Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 49513

Share This Special Issue

Special Issue Editors

Dr. Françoise Nerry

E-Mail Website
Guest Editor

ICube Lab, 300 bd Sébastien Brant, CS 10413, F-67412 Illkirch, CEDEX, France
Interests: retrieval methods for LST; urban heat island effect; definition of future sensors

Prof. Dr. José A. Sobrino

E-Mail Website
Guest Editor

Department of Earth Physics and Thermodynamics, University of Valencia, València, Spain
Interests: thermal infrared remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Thermal infrared remote-sensing is a unique way to obtain an accurate surface temperature that is one of the most important physical environmental variables monitored by earth-observing remote-sensing systems. Global changes in temperature endanger the environment; they must be monitored and consequently affect well-being. Surface temperature is a key parameter that must be monitored.

This Special Issue seeks contributions ranging from review papers to basic research. The focus will be on LST (Land Surface Temperature) rather than on SST (Sea Surface Temperature), where the physical processes involved are quite different. The scopes of this Special Issue are to present the latest studies on the retrieval of LST with a focus on the underlying physics and image processing techniques and on applications that use the LST to obtain a deeper understanding of land surface temperatures and dynamics, urban heat island effects, forest fires, volcanic eruption precursors, geothermal systems, and soil-moisture variability.

Dr. Françoise Nerry
Dr. José A. Sobrino
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) and Land Surface Emissivity (LSE) retrievals
up-scaling/down-scaling process
time series studies
application to land surface parameters

Published Papers (7 papers)

Download All Papers

Research

27 pages, 15915 KiB

Open AccessArticle

Land Surface Temperature Retrieval for Agricultural Areas Using a Novel UAV Platform Equipped with a Thermal Infrared and Multispectral Sensor

by Sascha Heinemann, Bastian Siegmann, Frank Thonfeld, Javier Muro, Christoph Jedmowski, Andreas Kemna, Thorsten Kraska, Onno Muller, Johannes Schultz, Thomas Udelhoven, Norman Wilke and Uwe Rascher

Remote Sens. 2020, 12(7), 1075; https://doi.org/10.3390/rs12071075 - 27 Mar 2020

Cited by 36 | Viewed by 7643

Abstract

Land surface temperature (LST) is a fundamental parameter within the system of the Earth’s surface and atmosphere, which can be used to describe the inherent physical processes of energy and water exchange. The need for LST has been increasingly recognised in agriculture, as it affects the growth phases of crops and crop yields. However, challenges in overcoming the large discrepancies between the retrieved LST and ground truth data still exist. Precise LST measurement depends mainly on accurately deriving the surface emissivity, which is very dynamic due to changing states of land cover and plant development. In this study, we present an LST retrieval algorithm for the combined use of multispectral optical and thermal UAV images, which has been optimised for operational applications in agriculture to map the heterogeneous and diverse agricultural crop systems of a research campus in Germany (April 2018). We constrain the emissivity using certain NDVI thresholds to distinguish different land surface types. The algorithm includes atmospheric corrections and environmental thermal emissions to minimise the uncertainties. In the analysis, we emphasise that the omission of crucial meteorological parameters and inaccurately determined emissivities can lead to a considerably underestimated LST; however, if the emissivity is underestimated, the LST can be overestimated. The retrieved LST is validated by reference temperatures from nearby ponds and weather stations. The validation of the thermal measurements indicates a mean absolute error of about 0.5 K. The novelty of the dual sensor system is that it simultaneously captures highly spatially resolved optical and thermal images, in order to construct the precise LST ortho-mosaics required to monitor plant diseases and drought stress and validate airborne and satellite data. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Graphical abstract

24 pages, 4847 KiB

Open AccessArticle

Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

by Jingjing Yang, Si-Bo Duan, Xiaoyu Zhang, Penghai Wu, Cheng Huang, Pei Leng and Maofang Gao

Remote Sens. 2020, 12(5), 791; https://doi.org/10.3390/rs12050791 - 01 Mar 2020

Cited by 37 | Viewed by 3793

Abstract

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Graphical abstract

32 pages, 2140 KiB

Open AccessArticle

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

by Aliihsan Sekertekin and Stefania Bonafoni

Remote Sens. 2020, 12(2), 294; https://doi.org/10.3390/rs12020294 - 16 Jan 2020

Cited by 234 | Viewed by 17524

Abstract

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Figure 1

18 pages, 4893 KiB

Open AccessArticle

Retrieving Land Surface Temperature from Satellite Imagery with a Novel Combined Strategy

by María Sánchez-Aparicio, Paula Andrés-Anaya, Susana Del Pozo and Susana Lagüela

