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Temperature of the Earth
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Temperature of the Earth

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Spatial Data Science and Digital Earth".

Deadline for manuscript submissions: closed (30 November 2016) | Viewed by 43758

Special Issue Editor

Dr. Juan-Carlos Jiménez-Muñoz

E-Mail Website
Guest Editor
Global Change Unit, Image Processing Laboratory, University of Valencia, Catedrático José Beltrán 2, 46980 Paterna, Valencia, Spain
Interests: thermal remote sensing; land surface temperature/emissivity retrieval; temperature trends over tropical forests
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of anthropogenic global warming, there has been increased interest in examining Earth’s temperature trends. Temperature is an essential climate variable in which changes can be considered as a vital sign of the Planet. Compilation and processing of Earth’s temperature records is a key topic in order to assess the warming rate. Although such an analysis is commonly performed through the air temperature, sea and land surface temperatures (including lakes an ice/snow temperatures) are also key indicators of changes occurring in our planet. Surface temperatures also play an important role at local and regional level in order to characterize, analyse, and model the land surface processes. In the last decades, surface temperature datasets from remote sensing data have become increasingly available, which combined with compilation of temperature data from stations around the world and temperature products derived from reanalysis information provide complete information about the atmospheric and surface processes.

We would like to invite you to submit articles regarding your recent compilation of temperature datasets and/or processing techniques used to generate such datasets. Potential topics include:

  • New Sea/Land Surface Temperature products derived from remote sensing data, including thermal infrared and microwave data.
  • New surface/air temperature records derived from reanalysis data.
  • Processing of existing temperature datasets to generate thermal anomalies or thermal indices for different applications.
  • In situ measurements of surface/air temperature for calibration/validation purposes.

Dr. Juan C. Jiménez-Muñoz
Guest Editor

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. Data is an international peer-reviewed open access monthly 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 1600 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
  • Sea Surface Temperature
  • Ice Surface Temperature
  • Lake Surface Temperature
  • Air Temperature
  • Thermal Anomalies
  • Global Warming
  • Remote Sensing
  • Reanalysis
  • Thermal infrared
  • Microwave
  • Calibration/Validation
  • Climate data records

Published Papers (7 papers)

