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Technical Note

SDGSAT-1 TIS Prelaunch Radiometric Calibration and Performance

1
State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai 200083, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
International Research Center of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China
4
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(18), 4543; https://doi.org/10.3390/rs14184543
Submission received: 14 July 2022 / Revised: 30 August 2022 / Accepted: 8 September 2022 / Published: 11 September 2022

Abstract

:
SDGSAT-1 was launched in November 2021, and TIS (Thermal infrared sensor) is a major instrument onboard this satellite. An analysis of the radiometric calibration and noise performance of the TIS has been carried out in the thermal vacuum chamber before launch in order to ensure that it meets the requirements. The prelaunch test results show NEdT (noise equivalent temperature difference) is 0.034 K, 0.047 K and 0.076 K@300 K for the three bands, respectively. The maximum fitting residuals are less than 0.5 K at measured blackbody temperatures. In addition, this paper also discusses the dependence between TIS performance and instrument temperature and focal plane array (FPA) temperature. The good radiometric and noise performance of TIS demonstrates it has potential to provide high resolution thermal remote sensing data in urban heat island, and other environmental issues research.

1. Introduction

SDGSAT-1 is the first scientific satellite dedicated to serving the United Nations 2030 Agenda for Sustainable Development. It was launched on 5 November 2021, with three optical imagers, thermal infrared sensor (TIS), Multispectral Imager for Inshore (MII) and Glimmer Imager for Urbanization (GIU). SDGSAT-1 operates in an orbit of 505 km, and it can obtain global coverage imagery every 11 days. TIS is a whiskbroom sensor with a resolution of 30 m and a swath of 300 km. Compared with other thermal infrared sensors, the TIS has high resolution and wide swath, as shown in Table 1. This enables high quality remote sensing data for environmental monitoring, such as pollution (water, soil and air pollution) and ecological function assessment, arable land census and so on. Combined with GIU and MII remote sensing data, it can provide valuable information which can be used to engrave human activities, city functions and urban distribution. The data is helpful for “the Belt and Road Initiative” and sustainable development programs [1,2,3].
Three thermal bands on the TIS 8.0–10.5 μm, 10.3–11.3 μm and 11.5–12.5 μm provide a significant advantage over thermal bands in that three channel split-window algorithm, which allows for greater accuracy in land surface temperature retrieval [4,5,6,7].
Before launch, TIS carried out a collection of component level, subsystem level and system level experiments. These tests are required to determine whether the performance meets the requirements. Furthermore, some parameters, such as relative spectral response, which is a critical parameter for prelaunch and on-orbit radiometric calibration, can be derived from tests [8,9,10,11,12,13].
The prelaunch calibration coefficients are derived from prelaunch calibration measurements, and they provide a reference for on-orbit radiometric performance. Taking the slight changes in thermal conditions into account, the calibration coefficients are updated by viewing onboard blackbody and deep space on-orbit [14,15]. Whiskbroom sensor integration time is typically shorter than push broom sensor, resulting in lower sensitivity. The TIS integration time is less than 300 μs, whereas TIRS is 3.4 ms [16]. TIS utilizes the time delay integration (TDI) method to provide improved sensor sensitivity. In prelaunch calibration tests, it shows that NEdT (Noise equivalent temperature difference) is less than 0.08 K@300 K.
In this paper, we provide a brief overview of TIS design as well as a review of the main prelaunch tests and performance results. Each section is laid out as follows: Section 2 gives descriptions of instrument design and its basic parameters. The prelaunch radiometric calibration and noise performance are presented in Section 3. On-orbit calibration is discussed in Section 4 along with the influence of instrument temperatures. A summary and conclusions are presented in Section 5.

