LST
  • Ground measurements of LST and Land Surface Emissivities
  • Assimilation of LST in hydrological, climatological, and agronomic models.

Published Papers (3 papers)

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Research

Open AccessArticle
Reconstructing One Kilometre Resolution Daily Clear-Sky LST for China’s Landmass Using the BME Method
Remote Sens. 2019, 11(22), 2610; https://doi.org/10.3390/rs11222610 - 07 Nov 2019
Abstract
The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer [...] Read more.
The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to achieve this. However, it has many missing values caused by clouds and other factors. The current gap-filling methods need to be improved when applied to large areas. In this study, we used the Bayesian maximum entropy (BME) method, which considers spatial and temporal correlation, and takes multiple regression results of the Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), longitude and latitude as soft data to reconstruct space-complete daily clear-sky LSTs with a 1 km resolution for China’s landmass in 2015. The average Root Mean Square Error (RMSE) of this method was 1.6 K for the daytime and 1.2 K for the nighttime when we simultaneously covered more than 10,000 verification points, including blocks that were continuous in space, and the average RMSE of a single discrete verification point for 365 days was 0.4 K for the daytime and 0.3 K for the nighttime when we covered four discrete points. Urban and snow land cover types have a higher accuracy than forests and grasslands, and the accuracy is higher in winter than in summer. The high accuracy and great ability of this method to capture extreme values in urban areas can help improve urban heat island research. This method can also be extended to other study areas, other time periods, and the estimation of other geographical attribute values. How to effectively convert clear-sky LST into real LST requires further research. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
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Open AccessArticle
Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations
Remote Sens. 2019, 11(14), 1722; https://doi.org/10.3390/rs11141722 - 20 Jul 2019
Abstract
Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote [...] Read more.
Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
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Open AccessArticle
Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites
Remote Sens. 2019, 11(12), 1399; https://doi.org/10.3390/rs11121399 - 12 Jun 2019
Abstract
Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective [...] Read more.
Our objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST))
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