Generating 60–100 m, hourly, all-weather land surface temperatures based on the Landsat, ECOSTRESS, and reanalysis temperature combination (LERC)

Quan, Jinling; Guan, Yongjuan; Zhan, Wenfeng; Ma, Ting; Wang, Dandan; Guo, Zheng

doi:10.1016/j.isprsjprs.2023.10.004

Cited by 3 publications

(1 citation statement)

References 77 publications

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“…These missing gaps can be potentially addressed through data fusion (e.g., Kuznetsov & Gashnikov, 2020; Li et al., 2022; Luo et al., 2018). Reanalysis and land data assimilation system (LDAS) data sets provide spatiotemporally continuous LST under all‐weather conditions (e.g., Quan et al., 2023; Rodell et al., 2004). Data fusion approaches can be used to merge land data assimilation system and reanalysis data with ECOSTRESS or satellite remote sensing data to produce temporally continuous LST and address the temporal continuity of ECOSTRESS (Long et al., 2020; C. Zhang et al., 2021; X. Zhang et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

Rashid,

Tian

2024

Water Resources Research

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This study developed and evaluated 30‐m daily evapotranspiration (ET) estimates using the Priestley‐Taylor Jet Propulsion Laboratory (PT‐JPL) model with ECOSTRESS, Moderate MODIS, harmonized Landsat Sentinel‐2 (HLS) imagery, ERA5‐Land reanalysis, and eddy covariance measurements. The new daily 30‐m ET showed significantly improved performance (overall, r = 0.8, RMSE = 1.736, KGE = 0.466) at 145 EC sites over contiguous United States compared to the current 70‐m ECOSTRESS ET (overall, r = 0.485, RMSE = 4.696, KGE = −0.841). A deep neural network postprocessing model trained with ET measurements from EC sites further improved the performance on test sites that were not used for model training (overall, r = 0.842, RMSE = 0.88, KGE = 0.792). The 30‐m ET estimation biases were significantly related to the biases in the upwelling longwave (RUL) and downwelling shortwave radiation (RDS) inputs, with ET estimates driven by MODIS radiation showing higher biases compared to those driven by ERA5‐Land radiation. The error diagnosis using random forest indicates that ET biases tend to be larger under higher ET estimates, and RUL and RDS were the primary contributors to the high bias at the higher ET ranges, with partial dependence plots revealing that the estimation biases tend to be higher under more humid environment, denser vegetation covers, and high net radiation conditions. In conclusion, higher spatial resolution satellite imagery of vegetation characteristics and higher temporal resolution radiation data, combined with continent‐wide EC measurements and deep learning, provided substantial added value for improving ET estimations at the field scale (30‐m).

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Section: Discussionmentioning

confidence: 99%

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

Rashid,

Tian

2024

Water Resources Research

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Improving the Spatiotemporal Resolution of Land Surface Temperature Using a Data Fusion Method in Haihe Basin, China

Lin,

Wei,

Chen

et al. 2024

Remote Sensing

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Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts.

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TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Tang,

Zhou,

et al. 2024

Earth Syst. Sci. Data

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Abstract. Land surface temperature (LST) is a key variable within Earth's climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China's landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37 K and a standard deviation of bias (SD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from −2.26 to 1.73 K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling. The TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

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Generating 60–100 m, hourly, all-weather land surface temperatures based on the Landsat, ECOSTRESS, and reanalysis temperature combination (LERC)

Cited by 3 publications

References 77 publications

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

Improved 30‐m Evapotranspiration Estimates Over 145 Eddy Covariance Sites in the Contiguous United States: The Role of ECOSTRESS, Harmonized Landsat Sentinel‐2 Imagery, Climate Reanalysis, and Deep Neural Network Postprocessing

Improving the Spatiotemporal Resolution of Land Surface Temperature Using a Data Fusion Method in Haihe Basin, China

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

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