2. TIS Instrument Overview

TIS consists of an optical system, an onboard calibration source, a focal plane array, a Dewar, a cryocooler, an earth shield and a circuit box. By controlling the scan mechanism, the scan mirror switches between blackbody, earth scene and deep space, as shown in Figure 1. Since the satellite has only one view port, it must adjust its attitude in order to view deep space in orbit. Viewing deep space periodically can provide an update on the background response.
For thermal infrared bands, instrument self-emission is one of the major factors affecting sensitivity. To improve sensitivity, TIS telescope optics is controlled at approximately 195 K, and instruments temperatures are controlled by passive refrigeration. Additionally, the earth shield is also necessary to maintain the temperature.
A summary of TIS major parameters can be found in Table 2. A 260 mm four-element refractive lens assembly is used for TIS optical system. By controlling the scan mechanism, the field of view can achieve about 33.1° to accomplish the requirements of 300 km swath. It is made of silicon and germanium elements, and the refractive is sensitive to the temperature, causing an approximate linear relationship between off-focus length and optics temperature. Due to this characteristic, the optical system can adjust the focal length by slightly adjusting the telescope optics temperature on-orbit. The maximum adjustable temperature is approximately 10 K.
To obtain a 300 km swath, the TIS uses a HgCdTe detector, which is composed of four modules. In the focal plane structure, each two modules overlap 25 photosensitive pixels in the column direction. Each module has 512 × 4 pixels, and the time-delay-integration method is used to improve the sensitivity. Three thermal infrared bands are split by an integrated filter, 8.0–10.5 µm, 10.3–11.3 µm and 11.5–12.5 µm, respectively. By using dumb pixels that are not masked by filters, photosensitive areas on the substrate are isolated from optical crosstalk between adjacent bands. The layout of the focal plane array is shown in Figure 2.

3. TIS Prelaunch Test and Characterization

The onboard blackbody is the primary calibration source for estimating radiometric performance. It is a China National Institute of Metrology blackbody with an effective emissivity of 0.99. By rotating the scan mirror 90°, the field is switched from blackbody to zero-aperture radiance in a prelaunch test.
On-orbit, the blackbody is usually controlled at a constant low temperature, which is helpful to maintain the low telescope optics temperature. During the blackbody warm-up and cool-down stage, several temperatures are controlled to check its radiometric requirements and calculate calibration coefficients.
The equivalent temperature of deep space is 4 K, and the output when TIS views deep space is called background response. It contains the instrument self-emission and dark current. According to the Planck function, instruments such as telescope optics and Dewar emit photons that reach the focal plane array when their temperatures are greater than 0 Kelvin. On-orbit instrument temperatures are strictly controlled to maintain the instrument self-emission stability, which is necessary for the radiometric calibration. It should be noted that the remaining one part of background response is dark current, which is positively correlated with focal plane array temperature. When the sensor views the earth scene or onboard blackbody, the total signal can be directly divided into two parts: target response and background response.
As Figure 3 shows, the radiance passes through the scan mirror and telescope optics, and then focuses on the focal plane array. Photons that reach the focal plane arrays are transformed into electrons, and then the signal is converted to 12 bit digital number (DN) by analog-to-digital converter. A set of pre-launch tests are very important to assess the quality of the data. TIS is a whisk broom sensor, so the integration time is limited by the scan mode, making it more difficult to achieve high sensitivity compared to push broom sensor.
Before calibration measurements, a series of parameters such as integration time, integral capacitor, optics temperature and FPA temperature are tested in the pre-launch test. The optimal parameters are selected according to the test results. And these optimal parameters all contribute to better performance of TIS on-orbit.

3.1. The Noise Performance under Different FPA Temperatures

Here, we compare the system performance when the FPA is set at different temperatures. The system performance of different temperatures is compared quantitatively, especially with regard to the NEdT characterization. Normally, FPA operates at very low temperatures. It is placed inside the Dewar and chilled by a mechanical cryocooler to stable temperature. Dark current is the dominant cause of background response variations caused by changes in FPA temperature.
The FPA temperature affects both the signal and the noise. In the prelaunch test, three FPA temperatures are set: 52 K, 55 K and 60 K, in an attempt to quantify the effect of FPA temperature effect on system sensitivity.
In general, sensor noise can be divided into two types, shot noise and detector intrinsic noise. Shot noise is caused by the random generation of electrons. It comes from the dark current and photon electrons and follows Poisson statistics. Detector internal noise is mainly created by Analog-to Digital Converter (ADC) noise and read noise. Therefore, as the FPA temperature rises, the noise variation is mainly derived from dark current noise.
As seen in Figure 4 and Figure 5, considerable variations in output DN and noise might be found for the variations of FPA temperature. The output and noise descent rate from FPA 60 K to 55 K is faster than the changes from 55 K to 52 K. When the TIS views deep space, the readout noise accounts for most of the noise. A large part of noise is accounted for by readout noise as the dark current decreases. Consequently, noise decreases when FPA temperature changes from 55 K to 52 K. The noise increases by 15%, 34% and 47%, whereas the background response increases about 41%, 47% and 42% compared with 52 K for the three bands when the FPA temperature is 60 K.
When the FPA temperature is lower, the effective response DN range (the maximum DN subtracted background response DN) is lager and the noise is lower. These two factors both directly influence the NEdT. The FPA temperature is set at approximately 52 K based on the refrigerator power consumption and noise performance at different FPA temperatures.

