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Thermal Remote Sensing for Monitoring Terrestrial Environment

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 11942

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Prof. Dr. Jie Cheng

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Guest Editor

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: thermal infrared remote sensing; atmospheric radiation and surface energy balance
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qiao Wang

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Guest Editor

1. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2. Satellite Environment Application Center, Ministry of Environmental Protection, Beijing 100101, China
Interests: environment remote sensing monitoring; environmental information system; environmental assessment and protection

Special Issue Information

Dear Colleagues,

It is well known that everything above absolute zero (-273.1 °C) emits radiation in the thermal infrared range of the electromagnetic spectrum. Based on this fact, thermal infrared (TIR, 3–14 μm) remote sensing detects the transmitted surface-leaving radiation and the emission by the atmosphere. The surface–atmosphere coupling allows the estimate of a number of environmental variables, including land surface temperature (LST), land surface emissivity, air temperature, water vapor, trace gases, the component of surface radiation, and energy balances, etc. Among these variables, LST may be the most widely used one that has been recognized by the Global Climate Observing System (GCOS) as one of the essential climate variables. These variables are widely used to study urban climate and environment, environmental and ecological impacts of climate change, resource exploration, etc.

In addition to directly applying the estimated variables to monitor the related terrestrial environment and land surface processes, the TIR image is also employed to detect thermal anomalies, such as coal fire, forest fire, warm water discharge, etc. With the advent of commercial high-resolution (~ a few meters) TIR remote sensing, we can monitor the thermal traces caused by human activities.

In this context, reviewing the achieved progress on thermal remote sensing for monitoring the terrestrial environment and looking forward to future development hold great relevance. In this Special Issue, we will compile state-of-the-art methods for estimating TIR variables, monitoring the terrestrial environment, and detecting thermal anomalies. Potential topics include but are not limited to the following:

Thermal environment;
Urban heat island;
Heatwave;
Geological mapping;
Land cover classification;
Landscape thermal responses;
Thermal anomaly;
Coal fire;
Warm water discharge;
Active fire detection;
Anthropogenic heat emission;
Land surface temperature and emissivity;
Air temperature;
Water vapor;
Surface radiation and energy budget.

Prof. Dr. Jie Cheng
Prof. Dr. Qiao Wang
Guest Editors

Manuscript Submission Information

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Keywords

thermal environment
urban heat island
heatwave
geological mapping
land cover classification
landscape thermal responses
thermal anomaly
coal fire
warm water discharge
active fire detection
anthropogenic heat emission
land surface temperature and emissivity
air temperature
water vapor
surface radiation and energy budget

Published Papers (7 papers)

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Research

17 pages, 4536 KiB

Open AccessArticle

Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data

by Zijing Xie, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Xiaotong Zhang, Bo Jiang, Jia Xu, Ruiyang Yu, Lu Liu, Jing Ning, Jiahui Fan and Luna Zhang

Remote Sens. 2024, 16(1), 44; https://doi.org/10.3390/rs16010044 - 21 Dec 2023

Viewed by 670

Abstract

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R², 0.71), RMSE (21.9 W/m²) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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25 pages, 17585 KiB

Open AccessArticle

Seasonal Cooling Effect of Vegetation and Albedo Applied to the LCZ Classification of Three Chinese Megacities

by Yifan Luo, Jinxin Yang, Qian Shi, Yong Xu, Massimo Menenti and Man Sing Wong

Remote Sens. 2023, 15(23), 5478; https://doi.org/10.3390/rs15235478 - 23 Nov 2023

Cited by 1 | Viewed by 817

Abstract

The urban heat island effect poses a growing threat to human society, especially in densely populated and developed megacities. With the introduction of the Local Climate Zones (LCZ) framework, new perspectives and findings have been brought to urban heat island studies. This study investigated the cooling effect of vegetation and albedo on the surface urban heat island (SUHI) in the classification system of LCZ during different seasons, using three Chinese megacities as case study areas. Single-factor linear regression and Pearson’s correlation coefficient were applied to analyze the seasonal cooling effect of both albedo and the NDVI on the SUHI within different LCZs. The results show that (1) the variability of the SUHI is reflected in its dominance and intensity within certain LCZs in different cities and in the efficiency of cooling factors; (2) the cooling effect of vegetation is dominant in each season, and the cooling effect produced by albedo within specific seasons can be differentiated by LCZs. This study provides valuable information for the mitigation of the SUHI magnitude in specific regions and at specific times of the year. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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21 pages, 9145 KiB

Open AccessArticle

Fine-Scale Analysis of the Long-Term Urban Thermal Environment in Shanghai Using Google Earth Engine

by Mengen Wang, Huimin Lu, Binjie Chen, Weiwei Sun and Gang Yang

Remote Sens. 2023, 15(15), 3732; https://doi.org/10.3390/rs15153732 - 27 Jul 2023

Cited by 2 | Viewed by 1186

Abstract

Exploring the spatiotemporal patterns of urban thermal environments is crucial for mitigating the detrimental effects of urban heat islands (UHI). However, the long-term and fine-grained monitoring of UHI is limited by the temporal and spatial resolutions of various sensors. To address this limitation, this study employed the Google Earth Engine (GEE) platform and a multi-source remote sensing data fusion approach to generate a densely time-resolved Landsat-like Land Surface Temperature (LST) dataset for daytime observations spanning from 2001 to 2020 in Shanghai. A comprehensive analysis of the spatiotemporal patterns of UHI was conducted. The results indicate that over the past 20 years, the highest increase in average LST was observed during spring with a growth coefficient of 0.23, while the lowest increase occurred during autumn (growth coefficient of 0.12). The summer season exhibited the most pronounced UHI effect in the region (average proportion of Strong UHI and General UHI was 28.73%), while the winter season showed the weakest UHI effect (proportion of 22.77%). The Strong UHI areas gradually expanded outward over time, with a noticeable intensification of heat island intensity in the northwest and coastal regions, while other areas did not exhibit significant changes. Impervious surfaces contributed the most to LST, with a contribution of 0.96 °C, while water had the lowest contribution (−0.42 °C). The average correlation coefficients between LST and NDVI, NDWI, and NDBI over 20 years were −0.4236, −0.5128, and 0.5631, respectively. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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21 pages, 1894 KiB

