A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Zuyi, Tao; Zhou, Yuyu; Zhu, Zhengyuan; Li, Xiaoma; Asrar, Ghassem

doi:10.5194/essd-2021-313

Cited by 8 publications

(13 citation statements)

References 21 publications

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“…Monthly anomaly variations in global LST and AT are shown in 2014)) were used to characterize the AT variation (Figure 11a). Other all-sky LST datasets were averaged and shown to verify the GHA-LST anomaly, including two MODIS-derived gap-free results, as shown in Table 1 (Hong et al, 2022;Zhang et al, 2022) The LST anomaly couples well with the global AT anomaly at the Tmean and Tmin scales, and LST has a slightly larger amplitude than AT, whereas the Tmax and DTR of the two variables can only match the anomalous direction and the magnitude is quite different. At the Tmean scale, anomalies of GHA-LST and other LSTs have very similar variations with the AT datasets, even though they have completely different data sources (Figure 11a).…”

Section: Global Anomaly Analysismentioning

confidence: 99%

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

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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.

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Section: Global Anomaly Analysismentioning

confidence: 99%

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

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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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“…Long‐term satellite‐retrieved LST and land‐cover data provide a self‐consistent standard and enable comparative analysis of the RSUHI spatial pattern and scale over urban agglomerations from a regional perspective. A global seamless 1‐km‐resolution daily clear‐sky LST data set for 2003–2020, retrieved from the Mid‐Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites and generated by using a spatiotemporal gap‐filling framework to gap‐fill missing values in the initial MODIS/LST (X. Zhang, Liu, et al., 2022; T. Zhang, Zhou, et al., 2022), was used to quantify the RSUHIs in the YRD urban agglomeration. The Terra and Aqua combined 500‐m‐resolution land‐cover data set (MCD12Q1, 2001–2020) was used to extract the land cover types of croplands, forests and urban areas (Abercrombie & Friedl, 2016).…”

Section: Datamentioning

confidence: 99%

“…The Terra and Aqua combined 500‐m‐resolution land‐cover data set (MCD12Q1, 2001–2020) was used to extract the land cover types of croplands, forests and urban areas (Abercrombie & Friedl, 2016). A 30‐m‐resolution Landsat impervious surface data set at a 5‐year interval (GISD30, 1985–2020) (X. Zhang et al., 2022; T. Zhang et al., 2022) was used to investigate the expansion of suburbs and small towns around the large cities.…”

Section: Datamentioning

confidence: 99%

An Approach to Characterizing the Spatial Pattern and Scale of Regional Heat Islands Over Urban Agglomerations

Xie

Yun

Miao

et al. 2022

Geophysical Research Letters

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Under the driving forces of economic globalization, new industrialization, and the fast transportation, urban agglomerations are gradually becoming the most important urban spatial form for China's New Urbanization plan in recent years (Fang & Yu, 2017). Rapid urbanization has changed urban climate and aggravated summertime heat waves over the Jing-Jin-Ji, Yangtze River Delta (YRD) and Pearl River Delta urban agglomerations in China through creating regional surface urban heat islands (RSUHIs) (Y. Du et al., 2007;Liu et al., 2018). The interaction of global warming and rapid urbanization is changing the urban thermal environment and exposing more people to heat stress in large cities (Kim & Brown, 2021a). Socially and economically vulnerable older and low-income residents in suburban and rural areas around urban agglomerations might confront enhanced heat-related health risks (Bradford et al., 2015). World Cities Report 2020 by the United Nation's Habitat program suggested that unplanned urbanization represents a threat to environmental sustainability, biodiversity, energy consumption and land-use change (Knudsen et al., 2020). Accurately estimating the spatial pattern and scale of RSUHIs is conducive to improving urban planning and minimizing heat-related health risks (Fang & Yu, 2017;Kim & Brown, 2021a).Dramatic urbanization is taking place in many developing countries, and insights into the spatial pattern and scale of RSUHIs can provide guidance for urban planning on a regional scale and help establish policies that mitigate the adverse effects of RSUHIs (Kim & Brown, 2021b). Surface urban heat islands (SUHIs) tend to vary strongly with changes in time and space and have been extensively studied in North America, Europe and East Asia using

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A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Cited by 8 publications

References 21 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

An Approach to Characterizing the Spatial Pattern and Scale of Regional Heat Islands Over Urban Agglomerations

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