Abstract
Land surface temperature (LST) retrieval is a key issue in infrared quantitative remote sensing. In this paper, a split window algorithm is proposed to estimate LST with daytime data in two mid-infrared channels (channel 66 (3.746~4.084 μm) and channel 68 (4.418~4.785 μm)) from Airborne Hyperspectral Scanner (AHS). The estimation is conducted after eliminating reflected direct solar radiance with the aid of water vapor content (WVC), the view zenith angle (VZA), and the solar zenith angle (SZA). The results demonstrate that the LST can be well estimated with a root mean square error (RMSE) less than 1.0 K. Furthermore, an error analysis for the proposed method is also performed in terms of the uncertainty of LSE and WVC, as well as the Noise Equivalent Difference Temperature (NEΔT). The results show that the LST errors caused by a LSE uncertainty of 0.01, a NEΔT of 0.33 K, and a WVC uncertainty of 10% are 0.4~2.8 K, 0.6 K, and 0.2 K, respectively. Finally, the proposed method is applied to the AHS data of 4 July 2008. The results show that the differences between the estimated and the ground measured LST for water, bare soil and vegetation areas are approximately 0.7 K, 0.9 K and 2.3K, respectively.
Highlights
Land surface temperature (LST) is an important indicator for monitoring the changing of earth resources and one of the most critical parameters in the physical process of surface energy and water balance at local and global scales [1,2,3,4,5,6]
The study on LST retrieval from Mid-Infrared (MIR) data is under-developed because the radiance measured during daytime in the MIR spectrum contains both the surface emitted thermal radiance and the reflected solar radiance, which are equal in magnitude [21,22]
The above analyses show that Di can be expressed as a function of water vapor content (WVC), solar zenith angle (SZA) and view zenith angle (VZA), and the fitting coefficients can be obtained using the simulated data
Summary
Land surface temperature (LST) is an important indicator for monitoring the changing of earth resources and one of the most critical parameters in the physical process of surface energy and water balance at local and global scales [1,2,3,4,5,6]. Enhanced Visible and Infrared Imager (SEVIRI), provide a valuable way for measuring LST over the entire globe. Different methods have been proposed to retrieve LST from Thermal Infrared (TIR) remotely sensed data, such as the single channel algorithm and the split-window algorithm [16,17,18,19,20]. The study on LST retrieval from Mid-Infrared (MIR) data is under-developed because the radiance measured during daytime in the MIR spectrum contains both the surface emitted thermal radiance and the reflected solar radiance, which are equal in magnitude [21,22]. Sun and Pinker proposed a split algorithm, with three TIR channels and one MIR channel, to retrieve LST from SEVIRI data [24].
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