Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability

Wanyue Liu,Jiaguo Li,Limin Zhao,Ying Zhang,Qiuming Cheng

doi:10.3390/rs13163242

Abstract

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

Highlights

Land surface temperature (LST) is a critical and reliable land surface feature parameter used to estimate land surface physical processes
Of the thermal Infrared Spectrometer (TIS) sensor and LST was estimated with noised top of atmosphere (TOA) brightness temperatures
Background subtraction was first performed followed by the conversion to radiance, and linear regression was performed to obtain the calibration coefficients

Summary

Introduction

Land surface temperature (LST) is a critical and reliable land surface feature parameter used to estimate land surface physical processes. Many other land surface parameters, such as evapotranspiration modeling [1] and soil moisture [2], rely on the prior knowledge of LST. TIR data provide a simultaneous and large-scale view of land surfaces [3] and LST can be retrieving from TIR imagery. 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]

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Discussion

Conclusion