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

Abstract

Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination, indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang et al., 2021).

A global dataset of daily near-surface air temperature at 1-km resolution (2003–2020)

“…7) and overcast weather (days 219 and 293 in Fig. 7) (Zhang et al, 2022a), the gridded Ta can illustrate the UHI phenomenon. An existing study has found that the estimated Ta in urban areas 210 was more accurate than those of other regions (Zhang et al, 2022b), specifically suggesting its great value for urban applications.…”

HiTIC-Monthly: A High Spatial Resolution (1 km×1 km) Monthly Human Thermal Index Collection over China from 2003 to 2020

“…In this study, eight variables reflecting the changes and spatial distribution characteristics of temperature were 170 used to predict human thermal indices (Table 1) in addition to the meteorological variables. As LST is one of the most essential parameters for predicting human thermal indices, the seamless LST dataset created by Zhang et al (2022b) The spatial resolution of this dataset is 3″ (i.e., ~90 meters at the equator). In addition, the slope was also extracted from the elevation data to act as the topography predictor.…”

“…Limited is high-resolution multiple human thermal stress indices dataset, compared with extensive studies in producing land surface temperature (LST) or near surface air temperatures (SAT). In particular, 100 numerous LST datasets, such as Land Surface Temperature in China (LSTC) (Zhao et al, 2020) and the global seamless land surface temperature dataset (Zhang et al, 2022b;Hong et al, 2022), and near surface air temperature datasets such as ERA5 (ECMWF, 2017), TerraClimate (Abatzoglou et al, 2018), and GPRChinaTemp1km (He et al, 2021) have been produced. Few coarse-resolution human thermal stress datasets have been produced, such as ERA5-HEAT (Di Napoli et al, 2020), 105 HDI_0p25_1970_2018 (hereafter, HDI) (Mistry, 2020), andHiTiSEA (Yan et al, 2021).…”

“…These indices include wet bulb globe temperature (WBGT), predicted mean vote (PMV), UTCI, from a global seamless 1 km resolution daily LST dataset (Zhang et al, 2022b). This dataset was generated based on spatiotemporal gap-filling algorithms and the MODIS LST dataset.…”

Sustainable urban systems: from landscape to ecological processes

“…A variety of models and algorithms have been proposed to improve existing satellite observations or develop new indicators for monitoring urban environment. For example, with the advance of these methods, the gap-filled seamless data of land surface temperature and aerosol optical depth have been developed (Zhang et al 2022 ; Li et al 2020b ), and derived products from satellite observations such as phenology indicators with high spatial resolutions in urban areas are emerging (Li et al 2019 ). These improved products in terms of coverage or spatial resolution serve as important and complementary information to understand urban environment.…”