Abstract
Land surface temperature (LST) is a critical state variable of land surface energy equilibrium and a key indicator of environmental change such as climate change, urban heat island, and freezing-thawing hazard. The high spatial and temporal resolution datasets are urgently needed for a variety of environmental change studies, especially in remote areas with few LST observation stations. MODIS and Landsat satellites have complementary characteristics in terms of spatial and temporal resolution for LST retrieval. To make full use of their respective advantages, this paper developed a pixel-based multi-spatial resolution adaptive fusion modeling framework (called pMSRAFM). As an instance of this framework, the data fusion model for joint retrieval of LST from Landsat-8 and MODIS data was implemented to generate the synthetic LST with Landsat-like spatial resolution and MODIS temporal information. The performance of pMSRAFM was tested and validated in the Heihe River Basin located in China. The results of six experiments showed that the fused LST was high similarity to the direct Landsat-derived LST with structural similarity index (SSIM) of 0.83 and the index of agreement (d) of 0.84. The range of SSIM was 0.65–0.88, the root mean square error (RMSE) yielded a range of 1.6–3.4 °C, and the averaged bias was 0.6 °C. Furthermore, the temporal information of MODIS LST was retained and optimized in the synthetic LST. The RMSE ranged from 0.7 °C to 1.5 °C with an average value of 1.1 °C. When compared with in situ LST observations, the mean absolute error and bias were reduced after fusion with the mean absolute bias of 1.3 °C. The validation results that fused LST possesses the spatial pattern of Landsat-derived LSTs and inherits most of the temporal properties of MODIS LSTs at the same time, so it can provide more accurate and credible information. Consequently, pMSRAFM can be served as a promising and practical fusion framework to prepare a high-quality LST spatiotemporal dataset for various applications in environment studies.
Highlights
Land surface temperature (LST) is the measurement of the radiative skin temperature over the earth’s surface [1]
The pixel of MOD11A1 was constrained to that: the local time of MOD11A1 observation was limited to 11:42 ~ 11: 48, the zenith angles were within the range from 60◦ to 62◦, pixel observation was limited to 11:42 ~ 11: 48, the zenith angles were within the range from 60° to and62°, there invalid valuevalue in the corresponding
This study proposed an operational data fusion framework, named pMSRAFM, for generating the synthetic LST based on the assumption that the correspondence between LSTs at different resolutions or from different sources can be linearly modeled
Summary
Land surface temperature (LST) is the measurement of the radiative skin temperature over the earth’s surface [1]. As a crucial variable associated with the energy balance, LST is highly responsive to land surface energy equilibrium [2,3], which may contribute to explore a variety of phenomena taking place at the surface-atmosphere interface [4] It becomes valuable for various scientific studies [1], such as climatology [5], drought monitoring [6], urban heat island [7,8,9], hydrology [10,11], infrastructure [12], agriculture [13,14], public health [15], and permafrost mapping [16]. In remote mountainous areas where the in situ measurements are of scarcity and uneven, satellite-derived LSTs can serve as an efficient proxy for air temperature estimation [18].
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