Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques

Wu, Penghai; Yin, Zhixiang; Zeng, Chao; Duan, Si‐Bo; Göttsche, Frank-M.; Ma, Xiaoshuang; Li, Xinghua; Yang, Hui; Shen, Huanfeng

doi:10.1109/mgrs.2021.3050782

Cited by 77 publications

(39 citation statements)

References 154 publications

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“…First, the performance of the proposed framework is greatly reliant on availability of remote sensing imagery . Typically, the available remote sensing imagery may be insufficient for a study area, particularly in cloudy regions such as tropical and subtropical areas [31], [32]. In addition, the downloading, storage, and processing of imagery may be timeconsuming.…”

Section: Discussionmentioning

confidence: 99%

A Framework to Calculate Annual Landscape Ecological Risk Index Based on Land Use/Land Cover Changes: A Case Study on Shengjin Lake Wetland

Wu¹,

Zhan²,

Cheng³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Discussionmentioning

confidence: 99%

A Framework to Calculate Annual Landscape Ecological Risk Index Based on Land Use/Land Cover Changes: A Case Study on Shengjin Lake Wetland

Wu¹,

Zhan²,

Cheng³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Furthermore, the training samples of T dm ( T sb ) can also be from geostationary satellite data, which can help reduce the computational complexity of the DTC modeling. Third, other highly efficient under-cloud LST reconstruction methods, such as statistical interpolation, spatiotemporal fusion, and the passive microwave-based method (Wu et al, 2021;Hong et al, 2021), or the generated under-cloud LST products (Zhang et al, 2022;Zhao et al, 2020) can replace the ATC model in the T dm generation framework. Similarly, more efficient diurnal LST dynamics modeling methods can also replace the DTC model (Jia et al, 2022).…”

Section: Future Perspectivesmentioning

confidence: 99%

A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Hong

Goettsche

Liu

et al. 2022

Earth Syst. Sci. Data

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Abstract. Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔTsb) on the daily mean LST (Tdm) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the Tdm. Several methods have been proposed for the estimation of the Tdm, yet they are becoming less capable of generating spatiotemporally seamless Tdm across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless Tdm products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔTsb significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the Tdm estimated with the traditional averaging method yields a positive systematic ΔTsb of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr−1), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr−1. We consider the IADTC framework can guide the further optimization of Tdm estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis. The GADTC products are freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).

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“…Additionally, the relationship of fine and coarse resolution data varies diurnally, which could not be captured adequately using a static downscaling approach. Comprehensive reviews of advancement in LST downscaling methods (both spatially and temporally) along with their advantages and limitations were recently outlined by Mao et al [32] and Wu et al [33].…”

Section: Introductionmentioning

confidence: 99%

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

et al. 2022

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The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

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Spatially Continuous and High-Resolution Land Surface Temperature Product Generation: A review of reconstruction and spatiotemporal fusion techniques

Cited by 77 publications

References 154 publications

A Framework to Calculate Annual Landscape Ecological Risk Index Based on Land Use/Land Cover Changes: A Case Study on Shengjin Lake Wetland

A Framework to Calculate Annual Landscape Ecological Risk Index Based on Land Use/Land Cover Changes: A Case Study on Shengjin Lake Wetland

A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

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