3.2. Prelaunch Test and Performance

After FPA temperature is selected, the system radiometric calibration test is carried out in the thermal vacuum chamber. The linearity deviation between produced electrons and output influences the radiometric calibration calculation methods. Before the radiometric calibration measurements, the integration time is set as 100 μs, 200 μs, 300 μs, 400 μs, 500 μs, 700 μs, 800 μs and 1000 μs to assess the linear relationship between the integration time and output.
As the integration time increases, the number of electrons produced by the dark current and instrument self-emission increases, and the detector output also increases. As shown in Figure 6, the R-square is more than 0.999, indicating good linearity of integration time and digital number. This also explains the linear relationship between electrons and output.
(1)
Radiometric calibration
In the prelaunch test, the onboard blackbody and cooling panel are the calibration sources. The blackbody temperature varies from 240 K to 300 K. In a complete data collection process, by rotating the angle of scan mirror, the view is switched to the blackbody and then to the cooling panel. Since the time between two observations is very short, it is possible to ignore the effect of background variation caused by variations in instrument temperature.
Radiometric calibration establishes the relationship between spectral radiance and response output of the instrument. An important parameter for radiometric calibration is the spectral response of instrument. The spectral response is tested in prelaunch experiment. The normalized relative spectral response of TIS is shown in Figure 7.
To calculate the calibration coefficients, we first we calculate the spectral radiance with the relative spectral response and Planck equation, as shown in Equation (1), and then we subtracted the background by Equation (2). Finally, the calibration coefficients are expressed in Equation (3).
L B B = ε 2 h c 2 λ 5 1 e h c / λ k T 1 R S R ( λ ) d λ R S R ( λ ) d λ
Δ D N = D N D N b a c k g r o u n d
L B B = a Δ D N + b
where LBB is the blackbody spectral radiance, ԑ is onboard blackbody emissivity, DN is the average output when it views blackbody, DNbackground is the average output when it observes cooling panel and ΔDN is the blackbody response subtracted background response. a and b are the linear coefficient and bias coefficient obtained by the least squares method, respectively.
As shown in Figure 8, there is good linearity between the radiance and DN. The goodness of fit is more than 0.999 for each band. The fitting residuals can also evaluate the fitting results. The equation is shown as follows:
T = k 2 ln ( k 1 / L λ + 1 )
Δ T = T b b T
where L λ is spectral radiance calculated using the fitting coefficients and DN. k1 and k2 are the coefficients to derive brightness temperature from radiance, and they are obtained by least squares algorithm. Tbb is measured blackbody temperature, T′ is the calculated blackbody temperature using calibration coefficients a, b, k1 and k2. ΔT is the fitting residuals compared with measured temperature.
The fitting residuals are shown in Figure 9, and the maximum residual is less than 0.5 K for the three bands. It is evident that band 1 and band 2 show better fitting results, in which the fitting errors are all within ±0.25 K at every measured temperature.
(2)
Noise performance
Noise both reduces the image quality and calibration accuracy. To evaluate the sensitivity of thermal infrared bands, we often use NEdT at nominal BB temperatures (300 K). The NEdT calculation equation is shown as below:
n o i s e = 1 N i = 1 N ( D N i D N ¯ ) 2
N E d T = L / ( D N ¯ / n o i s e ) d L / d T
where N is the total number of scan lines, DNi is the output of single frame when observing specific temperature blackbody, D N ¯ is mean of DNi, noise is the standard deviation of output (all scan lines), L is the target spectral radiance, dL/dT is the derivative of the Planck function at specific temperature blackbody. The NEdT at 300 K is shown in the Table 3.

