Abstract
Land Surface Temperature (LST) is a key parameter in climate systems. The methods for retrieving LST from hyperspectral thermal infrared data either require accurate atmospheric profile data or require thousands of continuous channels. We aim to retrieve LST for natural land surfaces from hyperspectral thermal infrared data using an adapted multi-channel method taking Land Surface Emissivity (LSE) properly into consideration. In the adapted method, LST can be retrieved by a linear function of 36 brightness temperatures at Top of Atmosphere (TOA) using channels where LSE has high values. We evaluated the adapted method using simulation data at nadir and satellite data near nadir. The Root Mean Square Error (RMSE) of the LST retrieved from the simulation data is 0.90 K. Compared with an LST product from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat, the error in the LST retrieved from the Infared Atmospheric Sounding Interferometer (IASI) is approximately 1.6 K. The adapted method can be used for the near-real-time production of an LST product and to provide the physical method to simultaneously retrieve atmospheric profiles, LST, and LSE with a first-guess LST value. The limitations of the adapted method are that it requires the minimum LSE in the spectral interval of 800–950 cm−1 larger than 0.95 and it has not been extended for off-nadir measurements.
Highlights
Land Surface Temperature (LST) is a key parameter in climate systems
The central wavenumbers of the channels and the αi calculated in Section 2 were used to retrieve LST from the independent simulation data
Assuming the channel Land Surface Emissivity (LSE) have large values in the spectral interval of 800–950 cm1, we adapted the multi-channel method to retrieve LST from hyperspectral thermal data for natural land surfaces using 36 channels centered in the spectral interval of 800–950 cm1 with simulation data
Summary
Land Surface Temperature (LST) is a key parameter in climate systems. LST is used for Earth surface energy budget studies [1], numerical weather/climate forecasting [2], the retrieval of climate variables [3], soil moisture/evapotranspiration estimations [4], and generation of time-consistentLST product [5,6]. Sensors 2016, 16, 687 method [19], the Temperature and Emissivity Separation (TES) method [20], the multi-temporal physical method [21], the Kalman filter physical method [22], and the Two-Step Retrieval Method. The single channel method requires good knowledge of the Land Surface Emissivity (LSE) at the channel used and an accurate atmospheric profile. The SW method requires accurate atmospheric water vapor content and LSE for land applications [8]. The TES method, the multi-temporal physical method, and the Kalman-filter physical method require good atmospheric corrections [21,22,28]. The expected accuracy of a LST product from thermal infrared sensors is less than 1 K [29], this has not yet been achieved
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
