Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data

Jia, Aolin; Liang, Shunlin; Wang, Dongdong

doi:10.1016/j.rse.2022.113105

Cited by 36 publications

(29 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…hourly LSTs were available in a day, which is sufficient to represent the entire DTC based on Jia et al (2022a). The validation period was 2011-2020.…”

Section: Ground Measurementmentioning

confidence: 99%

“…In addition, partially cloud-covered yet retrieved pixels were masked out before assimilation. The detection method follows Jia et al (2022a). One clear-sky retrieval is excluded if it has a substantially larger absolute difference with the modeled LST (three standard deviations) than neighboring days within ±15 days, which assumes that modeled LSTs have fewer anomalies than directly retrieved values.…”

Section: Time-evolving Modelmentioning

confidence: 99%

“…Therefore, the optimal ΔTs must be determined based on the optimization method to satisfy the SEB. Following Jia et al (2022a), 𝛥𝛥𝐿𝐿 𝑠𝑠 was initially assumed to be 0 K and the initial Rc was obtained based on Eq. 12.…”

Section: Cloud Effect Estimationmentioning

confidence: 99%

“…surface longwave radiation data were used to build a spatiotemporally evolving model, and the clear-sky GEO LSTs were assimilated to the evolving model to correct its predictions on cloudy days. Moreover, an optimization method was used to compute the cloud effect during both the daytime and nighttime, and complete DTCs could be recovered for hourly LSTs (Jia et al, 2022a).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Jia

Liang²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Land surface temperature (LST) plays a dominant role in the surface energy budget (SEB) and hydrological cycling. Thermal infrared (TIR) remote sensing is the primary method of estimating LST globally. However, cloud cover leaves numerous data gaps in satellite LST products, which seriously restricts their applications. Efforts have been made to produce gap-free LST products from polar-orbiting satellites (e.g., Terra and Aqua); however, satellite data from limited overpasses are not suitable for characterizing the diurnal temperature cycle (DTC), which is directly related to heat waves, plant water stress, and soil moisture. Considering the high temporal variability of LST and the importance of the DTC, we refined the SEB-based cloudy-sky LST recovery method by improving its feasibility and efficiency and produced a global hourly, 5 km, all-sky land surface temperature (GHA-LST) dataset from 2011 to 2021. The GHA-LST product was generated using TIR LST products from geostationary and polar-orbiting satellite data from the Copernicus Global Land Service (CGLS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Based on the ground measurements at the 197 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River Basin (HRB), and Tibet Plateau (TP) networks, the overall root mean square error (RMSE) of the hourly GHA-LST product is 3.38 K, with a bias of -0.53 K and R2 of 0.95, and it was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.67 K. Validation results at individual sites indicate that the GHA-LST dataset has relatively larger RMSEs for high elevation regions, which can be attributed to high surface heterogeneity and input data uncertainty. Temporal and spatial analyses suggested that GHA-LST has satisfactory spatiotemporal continuity and reasonable variation and matches the reference data well at hourly and daily scales. Furthermore, the regional comparison of GHA-LST with other gap-free hourly datasets (ERA5 and Global Land Data Assimilation System, GLDAS) demonstrated that GHA-LST can provide more spatial texture information. The monthly anomaly analysis suggests that GHA-LST couples well with global surface air temperature datasets and other LST datasets at daily mean and minimum temperature scales, whereas the maximum temperature and diurnal temperature range of LST and air temperature (AT) have different anomalous magnitudes. The GHA-LST dataset is the first global gap-free LST dataset at an hourly, 5 km scale with high accuracy, and it can be used to estimate global evapotranspiration, monitor extreme weather, and advance meteorological forecasting models. GHA-LST is freely available at https://doi.org/10.5281/zenodo.6981704 (Jia et al., 2022c) and glass.umd.edu/allsky_LST/GHA-LST.

show abstract

“…hourly LSTs were available in a day, which is sufficient to represent the entire DTC based on Jia et al (2022a). The validation period was 2011-2020.…”

Section: Ground Measurementmentioning

confidence: 99%

Section: Time-evolving Modelmentioning

confidence: 99%

Section: Cloud Effect Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Jia

Liang²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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).

show abstract

Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Jia

Wang

Liang

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data

Cited by 36 publications

References 106 publications

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

Global hourly, 5 km, all-sky land surface temperature data from 2011 to 2021 based on integrating geostationary and polar-orbiting satellite data

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

Improved cloudy-sky snow albedo estimates using passive microwave and VIIRS data

Contact Info

Product

Resources

About