4. On-Orbit Calibration Procedure

On-orbit, deep space can only be viewed every two weeks due to the satellite’s system structure. As shown in Equation (2), instrument self-emission and dark current should be removed to characterize the target radiance. The variation of instrument temperature during the deep space observation interval introduces an error in the background subtracted from the target signal as shown in Equation (2). Thus, this error is introduced into the radiance calculated from the target signal.
Figure 10 illustrates the calibration procedure on-orbit. The calibration procedure can be divided into two parts. In the first stage, to monitor the stability of response and instrument self-emission, the onboard blackbody is controlled at 250 K and viewed before and after earth scene acquisition. The equivalent temperature of deep space is so low that the photons emitted from deep space can be ignored. When it views the 250 K blackbody, the instrument self-emission and dark current contribute to the signal as well as the blackbody. As a result, the variation of output may be caused by changes in the sensor’s response rate and instrument temperature. By comparing the blackbody response before and after earth observation, the short-term background response fluctuation is monitored.
In the second stage, despite viewing the blackbody before every or after earth observation, a complete blackbody calibration procedure is adopted every two weeks on orbit. The blackbody is controlled at several temperatures and the background signal is measured by viewing deep space after blackbody observation. This procedure is used to check its radiometric performance and noise. Calibration coefficients can be updated every two weeks. Combined with long term data collected from these two stages, both the background response and response stability of detector can be evaluated.
The instrument self-emission for thermal infrared bands is part of the signal which can’t be ignored. The output DN and its radiometric performance fluctuate with the instrument’s temperature. The stability of instrument temperatures is necessary to reduce the calibration error caused by instrument self-emission change. In prelaunch stage, we collect the background response under different instruments temperatures. We slightly change the main instruments temperatures by heater in the thermal vacuum chamber, whereas the blackbody temperature is stable. Figure 11 shows the fluctuation of DN, telescope temperature, Dewar window temperature and scan mirror temperature during 12 h, respectively.
As illustrated in Figure 11, the telescope temperature fluctuates the most, and the maximum temperature change of the telescope is 2.71 K. When instruments temperatures fluctuate, DN and telescope temperature tend to follow similar trends. As for the background response variation, the DN maximum variation is approximately 60, 76 and 80 for the three bands. The error corresponding to the variation of background response is larger than the calibration accuracy requirement (1 K) if the background response is not corrected. Therefore, the stability of instrument temperature is necessary on-orbit.

5. Conclusions

SDGSAT-1 TIS is a thermal infrared instrument with high spatial resolution and wide width. The paper describes the design, methodologies and pre-launch radiometric performance of TIS, including radiometric calibration, NEdT and linearity.
A discussion of the relationship between noise performance and FPA temperature precedes the radiometric calibration test. Considering consumption and noise performance, the FPA temperature is set to 52 K on-orbit. The pre-launch radiometric calibration results show that the TIS has a good performance. The linearity between spectral radiance and DN is good and the fitting residuals are less than 0.5 K for the measured blackbody temperatures. For noise performance, NEdT is 0.034 K, 0.047 K and 0.076 K for the three bands. We also discuss the performance when the instruments temperatures fluctuate slightly. The noise performance is more susceptible to FPA temperature than telescope optics temperature. Given the on-orbit calibration procedure, the instrument temperatures must be strictly controlled during one orbit to keep background response stable.
After launch, the stability of radiometric performance should be monitored to ensure that the remote sensing data meets the requirements throughout the lifetime of the instrument. In future studies, it will be important to examine how best to correct relative differences in radiance among four modules. This is very critical to achieve high quality imagery and calibration accuracy.

