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Land Surface Temperature Estimation Using Remote Sensing

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

Deadline for manuscript submissions: closed (1 June 2022) | Viewed by 33340

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Dr. Anand Inamdar

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North Carolina Institute for Climate Studies, North Carolina State University, Raleigh, NC 27695, USA
Interests: earth radiation budgets; remote sensing of land surface parameters; surface energy balance from satellites
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Land surface temperature (LST) is a basic determinant of the terrestrial thermal behavior which controls the effective radiating temperature of the Earth’s surface. It is an important aspect of climate and biology with a major influence on hydrology, meteorology, and climatology. It has been identified as one of the most important Earth system data records (EDR) by NASA, a legacy national weather service (NWS) requirement, and also an essential climate variable (ECV) required by the global climate observing system (GCOS) of the World Meteorological Organization (WMO). Over the years, applications of LST have expanded beyond its traditional use as a climate change indicator. It is an important indicator of the redistribution of energy at the land–atmosphere interface, plant water stress, monitoring of drought, land cover/land use change, urban heat island effects, heat stress, epidemiological studies, and so on. Additionally, the retrieval methods have expanded beyond the conventional thermal infrared and microwave with the launch of new generation of hyperspectral sensors such as Infrared atmospheric sounding interferometer (IASI) and cross-track infrared sounder (CrIS).

This issue solicits papers dealing with state-of-the-art techniques in remote sensing of LST, especially filling up gaps in LST measurements due to cloud contamination and extension of LST retrievals under all-weather conditions and applications to drought monitoring and crop health, novel climate change indicators derived from LST, etc.

Dr. Anand Inamdar
Guest Editor

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Published Papers (9 papers)

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Research

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21 pages, 10978 KiB

Open AccessArticle

Cross-Comparison between Sun-Synchronized and Geostationary Satellite-Derived Land Surface Temperature: A Case Study in Hong Kong

by Ibrahim Ademola Adeniran, Rui Zhu, Jinxin Yang, Xiaolin Zhu and Man Sing Wong

Remote Sens. 2022, 14(18), 4444; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184444 - 06 Sep 2022

Cited by 5 | Viewed by 1781

Abstract

Harmonization of satellite imagery provides a good opportunity for studying land surface temperature (LST) as well as the urban heat island effect. However, it is challenging to use the harmonized data for the study of LST due to the systematic bias between the LSTs from different satellites, which is highly influenced by sensor differences and the compatibility of LST retrieval algorithms. To fill this research gap, this study proposes the comparison of different LST images retrieved from various satellites that focus on Hong Kong, China, by applying diverse retrieval algorithms. LST images generated from Landsat-8 using the mono-window algorithm (MWA_L8) and split-window algorithm (SWA_L8) would be compared with the LST estimations from Sentinel-3 SLSTR and Himawari-8 using the split-window algorithm (SWA_S3 and SWA_H8). Intercomparison will also be performed through segregated groups of different land use classes both during the daytime and nighttime. Results indicate that there is a significant difference among the quantitative distribution of the LST data generated from these three satellites, with average bias of up to −1.80 K when SWA_H8 was compared with MWA_L8, despite having similar spatial patterns of the LST images. The findings also suggest that retrieval algorithms and the dominant land use class in the study area would affect the accuracy of image-fusion techniques. The results from the day and nighttime comparisons revealed that there is a significant difference between day and nighttime LSTs, with nighttime LSTs from different satellite sensors more consistent than the daytime LSTs. This emphasizes the need to incorporate as much night-time LST data as available when predicting or optimizing fine-scale LSTs in the nighttime, so as to minimize the bias. The framework designed by this study will serve as a guideline towards efficient spatial optimization and harmonized use of LSTs when utilizing different satellite images associated with an array of land covers and at different times of the day. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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20 pages, 9624 KiB

Open AccessArticle

All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning

by Dongjin Cho, Dukwon Bae, Cheolhee Yoo, Jungho Im, Yeonsu Lee and Siwoo Lee

Remote Sens. 2022, 14(8), 1815; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081815 - 09 Apr 2022

Cited by 17 | Viewed by 3471

Abstract

A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model’s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R²) of 0.89–0.91 with a root mean square error (RMSE) of 1.11–1.39 °C during the daytime, and an R² of 0.96–0.97 with an RMSE of 0.59–0.60 °C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R² of 0.55–0.63 with an RMSE of 2.41–3.00 °C during the daytime, and an R² of 0.70–0.74 with an RMSE of 1.31–1.36 °C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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20 pages, 6154 KiB

Open AccessArticle

The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)

by Muhammad Amir Siddique, Yu Wang, Ninghan Xu, Nadeem Ullah and Peng Zeng

Remote Sens. 2021, 13(22), 4697; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224697 - 20 Nov 2021

Cited by 35 | Viewed by 3091

Abstract

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km² (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km² (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km² (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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20 pages, 10061 KiB

Open AccessArticle

Retrieval of All-Weather 1 km Land Surface Temperature from Combined MODIS and AMSR2 Data over the Tibetan Plateau

by Yanmei Zhong, Lingkui Meng, Zushuai Wei, Jian Yang, Weiwei Song and Mohammad Basir

Remote Sens. 2021, 13(22), 4574; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224574 - 14 Nov 2021