Open AccessArticle

Quantifying the Scale Effect of the Relationship between Land Surface Temperature and Landscape Pattern

by Jiazheng Chen, Li Wang, Lin Ma and Xinyan Fan

Remote Sens. 2023, 15(8), 2131; https://doi.org/10.3390/rs15082131 - 18 Apr 2023

Cited by 1 | Viewed by 1109

Abstract

The spatial scaling of patterns and processes is a hot topic of research in landscape ecology, and different scales may yield completely inconsistent results. Therefore, to understand the impact of the scale effect on urban heat island effect, this study analyzes the correlation between surface temperature and landscape index at different spatial scales over Nanjing. The scale effect is calculated thorough curve fitting of the Pearson’s correlation coefficient between ten landscape indices and land surface temperature at different window sizes, and the optimal one is determined. We have found that landscape indices can be divided into exponential and Gaussian landscape indices whose correlation with land surface temperature at different windows conforms to binomial exponential or multi-Gaussian functions, respectively. The optimal window size is approximately 4000–5100 m for exponential landscape indices, 1000–2000 m for aggregation index (AI) and percentage of like adjacencies (PLADJ), 6330 m for contagion (CONTAG) and 4380 m for total edge contrast index (TECI). Moreover, CONTAG and TECI have a high correlation coefficient plateau where the Pearson correlation coefficient is high and changes by less than 0.03 as the window size changes by more than 3000 m, which makes it possible to decrease the window size in order to save the calculation time without an obvious decrease in the Pearson correlation coefficient. To achieve this, we proposed a suitable window selection function so that the window size becomes 4260 m and 2070 m, respectively. The window sizes obtained in this study are just suitable in Nanjing, but the window sizes in other cities can also be obtained by the method in this study. This study provides a reference for future research on the relationship between landscape pattern and land surface temperature and its driving mechanisms, as well as for the impact of urban land use planning on the heat island effect. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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20 pages, 5129 KiB

Open AccessArticle

Large-Scale Estimation of Hourly Surface Air Temperature Based on Observations from the FY-4A Geostationary Satellite

by Zhenwei Zhang, Yanzhi Liang, Guangxia Zhang and Chen Liang

Remote Sens. 2023, 15(7), 1753; https://doi.org/10.3390/rs15071753 - 24 Mar 2023

Viewed by 1189

Abstract

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth’s surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China’s new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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19 pages, 6887 KiB

Open AccessArticle

Distinguishing Dominant Drivers on LST Dynamics in the Qinling-Daba Mountains in Central China from 2000 to 2020

by Mengzhu Xi, Wen Zhang, Wanlong Li, Haodong Liu and Hui Zheng

Remote Sens. 2023, 15(4), 878; https://doi.org/10.3390/rs15040878 - 05 Feb 2023

Cited by 5 | Viewed by 3569

Abstract

Land surface temperature (LST) is an important driving factor in the land-atmosphere energy cycle. To examine the spatiotemporal patterns of LST changes and the internal mechanisms driven by multiple factors, we used a trend analysis method on TRIMS LST data from 2000 to 2020 in the Qingling-Daba Mountains. The optimal parameter geographic detector (OPGD) model was used to detect the influence of twelve factors, including elevation, precipitation, albedo, relative humidity (RH) and normalized difference vegetation index (NDVI), on the spatial distribution of LST, as well as to explore the dominant factors affecting LST differentiation in the study area. The results showed that: (1) From 2000 to 2020, the average annual LST of the Qinling-Daba Mountains was 18.17 °C. The warming trend was obvious (0.034 °C/a), and the warming effect at nighttime (0.066 °C/a) was stronger than that during daytime (0.0004 °C/a). The difference between day and night temperature (DIF) was decreasing. (2) The seasonal changes in LST and DIF in the Qinling-Daba Mountains were significant, and the spatial distribution of their average values in the summer was slightly larger and fluctuated more than in the other seasons. (3) Elevation was the main driving factor affecting the spatial distribution of LST, with the contribution scores of 62.9% in the daytime and 92.7% in the nighttime. The controlling effects of these factors were generally stronger in the nighttime than in the daytime. (4) Nighttime elevation had the strongest interaction with precipitation (contribution score of 95%), while daytime elevation had the strongest interaction with albedo (contribution rate of 83%). We revealed the temporal and spatial variation in LST in the Qinling-Daba Mountains since 2000 and explored the main driving factors involved, thereby improving our understanding of LST changes in the Qinling-Daba Mountains. This study can provide a scientific basis for distinguishing dominant drivers of LST dynamics in the Qinling-Daba Mountains. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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23 pages, 12024 KiB

Open AccessArticle

A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

by Shengyue Dong, Jie Cheng, Jiancheng Shi, Chunxiang Shi, Shuai Sun and Weihan Liu

Remote Sens. 2022, 14(20), 5170; https://doi.org/10.3390/rs14205170 - 16 Oct 2022

Cited by 8 | Viewed by 1911

Abstract

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI. Full article

(This article belongs to the Special Issue Thermal Remote Sensing for Monitoring Terrestrial Environment)

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