Author Contributions

Conceptualization, Z.H. and M.Z.; methodology, Z.H.; validation, Q.W.; investigation, X.S.; resources, F.C.; data curation, F.C.; writing—original draft preparation, Z.H.; writing—review and editing, Z.H.; visualization, Q.W. and M.Z., supervision, Z.H.; project administration, F.C.; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research is funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDA19010102, and the National Natural Science Foundation of China under grant number 61975222.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank the SDG BIG DATA Center and National Space Science Center for providing us with data.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rajendran, S.; Al-Kuwari, H.A.-S.; Sadooni, F.N.; Nasir, S.; Govil, H. Remote sensing of inland Sabkha and a study of the salinity and temporal stability for sustainable development: A case study from the West coast of Qatar. Sci. Total Environ. 2021, 782, 146932. [Google Scholar] [CrossRef]
  2. Jimenez-Munoz, J.C.; Cristobal, J.; Sobrino, J.A.; Soria, G.; Ninyerola, M.; Pons, X. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval from Landsat Thermal-Infrared Data. IEEE Trans. Geosci. Remote Sens. 2009, 47, 339–349. [Google Scholar] [CrossRef]
  3. Weng, Q. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS J. Photogramm. Remote Sens. 2009, 64, 335–344. [Google Scholar] [CrossRef]
  4. Qin, Z.; Karnieli, A.; Berliner, P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. Int. J. Remote Sens. 2001, 22, 3719–3746. [Google Scholar] [CrossRef]
  5. Li, Z.-L.; Tang, B.-H.; Wu, H.; Ren, H.; Yan, G.; Wan, Z.; Trigo, I.F.; Sobrino, J.A. Satellite-derived land surface temperature: Current status and perspectives. Remote Sens. Environ. 2013, 131, 14–37. [Google Scholar] [CrossRef]
  6. Rozenstein, O.; Qin, Z.; Derimian, Y.; Karnieli, A. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm. Sensors 2014, 14, 5768–5780. [Google Scholar] [CrossRef] [PubMed]
  7. Jiménez-Muñoz, J.C.; Sobrino, J.A.; Skokovic, D.; Mattar, C.; Cristóbal, J. Land surface temperature retrieval methods from Landsat-8 Thermal Infrared Sensor data. IEEE Geosci. Remote Sens. Lett. 2014, 11, 1840–1843. [Google Scholar] [CrossRef]
  8. Liu, W.; Li, J.; Zhang, Y.; Zhao, L.; Cheng, Q. Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability. Remote Sens. 2021, 13, 3242. [Google Scholar] [CrossRef]
  9. Pearlman, A.; Montanaro, M.; Efremova, B.; McCorkel, J.; Wenny, B.; Lunsford, A.; Reuter, D. Prelaunch Radiometric Calibration and Uncertainty Analysis of Landsat Thermal Infrared Sensor 2. IEEE Trans. Geosci. Remote Sens. 2020, 59, 2715–2726. [Google Scholar] [CrossRef]
  10. Xu, N.; Niu, X.; Hu, X.; Wang, X.; Wu, R.; Chen, S.; Chen, L.; Sun, L.; Ding, L.; Yang, Z.; et al. Prelaunch calibration and radiometric performance of the advanced MERSI II on FengYun-3D. IEEE Trans. Geosci. Remote Sens. 2018, 56, 4866–4875. [Google Scholar] [CrossRef]
  11. Smith, D.; Barillot, M.; Bianchi, S.; Brandani, F.; Coppo, P.; Etxaluze, M.; Frerick, J.; Kirschstein, S.; Lee, A.; Maddison, B.; et al. Sentinel-3A/B SLSTR pre-launch calibration of the thermal infrared channels. Remote Sens. 2020, 12, 2510. [Google Scholar] [CrossRef]
  12. Schwarting, T.; Mcintire, J.; Oudrari, H.; Xiong, X. JPSS-1/NOAA-20 VIIRS day-night band prelaunch radiometric calibration and performance. IEEE Trans. Geosci. Remote Sens. 2019, 57, 7534–7546. [Google Scholar] [CrossRef]
  13. Pearlman, A.J.; McCorkel, J.; Montanaro, M.; Efremova, B.; Wenny, B.; Lunsford, A.; Simon, A.; Hair, J.; Reuter, D. Landsat 9 Thermal Infrared Sensor 2 Pre-Launch Characterization: Initial Imaging and Spectral Performance Results. In Earth Observing Systems XXIII, Proceedings of the SPIE Optical Engineering + Applications, San Diego, CA, USA, 19–23 August 2018; SPIE: Bellingham, WA, USA, 2018. [Google Scholar]
  14. Pearlman, A.; Efremova, B.; Montanaro, M.; Lunsford, A.; Reuter, D.; McCorkel, J. Landsat 9 Thermal Infrared Sensor 2 On-Orbit Calibration and Initial Performance. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1002608. [Google Scholar] [CrossRef]
  15. Barsi, J.A.; Markham, B.L.; Montanaro, M.; Hook, S.; Raqueño, N.; Miller, J.A.; Willette, R. Landsat-8 TIRS thermal radiometric calibration status. In Earth Observing Systems XXV, Proceedings of the SPIE Optical Engineering + Applications, Virtual, 24 August–4 September 2020; SPIE: Bellingham, WA, USA, 2020; Volume 11501. [Google Scholar]
  16. Montanaro, M.; Lunsford, A.; Tesfaye, Z.; Wenny, B.; Reuter, D. Radiometric calibration methodology of the Landsat 8 thermal infrared sensor. Remote Sens. 2014, 6, 8803–8821. [Google Scholar] [CrossRef] [Green Version]