Cited by 7 | Viewed by 2179

Abstract

Land surface temperature (LST) is one of the most valuable variables for applications relating to hydrological processes, drought monitoring and climate change. LST from satellite data provides consistent estimates over large scales but is only available for cloud-free pixels, greatly limiting applications over frequently cloud-covered regions. With this study, we propose a method for estimating all-weather 1 km LST by combining passive microwave and thermal infrared data. The product is based on clear-sky LST retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) thermal infrared measurements complemented by LST estimated from the Advanced Microwave Scanning Radiometer Version 2 (AMSR2) brightness temperature to fill gaps caused by clouds. Terrain, vegetation conditions, and AMSR2 multiband information were selected as the auxiliary variables. The random forest algorithm was used to establish the non-linear relationship between the auxiliary variables and LST over the Tibetan Plateau. To assess the error of this method, we performed a validation experiment using clear-sky MODIS LST and in situ measurements. The estimated all-weather LST approximated MODIS LST with an acceptable error, with a coefficient of correlation (r) between 0.87 and 0.99 and a root mean square error (RMSE) between 2.24 K and 5.35 K during the day. At night-time, r was between 0.89 and 0.99 and the RMSE was between 1.02 K and 3.39 K. The error between the estimated LST and in situ LST was also found to be acceptable, with the RMSE for cloudy pixels between 5.15 K and 6.99 K. This method reveals a significant potential to derive all-weather 1 km LST using AMSR2 and MODIS data at a regional and global scale, which will be explored in the future. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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25 pages, 14161 KiB

Open AccessArticle

Spatial Downscaling of Land Surface Temperature over Heterogeneous Regions Using Random Forest Regression Considering Spatial Features

by Kai Tang, Hongchun Zhu and Ping Ni

Remote Sens. 2021, 13(18), 3645; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183645 - 12 Sep 2021

Cited by 16 | Viewed by 2807

Abstract

Land surface temperature (LST) is one of the crucial parameters in the physical processes of the Earth. Acquiring LST images with high spatial and temporal resolutions is currently difficult because of the technical restriction of satellite thermal infrared sensors. Downscaling LST from coarse to fine spatial resolution is an effective means to alleviate this problem. A spatial random forest downscaling LST method (SRFD) was proposed in this study. Abundant predictor variables—including land surface reflection data, remote sensing spectral indexes, terrain factors, and land cover type data—were considered and applied for feature selection in SRFD. Moreover, the shortcoming of only focusing on information from point-to-point in previous statistics-based downscaling methods was supplemented by adding the spatial feature of LST. SRFD was applied to three different heterogeneous regions and compared with the results from three classical or excellent methods, including thermal image sharpening algorithm, multifactor geographically weighted regression, and random forest downscaling method. Results show that SRFD outperforms other methods in vision and statistics due to the benefits from the supplement of the LST spatial feature. Specifically, compared with RFD, the second-best method, the downscaling results of SRFD are 10% to 24% lower in root-mean-square error, 5% to 20% higher in the coefficient of determination, 11% to 25% lower in mean absolute error, and 4% to 17% higher in structural similarity index measure. Hence, we conclude that SRFD will be a promising LST downscaling method. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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20 pages, 10810 KiB

Open AccessArticle

Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas

by Shumin Wang, Youming Luo, Xia Li, Kaixiang Yang, Qiang Liu, Xiaobo Luo and Xiuhong Li

Remote Sens. 2021, 13(8), 1580; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081580 - 19 Apr 2021

Cited by 15 | Viewed by 2755

Abstract

Land surface temperature (LST) is a vital physical parameter in geoscience research and plays a prominent role in surface and atmosphere interaction. Due to technical restrictions, the spatiotemporal resolution of satellite remote sensing LST data is relatively low, which limits the potential applications of these data. An LST downscaling algorithm can effectively alleviate this problem and endow the LST data with more spatial details. Considering the spatial nonstationarity, downscaling algorithms have been gradually developed from least square models to geographical models. The current geographical LST downscaling models only consider the linear relationship between LST and auxiliary parameters, whereas non-linear relationships are neglected. Our study addressed this issue by proposing an LST downscaling algorithm based on a non-linear geographically weighted regressive (NL-GWR) model and selected the optimal combination of parameters to downscale the spatial resolution of a moderate resolution imaging spectroradiometer (MODIS) LST from 1000 m to 100 m. We selected Jinan city in north China and Wuhan city in south China from different seasons as study areas and used Landsat 8 images as reference data to verify the downscaling LST. The results indicated that the NL-GWR model performed well in all the study areas with lower root mean square error (RMSE) and mean absolute error (MAE), rather than the linear model. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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29 pages, 23397 KiB

Open AccessArticle

A Two-Step Integrated MLP-GTWR Method to Estimate 1 km Land Surface Temperature with Complete Spatial Coverage in Humid, Cloudy Regions

by Zhen Gao, Ying Hou, Benjamin F. Zaitchik, Yongzhe Chen and Weiping Chen

Remote Sens. 2021, 13(5), 971; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050971 - 04 Mar 2021

Cited by 11 | Viewed by 2334

Abstract

There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth’s land–atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013–2018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29–2.65 K), root mean square error (2.92–3.25 K), and coefficients of determination (0.82–0.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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20 pages, 44919 KiB

Open AccessArticle

Global Land Surface Temperature Change (2003–2017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis

by Jiang Liu, Daniel Fiifi Tawia Hagan and Yi Liu

Remote Sens. 2021, 13(1), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010044 - 24 Dec 2020

Cited by 51 | Viewed by 6446

Abstract

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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Review

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21 pages, 707 KiB

Open AccessReview

A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

by Yaping Mo, Yongming Xu, Huijuan Chen and Shanyou Zhu

Remote Sens. 2021, 13(14), 2838; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142838 - 19 Jul 2021

Cited by 35 | Viewed by 4481

Abstract

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided. Full article

(This article belongs to the Special Issue Land Surface Temperature Estimation Using Remote Sensing)

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