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3403 KiB  
Article
The Land Surface Temperature Synergistic Processor in BEAM: A Prototype towards Sentinel-3
by Ana Belen Ruescas, Olaf Danne, Norman Fomferra and Carsten Brockmann
Data 2016, 1(3), 18; https://doi.org/10.3390/data1030018 - 21 Oct 2016
Cited by 11 | Viewed by 5706
Abstract
Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European [...] Read more.
Land Surface Temperature (LST) is one of the key parameters in the physics of land-surface processes on regional and global scales, combining the results of all surface-atmosphere interactions and energy fluxes between the surface and the atmosphere. With the advent of the European Space Agency (ESA) Sentinel 3 (S3) satellite, accurate LST retrieval methodologies are being developed by exploiting the synergy between the Ocean and Land Colour Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR). In this paper we explain the implementation in the Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM) and the use of one LST algorithm developed in the framework of the Synergistic Use of The Sentinel Missions For Estimating And Monitoring Land Surface Temperature (SEN4LST) project. The LST algorithm is based on the split-window technique with an explicit dependence on the surface emissivity. Performance of the methodology is assessed by using MEdium Resolution Imaging Spectrometer/Advanced Along-Track Scanning Radiometer (MERIS/AATSR) pairs, instruments with similar characteristics than OLCI/ SLSTR, respectively. The LST retrievals were properly validated against in situ data measured along one year (2011) in three test sites, and inter-compared to the standard AATSR level-2 product with satisfactory results. The algorithm is implemented in BEAM using as a basis the MERIS/AATSR Synergy Toolbox. Specific details about the processor validation can be found in the validation report of the SEN4LST project. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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1424 KiB  
Article
Permanent Stations for Calibration/Validation of Thermal Sensors over Spain
by Jose Antonio Sobrino and Dražen Skoković
Data 2016, 1(2), 10; https://doi.org/10.3390/data1020010 - 28 Jul 2016
Cited by 17 | Viewed by 5043
Abstract
The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous [...] Read more.
The Global Change Unit (GCU) at the University of Valencia has been involved in several calibration/validation (cal/val) activities carried out in dedicated field campaigns organized by ESA and other organisms. However, permanent stations are required in order to ensure a long-term and continuous calibration of on-orbit sensors. In the framework of the CEOS-Spain project, the GCU has managed the set-up and launch of experimental sites in Spain for the calibration of thermal infrared sensors and the validation of Land Surface Temperature (LST) products derived from those data. Currently, three sites have been identified and equipped: the agricultural area of Barrax (39.05 N, 2.1 W), the marshland area in the National Park of Doñana (36.99 N, 6.44 W), and the semi-arid area of the National Park of Cabo de Gata (36.83 N, 2.25 W). This work presents the performance of the permanent stations installed over the different test areas, as well as the cal/val results obtained for a number of Earth Observation sensors: SEVIRI, MODIS, and TIRS/Landsat-8. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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2166 KiB  
Data Descriptor
A New Integrated High-Latitude Thermal Laboratory for the Characterization of Land Surface Processes in Alaska’s Arctic and Boreal Regions
by Jordi Cristóbal, Patrick Graham, Marcel Buchhorn and Anupma Prakash
Data 2016, 1(2), 13; https://doi.org/10.3390/data1020013 - 21 Sep 2016
Cited by 4 | Viewed by 4986
Abstract
Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable [...] Read more.
Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming. However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions. This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change. With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way. Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research. UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery. The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes. In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST). The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions. This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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937 KiB  
Data Descriptor
A Spectral Emissivity Library of Spoil Substrates
by Marek Pivovarník, Miroslav Pikl, Jan Frouz, František Zemek, Veronika Kopačková, Gila Notesco and Eyal Ben Dor
Data 2016, 1(2), 12; https://doi.org/10.3390/data1020012 - 10 Sep 2016
Cited by 2 | Viewed by 5216
Abstract
Post-mining sites have a significant impact on surrounding ecosystems. Afforestation can restore these ecosystems, but its success and speed depends on the properties of the excavated spoil substrates. Thermal infrared remote sensing brings advantages to the mapping and classification of spoil substrates, resulting [...] Read more.