Figure 1. SDGSAT-1 TIS structure diagram.
Figure 1. SDGSAT-1 TIS structure diagram.
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Figure 2. Three bands and four modules locations on the focal plane layout. Each pixel is 30 µm × 30 µm. The 26 pixels between two bands are not used to eliminate the crosstalk. The adjacent modules overlap 25 pixels.
Figure 2. Three bands and four modules locations on the focal plane layout. Each pixel is 30 µm × 30 µm. The 26 pixels between two bands are not used to eliminate the crosstalk. The adjacent modules overlap 25 pixels.
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Figure 3. Structure diagram of calibration.
Figure 3. Structure diagram of calibration.
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Figure 4. Output DN at different FPA temperature.
Figure 4. Output DN at different FPA temperature.
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Figure 5. Normalized noise performance at different FPA temperature.
Figure 5. Normalized noise performance at different FPA temperature.
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Figure 6. Digital number changes with the integration time.
Figure 6. Digital number changes with the integration time.
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Figure 7. The normalized relative spectral response curve for three bands.
Figure 7. The normalized relative spectral response curve for three bands.
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Figure 8. Prelaunch calibration curve at measured blackbody temperature. The subtracted background DN (x axis) versus spectral radiance (y axis). The fitting results are illustrated as dashed lines and the measured values are illustrated as solid lines. B1: 8.0~10.5 μm, B2: 10.3~11.3 μm and B3: 11.5~12.5 μm.
Figure 8. Prelaunch calibration curve at measured blackbody temperature. The subtracted background DN (x axis) versus spectral radiance (y axis). The fitting results are illustrated as dashed lines and the measured values are illustrated as solid lines. B1: 8.0~10.5 μm, B2: 10.3~11.3 μm and B3: 11.5~12.5 μm.
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Figure 9. Fitting residuals for measured blackbody temperatures in the prelaunch calibration test.
Figure 9. Fitting residuals for measured blackbody temperatures in the prelaunch calibration test.
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Figure 10. SDGSAT-1 TIS calibration procedure flow chart on-orbit.
Figure 10. SDGSAT-1 TIS calibration procedure flow chart on-orbit.
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Figure 11. Variations of background digital number versus time. Background response digital number (Upper left), Telescope temperature optics temperature (Upper right), Dewar window temperature (Lower left) and Scan mirror temperature (Lower right).
Figure 11. Variations of background digital number versus time. Background response digital number (Upper left), Telescope temperature optics temperature (Upper right), Dewar window temperature (Lower left) and Scan mirror temperature (Lower right).
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Table 1. Summary of thermal infrared sensor parameters (long wave band).
Table 1. Summary of thermal infrared sensor parameters (long wave band).
ParameterMODIS (Terra)TIRS-2 (Landsat 9)TIRS
(Landsat 8)
VIMS
(GF-5)
TIS
(SDGSAT-1)
Launch dateDecember 1999September 2021February 2013May 2018November 2021
Orbital altitude (km)702705705705505
Swath (km)233018518560300
Ground resolution (km)10.10.10.040.03
Table 2. SDGSAT-1 TIS major parameters.
Table 2. SDGSAT-1 TIS major parameters.
ParameterPerformance
Swath300 km
Spatial resolution30 m
Wavelength8.0–10.5 μm
10.3–11.3 μm
11.5–12.5 μm
F-number 1.94
Optical aperture260 mm
Scanning FOV≥33.1°
Quantization bit12 bit
Table 3. SDGSAT-1 TIS prelaunch noise characterization at 300 K.
Table 3. SDGSAT-1 TIS prelaunch noise characterization at 300 K.
BandB1
(8.0–10.5 μm)
B2
(10.3–11.3 μm)
B3
(11.5–12.5 μm)
NEdT (K)0.0340.047 0.076
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Hu, Z.; Zhu, M.; Wang, Q.; Su, X.; Chen, F. SDGSAT-1 TIS Prelaunch Radiometric Calibration and Performance. Remote Sens. 2022, 14, 4543. https://doi.org/10.3390/rs14184543

AMA Style

Hu Z, Zhu M, Wang Q, Su X, Chen F. SDGSAT-1 TIS Prelaunch Radiometric Calibration and Performance. Remote Sensing. 2022; 14(18):4543. https://doi.org/10.3390/rs14184543

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Hu, Zhuoyue, Min Zhu, Qiyao Wang, Xiaofeng Su, and Fansheng Chen. 2022. "SDGSAT-1 TIS Prelaunch Radiometric Calibration and Performance" Remote Sensing 14, no. 18: 4543. https://doi.org/10.3390/rs14184543

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