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Remotely Sensed Land Surface Temperature
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Remotely Sensed Land Surface Temperature

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 9695

Special Issue Editors

Dr. Carlos C. DaCamara

E-Mail Website
Guest Editor
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
Interests: remote sensing; land surface temperature; land surface emissivity; wildfire detection; burned area monitoring; geometric effects on LST
Dr. Sofia L. Ermida

E-Mail Website
Guest Editor
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
Interests: remote sensing; land surface temperature; land surface emissivity; microwave LST; geometric effects on LST; LST validation

Special Issue Information

Dear Colleagues,

Land surface temperature (LST) is a key parameter when characterizing the physical processes of surface energy and water balance at the Earth’s surface, recently being recognised as an Essential Climate Variable. It is also a relevant diagnostic parameter of land surface conditions. As such, LST is widely used in a variety of fields that include evapotranspiration, climate change, hydrological cycle, vegetation monitoring, urban climate and environmental studies.

This Special Issue is dedicated to all aspects related to the problem of retrieving LST, including choice of channels (thermal infrared, microwave), multisensor approaches, retrieving procedures, emissivity estimation, data validation and sensor intercomparison, among others. Contributions on applications of  LST to the environment and climate are also welcome, such as studies on urban heat islands, desertification, and extreme events (heat waves, cold spels, droughts, etc.).

Dr. Carlos C. DaCamara
Dr. Sofia L. Ermida
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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
  • Land surface emissivity
  • Retrieval algorithms
  • Validation
  • Intercomparison
  • Application studies
  • Climate data records

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Published Papers (2 papers)

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Research

20 pages, 10781 KiB  
Article
Urban Design Factors Influencing Surface Urban Heat Island in the High-Density City of Guangzhou Based on the Local Climate Zone
by Yurong Shi, Yirui Xiang and Yufeng Zhang
Sensors 2019, 19(16), 3459; https://doi.org/10.3390/s19163459 - 8 Aug 2019
Cited by 60 | Viewed by 5020
Abstract
Surface urban heat island (SUHI) depicts the deteriorating thermal environment in high-density cities and local climate zone (LCZ) classification provides a universal protocol for SUHI identification. In this study, taking the central urbanized area of Guangzhou in the humid subtropical region of China [...] Read more.
Surface urban heat island (SUHI) depicts the deteriorating thermal environment in high-density cities and local climate zone (LCZ) classification provides a universal protocol for SUHI identification. In this study, taking the central urbanized area of Guangzhou in the humid subtropical region of China as the study area, the maps or images of LCZ, land surface temperature, SUHI, and urban design factors were achieved using Landsat satellite data, GIS database, and a series of retrieval and classification algorithms, and the urban design factors influencing SUHI were investigated based on 625 samples of LCZs. The results show that on the 18 September 2016 at the local time of 10:51 a.m., the land surface temperature (LST) varied greatly from 26 °C to 40 °C and the SUHI changed with a wide range of −6 °C to 8 °C in the LCZs of the study area. Seven and five urban design factors influencing the summer daytime SUHI were identified for the two dominant LCZs of LCZs 1–5 (LCZ 1 to LCZ 5) and the mixed LCZ (containing at least three types of LCZs), respectively, in which vegetation cover ratio, floor area ratio, ground emissivity, and complete surface area ratio showed negative correlations and building density showed positive correlations. The summer daytime SUHI prediction models were obtained by using the step-wise multiple linear regression, with the performance of R2 of 0.774, RMSE of 0.95 °C, and the d value of 0.91 for the model of LCZs 1–5, and the values of 0.819, 0.81 °C, and 0.94 for the model of the mixed LCZ, indicating that the models can effectively predict the changes of SUHI with LCZs. This study presents a methodology to efficiently achieve a large sample of SUHI and urban design factors of LCZs, and provides information beneficial to the urban designs and regenerations in high-density cities. Full article
(This article belongs to the Special Issue Remotely Sensed Land Surface Temperature)
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17 pages, 11090 KiB  
Article
Spatially Consistent High-Resolution Land Surface Temperature Mosaics for Thermophysical Mapping of the Mojave Desert
by Scott A. Nowicki, Richard D. Inman, Todd C. Esque, Kenneth E. Nussear and Christopher S. Edwards
Sensors 2019, 19(12), 2669; https://doi.org/10.3390/s19122669 - 13 Jun 2019
Cited by 8 | Viewed by 4224
Abstract
Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat [...] Read more.
Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution. Full article
(This article belongs to the Special Issue Remotely Sensed Land Surface Temperature)
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