Post-mining sites have a significant impact on surrounding ecosystems. Afforestation can restore these ecosystems, but its success and speed depends on the properties of the excavated spoil substrates. Thermal infrared remote sensing brings advantages to the mapping and classification of spoil substrates, resulting in the determination of its properties. A library of spoil substrates containing spectral emissivity and chemical properties can facilitate remote sensing activities. This study presents spectral library of spoil substrates’ emissivities extracted from brown coal mining sites in the Czech Republic. Extracted samples were homogenized by drying and sieving. Spectral emissivity of each sample was determined by spectral smoothing algorithm applied to data measured by a Fourier transform infrared (FTIR) spectrometer. A set of chemical parameters (pH, conductivity, Na, K, Al, Fe, loss on ignition and polyphenol content) and toxicity were determined for each sample as well. The spectral library presented in this paper also offers valuable information in the form of geographical coordinates for the locations where samples were obtained. Presented data are unique in nature and can serve many remote sensing activities in longwave infrared electromagnetic spectrum. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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13781 KiB  
Data Descriptor
MODIS-Based Monthly LST Products over Amazonia under Different Cloud Mask Schemes
by José Gomis-Cebolla, Juan C. Jiménez-Muñoz and José A. Sobrino
Data 2016, 1(2), 2; https://doi.org/10.3390/data1020002 - 04 Jul 2016
Cited by 6 | Viewed by 4945
Abstract
One of the major problems in the monitoring of tropical rainforests using satellite imagery is their persistent cloud coverage. The use of daily observations derived from high temporal resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), could potentially help to mitigate this [...] Read more.
One of the major problems in the monitoring of tropical rainforests using satellite imagery is their persistent cloud coverage. The use of daily observations derived from high temporal resolution sensors, such as Moderate Resolution Imaging Spectroradiometer (MODIS), could potentially help to mitigate this issue, increasing the number of clear-sky observations. However, the cloud contamination effect should be removed from these results in order to provide a reliable description of these forests. In this study the available MODIS Land Surface Temperature (LST) products have been reprocessed over the Amazon Basin (10 N–20 S, 80 W–45 W) by introducing different cloud masking schemes. The monthly LST datasets can be used for the monitoring of thermal anomalies over the Amazon forests and the analysis of spatial patterns of warming events at higher spatial resolutions than other climatic datasets. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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2223 KiB  
Data Descriptor
The LAB-Net Soil Moisture Network: Application to Thermal Remote Sensing and Surface Energy Balance
by Cristian Mattar, Andrés Santamaría-Artigas, Claudio Durán-Alarcón, Luis Olivera-Guerra, Rodrigo Fuster and Dager Borvarán
Data 2016, 1(1), 6; https://doi.org/10.3390/data1010006 - 07 Jun 2016
Cited by 16 | Viewed by 8416
Abstract
A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. [...] Read more.
A set of Essential Climate Variables (ECV) have been defined to be monitored by current and new remote sensing missions. The ECV retrieved at global scale need to be validated in order to provide reliable products to be used in remote sensing applications. For this, test sites are required to use in calibration and validation of the remote sensing approaches in order to improve the ECV retrievals at global scale. The southern hemisphere presents scarce test sites for calibration and validation field campaigns that focus on soil moisture and land surface temperature retrievals. In Chile, remote sensing applications related to soil moisture estimates have increased during the last decades because of the drought and water use conflicts that generate a strong interest on improved water demand estimates. This work describes the Laboratory for Analysis of the Biosphere (LAB)—NETwork, called herein after ‘LAB-net’, which was designed to be the first network in Chile for remote sensing applications. The test sites were placed in four sites with different cover types: vineyards and olive orchards located in the semi-arid region of Atacama, an irrigated raspberry crop in the Mediterranean climate zone of Chimbarongo, and a rainfed pasture in the south of Chile. Over each site, well implemented meteorological and radiative flux instrumentation was installed and continuously recorded the following parameters: soil moisture and temperature at two ground levels (10 and 20 cm), air temperature and relative humidity, net radiation, global radiation, radiometric temperature (8–14 µm), rainfall and soil heat flux. The LAB-net data base post-processing procedure is also described here. As an application, surface remote sensing products such as soil moisture data derived from the Soil Moisture Ocean Salinity (SMOS) and Land Surface Temperature (LST) extracted from the MODIS-MOD11A1 and GOES LST from Copernicus products were compared to in situ data in Oromo LAB-net site. Moreover, land surface energy flux estimation is also shown as an application of LAB-net data base. These applications revealed a good performance between in situ and remote sensing data. LAB-net data base also contributes to provide suitable information for land surface energy budget and therefore water resources management at cultivars scale. The data based generated by LAB-net is freely available for any research or scientific purpose related to current and future remote sensing applications. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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5363 KiB  
Data Descriptor
A MODIS/ASTER Airborne Simulator (MASTER) Imagery for Urban Heat Island Research
by Qunshan Zhao and Elizabeth A. Wentz
Data 2016, 1(1), 7; https://doi.org/10.3390/data1010007 - 06 Jun 2016
Cited by 16 | Viewed by 8701
Abstract
Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging [...] Read more.
Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect. Full article
(This article belongs to the Special Issue Temperature of the Earth)
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