Abstract
Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.
Highlights
Land-surface temperature (LST) plays a critical role in land-surface systems at regional through global scales
The Moderate Resolution Imaging Spectroradiometer (MODIS) LST is the input for deriving land-surface evaporation [9], surface moisture [10,11], air temperature [12,13,14,15], net radiation [16], and gross primary production [17]; it participates in building urban heat island indicators [18,19], the scaled drought condition index (SDCI) [20], the temperature-vegetation-soil moisture dryness index (TVMDI) [21], and the remote sensing-based ecological index (RSEI) [22], and it can help validate land-surface models and urban canopy models [23]
Under clear-sky conditions, the mean absolute error (MAE) between the MODIS-observed LST and the surface radiation budget network (SURFRAD) LST was 2.49 k in 2015 and 2.65 k in 2016, with an MAE value close to 2.6 K (Figure 3a, Figure 4a). This value can be regarded as the original maximum accuracy that can be achieved under ideal conditions, that is, in the presence of clouds, neither the reconstructed clear-sky LST nor the calculated real LST can be higher than this accuracy
Summary
Land-surface temperature (LST) plays a critical role in land-surface systems at regional through global scales. It directly affects the long-wave radiation budget, turbulent heat flux division, and hydrothermal balance [1,2]. The MODIS LST is the input for deriving land-surface evaporation [9], surface moisture [10,11], air temperature [12,13,14,15], net radiation [16], and gross primary production [17]; it participates in building urban heat island indicators [18,19], the scaled drought condition index (SDCI) [20], the temperature-vegetation-soil moisture dryness index (TVMDI) [21], and the remote sensing-based ecological index (RSEI) [22], and it can help validate land-surface models and urban canopy models [23]
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