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

Abstract

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.

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

“…First, the similarity of the ATC and DTC model parameters among neighboring pixels can be utilized to accelerate the calculation speed considerably (Hong et al, 2021;Hu et al, 2020;Zhan et al, 2016). Second, the physically based IADTC framework can also be integrated with some statistical or empirical estimation strategies (both on T dm or on T sb ) to help improve the computational efficiency (Xing et al, 2021). This is reasonable as T sb (and T dm ) is generally related to local surface properties (Figs.…”

“…However, most previous studies employed temporally aggregated results (8 d or monthly mean) of instantaneous cloud-free LSTs for long-term LST time-series analysis (Mao et al, 2017;Sobrino et al, 2020a, b;Xing et al, 2021), instead of continuous daily mean LST (termed as T dm ) on a day-to-day basis. Compared with the continuous daily T dm , temporally aggregated results of instantaneous cloudfree LSTs lack the information of under-cloud thermal observations and insufficiently sample the LST diurnal dynamics (Ermida et al, 2019;Hu et al, 2020;Westermann et al, 2012).…”

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

“…First, the similarity of the ATC and DTC model parameters among neighboring pixels can be utilized to accelerate the calculation speed considerably (Hong et al, 2021;Hu et al, 2020;Zhan et al, 2016). Second, the physically-based IADTC framework can also be integrated with some statistical or empirical estimation strategies (both on Tdm or on ΔTsb) to help improving the computational efficiency (Xing et al, 2021). This is reasonable as ΔTsb (and Tdm) is generally related to local surface properties (Fig.…”

“…However, most previous studies employed temporally aggregated results (8-day or monthly mean) of instantaneous cloud-free LSTs for long-term LST time series analysis (Mao et al, 2017;Sobrino et al, 2020a;Sobrino et al, 2020b;Xing et al, 2021), instead of continuous daily mean LST (termed as Tdm) on a day-to-day basis. Compared with the continuous daily Tdm, temporally aggregated results of instantaneous cloud-free LSTs lack the information of under-cloud thermal observations and insufficiently sample the LST diurnal dynamics (Ermida et al, 2019;Hu et al, 2020;Westermann et al, 2012).…”

Understanding the Drivers of Land Surface Temperature Based on Multisource Data: A Spatial Econometric Perspective

“…These impervious surfaces with high thermal capacity led to a significant changing trend in land Menghang Liu and Haitao Ma are with the Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and surface temperature (LST), which has altered the original urban energy balance [4][5][6][7]. This transition poses a threat to social and environmental issues, such as human health [8], energy consumption [9], heat waves [10], air pollution [11], and climate change [12][13][14]. Therefore, a deep investigating of the spatial patterns of LST is critically significant to support ecofriendly and sustainable urban planning.…”