Remote Sens. 2020, 12(2), 277; https://doi.org/10.3390/rs12020277 - 14 Jan 2020

Cited by 11 | Viewed by 6273

Abstract

Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Graphical abstract

18 pages, 3542 KiB

Open AccessArticle

Enterprise LST Algorithm Development and Its Evaluation with NOAA 20 Data

by Yuling Liu, Yunyue Yu, Peng Yu, Heshun Wang and Yuhan Rao

Remote Sens. 2019, 11(17), 2003; https://doi.org/10.3390/rs11172003 - 24 Aug 2019

Cited by 17 | Viewed by 3388

Abstract

Satellite land surface temperatures (LSTs) have been routinely produced for decades from a variety of polar-orbiting and geostationary satellites, which makes it possible to generate LST climate data globally. However, consistency of the satellite LSTs from different satellite missions is a concern for such purpose; an enterprise satellite LST algorithm is desired for the LST production through different satellite missions, or at the least, through series satellites of a satellite mission. The enterprise LST algorithm employs the split window technique and uses the emissivity explicitly in its formula. This research focuses on the enterprise LST algorithm design, development and its evaluations with the National Oceanic and Atmospheric Administration’s (NOAA) 20 (N20) Visible Infrared Imaging Radiometer Suite (VIIRS) data available since 5 January 2018. In this study, the enterprise LST algorithm was evaluated using simulation dataset consisting of over 2000 profiles from SeeBor collection and the results show a bias of 0.19 K and 0.34 K and standard deviation of 0.48 K and 0.69 K for nighttime and daytime, respectively. The in situ observations from seven NOAA Surface Radiation budget (SURFRAD) sites and two Baseline Surface Radiation Network (BSRN) sites were used for LST validation. The results indicate a bias of −0.3 K and a root mean square error (RMSE) of 2.06 K for SURFRAD stations and a bias of 0.2 K and a RMSE of ~2 K for BSRN sites. Further, the cross-satellite analysis presents a bias of 0.7 K and an RMSE of 1.9 K for comparisons with AQUA MODIS LST (MYD11_L2, Collection 6). The enterprise N20 VIIRS LST product reached the provisional maturity in February 2019 and is ready for users to use in their applications. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Graphical abstract

17 pages, 4864 KiB

Open AccessArticle

Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm

by Yitong Zheng, Huazhong Ren, Jinxin Guo, Darren Ghent, Kevin Tansey, Xingbang Hu, Jing Nie and Shanshan Chen

Remote Sens. 2019, 11(6), 650; https://doi.org/10.3390/rs11060650 - 17 Mar 2019

Cited by 35 | Viewed by 6215

Abstract

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Figure 1

27 pages, 8425 KiB

Open AccessArticle

An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data

by Shanshan Yu, Xiaozhou Xin, Qinhuo Liu, Hailong Zhang and Li Li

Remote Sens. 2019, 11(4), 425; https://doi.org/10.3390/rs11040425 - 19 Feb 2019

Cited by 8 | Viewed by 2892

Abstract

Surface downward longwave radiation (DLR) is a crucial component in Earth’s surface energy balance. Yu et al. (2013) developed a parameterization for retrieving clear-sky DLR at high spatial resolution by combined use of satellite thermal infrared (TIR) data and column integrated water vapor (IWV). We extended the Yu2013 parameterization to Moderate Resolution Imaging Spectroradiometer (MODIS) data based on atmospheric radiative simulation, and we modified the parameterization to decrease the systematic negative biases at large IWVs. The new parameterization improved DLR accuracy by 1.9 to 3.1 W/m² for IWV ≥3 cm compared to the Yu2013 algorithm. We also compared the new parameterization with four algorithms, including two based on Top-of-Atmosphere (TOA) radiance and two using near-surface meteorological parameters and water vapor. The algorithms were first evaluated using simulated data and then applied to MODIS data and validated using surface measurements at 14 stations around the globe. The results suggest that the new parameterization outperforms the TOA-radiance based algorithms in the regions where ground temperature is substantially different (enough that the difference between them is as large as 20 K) from skin air temperature. The parameterization also works well at high elevations where atmospheric parameter-based algorithms often have large biases. Furthermore, comparing different sources of atmospheric input data, we found that using the parameters interpolated from atmospheric reanalysis data improved the DLR estimation by 7.8 W/m² for the new parameterization and 19.1 W/m² for other algorithms at high-altitude sites, as compared to MODIS atmospheric products. Full article

(This article belongs to the Special Issue Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Thermal Infrared Remote Sensing and Its Application to Land Surface Parameters

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI