Performance Evaluation of Spaceborne Integrated Path Differential Absorption Lidar for Carbon Dioxide Detection at 1572 nm

Shuaibo Wang,Dong Liu,Chonghui Cheng,Ju Ke,Sijie Chen,Weibiao Chen,Bowen Tong,Jiqiao Liu,Zhuofan Zheng

doi:10.3390/rs12162570

Abstract

As one of the most influential greenhouse gases, carbon dioxide (CO2) has a profound impact on the global climate. The spaceborne integrated path differential absorption (IPDA) lidar will be a great sensor to obtain the columnar concentration of CO2 with high precision. This paper analyzes the performance of a spaceborne IPDA lidar, which is part of the Aerosol and Carbon Detection Lidar (ACDL) developed in China. The line-by-bine radiative transfer model was used to calculate the absorption spectra of CO2 and H2O. The laser transmission process was simulated and analyzed. The sources of random and systematic errors of IPDA lidar were quantitatively analyzed. The total systematic errors are 0.589 ppm. Monthly mean global distribution of relative random errors (RREs) was mapped based on the dataset in September 2016. Afterwards, the seasonal variations of the global distribution of RREs were studied. The global distribution of pseudo satellite measurements for a 16-day orbit repeat cycle showed relatively uniform distribution over the land of the northern hemisphere. The results demonstrated that 61.24% of the global RREs were smaller than 0.25%, or about 1 ppm, while 2.76% of the results were larger than 0.75%. The statistics reveal the future performance of the spaceborne IPDA lidar.

Highlights

Global warming is mainly caused by the increasing anthropogenic emissions of greenhouse gases (GHGs)
The statistics reveal the future performance of the spaceborne integrated path differential absorption (IPDA) lidar
Passive remote sensing is the main method for global CO2 detection, such as the ground-based Total Carbon Column Observing Network (TCCON), the Greenhouse gases Observing SATellite (GOSAT), and the Orbiting Carbon Observatory-2 (OCO-2) [3,4,5]

Summary

Introduction

Global warming is mainly caused by the increasing anthropogenic emissions of greenhouse gases (GHGs). Due to insufficient understanding of the sources and sinks distribution and spatiotemporal variation characteristics of CO2, there is still a large uncertainty in modeling the interaction between carbon cycle and climate. Measurements of CO2 concentration and distribution with high-precision and accuracy can help us to better understand the global carbon cycle and build a more accurate climate change forecasting model [2]. Passive remote sensing is the main method for global CO2 detection, such as the ground-based Total Carbon Column Observing Network (TCCON), the Greenhouse gases Observing SATellite (GOSAT), and the Orbiting Carbon Observatory-2 (OCO-2) [3,4,5]. TCCON has the advantage of high measurement accuracy, which can be used as verification stations for satellite remote sensing. The active remote sensing technique, which is mainly for differential absorption lidar (DIAL), is widely accepted as an appropriate way for atmosphere CO2 detecting; DIAL suffers some limitations due to low signal-to-noise ratio (SNR) that will limit the detecting accuracy especially when applied to satellite platforms [11]

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Conclusion