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Remote Sensing for Land Surface Temperature and Related Applications

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 1506

Special Issue Editors

Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece
Interests: urban environment; air pollution; thermal environment in cities; climate change adaptation
Special Issues, Collections and Topics in MDPI journals
Department of Environmental Physics and Meteorology, National and Kapodistrian University of Athens, Athens, Greece
Interests: remote sensing; urban climate

Special Issue Information

Dear Colleagues,

Land surface temperature (LST) is a key input parameter for climatic, urban, energy, agricultural, hydrological, ecological, and biogeochemical research. The LST is estimated via remote sensing techniques that are based on either aerial or spaceborne observations in the thermal infrared. A critical factor for the use of the spaceborne observations is the appropriate, per research area, mix of temporal and spatial resolutions. New algorithms are developed to support the improvement (downscaling) of the spatial resolution, whereas new spaceborne platforms provide the potential for more regular data. At the same time, the growth of cloud computing platforms, such as Google Earth Engine, has also opened the possibility for local-, regional-, or continental-scale studies on LST over extended time periods. Contributions toward LST-related applications in the field of climatic, urban, energy,  hydrological, ecological, and biogeochemical research; on the development of algorithms for the improvement of the spatial resolution of data; on new satellite missions that can improve the temporal resolution of data; and on the use of cloud computing systems are welcome.  Special consideration will be given to contributions that demonstrate the role of LST for energy studies, drought detection, and climate change adaptation or mitigation at the urban scale.

Topics:

  • Novel application of LST products.
  • Studies exploring long-term series of LST as well as the link between the LST and other variables, through cloud computing platforms.
  • LST as a predictor of atmospheric or climatic events.
  • Using LST to improve land products, e.g., fire detection, land-cover classification, soil moisture retrieval, etc.
  • Downscaling techniques to improve the spatial resolution of LST products.
  • New satellite missions in support of the extraction of LST.
  • LST and climate change.
  • LST in relation to urban form, urban fabric, and urban functions.
  • Inter-relationships between the near surface air temperature and LST.
  • LST acquired by Unmanned Aerial Vehicles (UAVs).

Prof. Dr. Constantinos Cartalis
Dr. Ilias Agathangelidis
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

  • land surface temperature (LST)
  • land products
  • fire detection
  • land-cover classification
  • soil moisture retrieval
  • spatial resolution
  • LST and climate change
  • urban form, urban fabric, and urban functions
  • near surface air temperature
  • thermal infrared remote sensing
  • land surface energy fluxes and evapotranspiration
  • downscaling and disaggregation techniques

Published Papers (2 papers)

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Research

25 pages, 4684 KiB  
Article
Improvements in the Estimation of Air Temperature with Empirical Models on Livingston and Deception Islands in Maritime Antarctica (2000–2016) Using C6 MODIS LST
by Alejandro Corbea-Pérez, Carmen Recondo and Javier F. Calleja
Remote Sens. 2024, 16(6), 1084; https://doi.org/10.3390/rs16061084 - 20 Mar 2024
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Abstract
Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth’s surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (Ta) have different physical [...] Read more.
Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth’s surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (Ta) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate Ta. For this reason, in this work, we estimated Ta from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean Ta was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R2 values (R2CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSECV = 2.2 °C, MAECV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study Ta in the area, as these filters contribute to the reduction of uncertainties in the modeling of Ta from satellites. Full article
(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)
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30 pages, 21321 KiB  
Article
The Improved U-STFM: A Deep Learning-Based Nonlinear Spatial-Temporal Fusion Model for Land Surface Temperature Downscaling
by Shanxin Guo, Min Li, Yuanqing Li, Jinsong Chen, Hankui K. Zhang, Luyi Sun, Jingwen Wang, Ruxin Wang and Yan Yang
Remote Sens. 2024, 16(2), 322; https://doi.org/10.3390/rs16020322 - 12 Jan 2024
Viewed by 660
Abstract
The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and [...] Read more.
The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation models, enhancing our understanding of physical and biochemical processes in nature. However, the limitations in swath width and orbit altitude prevent a single sensor from providing LST data with both high spatial and high temporal resolution. To tackle this challenge, the unmixing-based spatiotemporal fusion model (STFM) offers a promising solution by integrating data from multiple sensors. In these models, the surface reflectance is decomposed from coarse pixels to fine pixels using the linear unmixing function combined with fractional coverage. However, when downsizing LST through STFM, the linear mixing hypothesis fails to adequately represent the nonlinear energy mixing process of LST. Additionally, the original weighting function is sensitive to noise, leading to unreliable predictions of the final LST due to small errors in the unmixing function. To overcome these issues, we selected the U-STFM as the baseline model and introduced an updated version called the nonlinear U-STFM. This new model incorporates two deep learning components: the Dynamic Net (DyNet) and the Chang Ratio Net (RatioNet). The utilization of these components enables easy training with a small dataset while maintaining a high generalization capability over time. The MODIS Terra daytime LST products were employed to downscale from 1000 m to 30 m, in comparison with the Landsat7 LST products. Our results demonstrate that the new model surpasses STARFM, ESTARFM, and the original U-STFM in terms of prediction accuracy and anti-noise capability. To further enhance other STFMs, these two deep-learning components can replace the linear unmixing and weighting functions with minor modifications. As a deep learning-based model, it can be pretrained and deployed for online prediction. Full article
(This article belongs to the Special Issue Remote Sensing for Land Surface Temperature and Related Applications)
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