1. Introduction
Land surface temperature (LST) is a critical and reliable land surface feature parameter used to estimate land surface physical processes. Moreover, many other land surface parameters, such as evapotranspiration modeling [
1] and soil moisture [
2], rely on the prior knowledge of LST. Although the in situ temperature measurements could offer long-term coverage and highly accurate information, it is nearly impossible to used them for global monitoring. The thermal infrared (TIR) region is generally referred to a narrower range of 8~12 μm, which is an important atmospheric window for Earth science. Remotely sensed TIR data provide a simultaneous and large-scale view of land surfaces [
3] and LST can be retrieving from TIR imagery. Therefore, TIR satellite imagery provides a quick method over different scales with a positive cost–benefit ratio. In recent decades, various thermal infrared remote sensing data sets have been acquired, as they can be widely applied for both Earth [
4,
5,
6,
7,
8,
9,
10,
11] and Mars [
12,
13,
14,
15,
16,
17].
The first Sustainable Development Goals (SDG-1) satellite is the first sustainable satellite developed by Chinese Academic of Sciences (CAS), and will be launched in 2021. It is intended to provide data support for the United Nations Sustainable Developed Goals. The satellite is intended to be used to provide scientific evidence for the refined depiction of human traces. Arctic and Antarctic observation is an expansion task for the SDG-1 satellite. The satellite needs to make a side swing to cover the entire 66.5°N/S to 90°N/S range. The SDG-1 satellite is equipped with three high-resolution optical payloads: Thermal Infrared Spectrometer (TIS), Glimmer Imager for Urbanization (GIU), and Multispectral Imager for Inshore (MII). The TIS is used for global thermal radiation detection and it has three TIR bands. The three TIR channels centered at 9.3 μm (8.0~10.5 μm, Band 1 (B1)), 10.8 μm (10.3~11.3 μm, Band 2 (B2)), and 11.8 μm (11.5~12.5 μm, Band 3 (B3)) with a spatial resolution of 30 m, which provides more spatial details than the TIR sensors currently in-orbit.
Table 1 compares the thermal infrared band features of SDG-1 TIS and similar in-orbit sensors.
The TIS data could be used to support improved monitoring of ground conditions, so an accurate radiometric calibration is firstly required. The accuracy of physical variables retrieved from remotely sensed data relies highly on the accuracy of radiometric calibration. The radiometric calibration process is to convert the digital number (DN) into the expected at-aperture radiance [
18,
19]. The DN is the raw signal from the detector that expressed as 12-bit numbers and the at-aperture radiance is the radiance enters the aperture that operates in units of
. The preflight calibration ensures the instrument operates properly before being integrated into the launch vehicle, and provides a calibration method to be used after launch [
18]. The preflight calibration of TIS was performed in a thermal-vacuum chamber against a laboratory blackbody and the calibration method that was used to determine the radiometric calibration of TIS was described in this paper.
Retrieving LST from TIR imagery is challenging as the radiance emitted from the surface in the infrared region is a function of temperature and emissivity. For a
N band sensor, there will always be
N+1 unknowns, corresponding to
N emissivities at each band and an unknown temperature. Under specific assumptions, LST can be successfully retrieved from satellite measurements. Consequently, many algorithms have been developed to retrieve LST which can be generally classified into four categories [
20,
21]: the single-channel [
22,
23,
24], day/night [
25,
26,
27,
28], split window (SW) [
29,
30,
31,
32,
33] and temperature-emissivity separation (TES) methods [
34,
35]. Among these methods, SW is widely used because it can accurately remove the atmospheric effects by combining measurements from two adjacent channels at 11 and 12 μm. SW has been used in various satellite data to estimate LST, such as Advanced Very High Resolution Radiometer (AVHRR) [
36], Moderate Resolution Imaging Spectroradiometer (MODIS) [
29], Chinese Geostationary FengYun Meteorological Satellite (FY-2C) [
37] and Chinese Gaofen-5 (GF-5) satellite data [
31]. Herein, the Generalized Split-Window algorithm [
28,
29] was selected to firstly evaluate the ability of TIS on LST retrieval. As TIS sensor has three thermal infrared channels, three-channel SW algorithm was also adapted to estimate LST from TIS data.
This paper is organized as follows:
Section 2 presents the theoretical basis for preflight radiometric calibration and the SW algorithm. A simulation data set was established for LST retrieval.
Section 3 gives the results of radiometric calibration and LST retrieval. Improvement of radiometric calibration method was also performed. The LSTs retrieved by two SW methods were also compared. In addition, the effect of atmospheric column water vapor (CWV) was analyzed. The sensitivity analysis for SW algorithm is described in
Section 4. The conclusions are drawn in
Section 5.
5. Conclusions
To ensure the scientific objectives of the SDG-1 mission, the TIS instrument needed to be accurately calibrated. A series of experiments were performed preflight on the prototype of TIS and a method was developed to convert the raw digital numbers from the detector into an at-aperture radiance. Background subtraction was first performed followed by the conversion to radiance, and finally, linear regression was performed to obtain the calibration coefficients. The whole tested temperature range was divided into three sub-ranges and regressed separately to improve the calibration accuracy, especially for B1. The radiometric calibration errors were less than 1 K for three bands.
SDG-1 will provide observations in three TIR channels at a fine spatial resolution of 30 m. To make full use of the TIR data, the Generalized Split-Window algorithm and a three-channel SW algorithm were used to estimate LST from SDG-1 data and the sensitivity of each algorithm was analyzed. The regression coefficients were obtained from simulation data based on MODTRAN model. The results showed that three-channel method performed better than two-channel method with RMSE lower than 1 K. For different land surface types, the minimum RMSE of LST retrieval was obtained for water samples and the maximum was acquired for soil samples as the spectra of soil samples have more variations than the water spectra. The algorithms were also performed in different CWV subranges and the RMSE of LST retrieval increased with the increase of CWV. A sensitive analysis was conducted with considerations involving NEΔT, uncertainty of LSE, and input land surface temperature, . Both SW algorithms used in this study have its advantages. The Generalized Split-Window algorithm is less sensitive to uncertainties in NEΔT and while the three-channel algorithm refined the retrieval results with less RMSEs. Different methods could be adapted for different purposes to retrieval LST. All in all, TIS channels were eligible to retrieve accurate LSTs using two- or three-channel SW algorithm.
The radiometric calibration and LST retrieval method conducted in this study provide a possible post-launch state which might be easier to control and modified after launch. Also, the TIS sensor has the potential to provide LST results with high spatial resolution. Finally, the results in this study provide guidance to develop a high-precision flight model of TIS instrument that will be equipped on the SDG-1 satellite to fulfil its scientific mission.