All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature

Wang, Tianxing; Shi, Jiancheng; Ying, Ma; Letu, Husi; Li, Xingcai

doi:10.1016/j.isprsjprs.2020.01.011

Cited by 41 publications

(15 citation statements)

References 69 publications

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“…LST is related to the balance of energy and water between the land surface and atmosphere (Anderson et al, 2008;Becker and Li 1990;Li et al, 2013), influencing the growth of vegetation and land use and land cover (Amiri et al, 2009;Tran et al, 2017). LSTs retrieved from satellite measurements are widely used in drought monitoring (Huang et al, 2008), surface soil moisture and evapotranspiration estimations (Jiang and Islam 1999;Leng et al, 2017;Sandholt et al, 2002;Wang et al, 2020), near-surface air temperature retrieval (Vancutsem et al, 2010;Zhu et al, 2013), and urban heat island and climate change studies (Li et al, 2015;Weng et al, 2004). High-accuracy LST values can document secular trends in global temperature, such that the International Geosphere and Biosphere Programme (IGBP) listed LST as one of the priority parameters (Townshend et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Xing

Duan

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

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Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. 2Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

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Section: Introductionmentioning

confidence: 99%

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Xing

Duan

et al. 2021

ISPRS Journal of Photogrammetry and Remote Sensing

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“…3). The threshold of 20 W m −2 is the current accuracy level of the longwave radiation products (Wang et al, 2020).…”

Section: Cloud Effect Estimationmentioning

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

Liang

Wang

et al. 2023

Earth Syst. Sci. Data

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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 in 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 the Moderate Resolution Imaging Spectroradiometer (MODIS). Based on ground measurements at the 201 global sites from the Surface Radiation Budget (SURFRAD), Baseline Surface Radiation Network (BSRN), Fluxnet, AmeriFlux, Heihe River basin (HRB), and Tibetan Plateau (TP) networks, the overall root-mean-square error (RMSE) of the hourly GHA-LST product was 3.31 K, with a bias of −0.57 K and R2 of 0.95. Thus, this product was more accurate than the clear-sky CGLS and MODIS MYD21C1 LST samples. The RMSE value of the daily mean LST was 1.76 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.7487284 (Jia et al., 2022b) and http://glass.umd.edu/allsky_LST/GHA-LST (last access: 10 February 2023; Jia et al., 2022c).

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“…By iteratively comparing the Rc differences and adjusting ΔTs (step = 0.05 K), the surface energy budget is balanced (|ΔCRE| < 20 W•m -2 , see Figure 3). The threshold of 20 W•m -2 is the current accuracy level of the longwave radiation products (Wang et al, 2020).…”

Section: Cloud Effect Estimationmentioning

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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All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature

Cited by 41 publications

References 69 publications

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

Estimation of daily mean land surface temperature at global scale using pairs of daytime and nighttime MODIS instantaneous observations

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

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