Abstract
Satellite thermal remote sensing provides land surface temperatures (LST) over extensive areas that are vital in various applications, but this technique suffers from its sampling style and the impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC) models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS LST products as validation data, we implemented and tested the ATCE over the Yangtze River Delta region of China. The results demonstrate that, when compared with the ATCS, the overall root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively. The accuracy improvements vary with land cover types with greater improvements over the forest, grassland, and built-up areas than over cropland and wetland. The assessments at different time scales further confirm that LST fluctuations can be better described by the ATCE. Though with limitations, we consider this new model and its associated parameters hold great potentials in various applications.
Highlights
Satellite thermal infrared remote sensing allows generating land surface temperature (LST) products over extensive areas, which are indispensable for various applications [1], such as the quantification of land-atmosphere interactions [2], investigation of urban thermal environments [3], identification of forest fires [4], and monitoring of volcanoes [5] and earthquakes [6]
These models can be generally divided into three categories with respect to the time scale: inter-annual temperature dynamics (ATD) models [8,10,11], inner-annual temperature cycle (ATC) models [7,12], and diurnal temperature cycle (DTC) models [13,14,15,16,17,18,19,20]
In terms of the LST fluctuations induced by the synoptic conditions and vegetation phenology, this study enhanced the standard sinusoidal ATC model (ATCS) by incorporating in-situ surface air temperatures (SATs) measured at meteorological stations as well as the normalized difference vegetation indexes (NDVIs) data
Summary
Satellite thermal infrared remote sensing allows generating land surface temperature (LST) products over extensive areas, which are indispensable for various applications [1], such as the quantification of land-atmosphere interactions [2], investigation of urban thermal environments [3], identification of forest fires [4], and monitoring of volcanoes [5] and earthquakes [6]. While from the semi-physical perspective, Bechtel [30] presented the modeling of annual LST variations by a standard sinusoidal function (termed the ATCS hereafter) and later suggested the use of a second sinusoidal term outside the mid-latitudes [31] This sinusoidal model generally performs well on the reconstruction of LST dynamics; and the derived model parameters are worthwhile for the quantification of surface properties [12,27]. The incorporation of statistical processes/functions into physical methods can handle the LST fluctuations due to synoptic conditions and vegetation phenology, they become less effective once the number of temporal thermal observations is limited To resolve these issues, this study continues to employ the synthetic approach by combining the ATCS originally proposed by Bechtel [30] with auxiliary remote sensing and in-situ data.
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