Shortwave infrared CH4 spaceborne detection technology: providing scientific and technological support for China's low-carbon and sustainable development strategy.

The Sixth Integrated Assessment Report released by the Intergovernmental Panel on Climate Change (IPCC) in March 2023 indicated that global surface temperature from 2011 to 2020 was 1.1°C higher than pre-industrial levels (1850–1900). Greenhouse gas emissions from human activities such as fossil fuel combustion and land use have undeniably contributed to global warming. In recent years, extreme weather and climate events such as high temperatures, droughts, and torrential rains have become more frequent and intense, posing serious challenges to the sustainable development of future human society. Accurately monitoring greenhouse gas concentrations, sources, and trends is fundamental to greenhouse gas emission statistics and accounting. Compared with traditional ground-based monitoring methods, satellite remote sensing offers unique advantages such as high resolution, wide coverage, short revisit periods, and continuous dynamic data, providing high-precision, fundamental data support for global, national, and regional greenhouse gas monitoring.

△Global anthropogenic methane emissions and the share of methane emissions from major countries, 1970–2021

Shortwave infrared CH4 spaceborne detection technology is developing rapidly.

To obtain information on the spatiotemporal distribution and variation of near-surface CH4 concentration, shortwave infrared CH4 spaceborne detection technology has developed rapidly in recent years. Multiple shortwave infrared detection satellites have been launched both domestically and internationally, conducting a series of CH4 detection and related application studies, achieving space-based CH4 detection. The scientific experimental satellites launched during this period have promoted the development of CH4 satellite remote sensing inversion algorithms and flux inversion algorithms, laying a technological foundation for improving space detection capabilities, producing data products, and directly applying them. Furthermore, they have demonstrated the application potential of shortwave infrared detection in global CH4 source-sink balance estimation from the perspectives of detection data accuracy and flux estimation.

Most existing CH4 detection satellites focus on scientific CH4 detection experiments in the shortwave infrared band, achieving a breakthrough from scratch in shortwave infrared CH4 detection. Based on these satellite detections, shortwave infrared CH4 detection technology, inversion algorithms, and flux assimilation algorithms have developed rapidly. The resulting maturity and application of these algorithms have placed higher demands on the spatiotemporal coverage and data accuracy of spaceborne CH4 detection. To meet the data needs of scientific research, several CH4 detection satellites are currently in the planning stage and will be added to the spaceborne detection fleet in the future.

Development Trends of Spaceborne CH4 Detection Systems

To meet the strategic needs of global carbon inventory and carbon neutrality, and addressing the two key scientific questions of dynamic monitoring of CH4 emissions and flux estimation at different scales, CH4 detection satellites need to provide information on CH4 concentration changes at local to regional scales, distinguishing between sources and sinks. The Global Climate Observing System proposes that specific mission objectives for greenhouse gas satellite detection should meet a spatial resolution of 5–10 km, a temporal resolution of 4 hours, and a CH4 detection accuracy of 10 ppb. Given the current technical capabilities of CH4 detection satellite missions, a single satellite using a single detection method cannot complete a global detection mission, and satellites using different detection methods have different objectives. To meet the needs of detection and research at different scales, it is necessary to construct a constellation system for coordinated detection using a combination of active and passive detection methods, and high and low Earth orbit approaches. By comprehensively applying the detection results from multiple satellites, scientific support can be provided for evaluating the effectiveness of carbon neutrality-related emission reduction measures and for global carbon inventory plans. High-precision detection results primarily rely on high-resolution and high signal-to-noise ratio spectral data. Existing research indicates that when the short-wave infrared signal-to-noise ratio is better than 300 and the spectral resolution is better than 0.1 nm, the accuracy of XCH4 data products obtained by the all-physical inversion algorithm can be controlled within 10 ppb.

Satellite network observation is currently an effective means of achieving efficient global detection. Given the different detection characteristics of various satellite types, planning the operation of each satellite to maximize its detection capabilities and achieve global spatiotemporal coverage for CH4 detection is a complex issue. Under the premise of satellite network observation, geostationary satellites are used to monitor key global regions at a continental scale, while routine detection and target-mode detection by sun-synchronous orbit satellites are used to conduct repeated detection in emission-sensitive areas. Combined with active detection by lidar, a wide-coverage and highly timely spaceborne detection network is formed, providing accurate global spatiotemporal resolution detection data for CH4 budget estimation and emission monitoring.

Development Trends of Spaceborne CH4 High-Precision Inversion Algorithms

To achieve high-precision inversion results, all-physics algorithms require multiple calculations of the atmospheric radiative transfer process, consuming significant computational time and resources. Future CH4 detection satellite missions will accumulate massive amounts of spectral data. Processing this massive data and enabling operational satellite operations necessitates improved data inversion efficiency; therefore, rapid and accurate inversion calculations are critical issues that urgently need to be addressed. Because inversion algorithms typically employ relatively simple models to simplify the calculation process and introduce model errors, the accuracy of inversion data products varies considerably under different surface and atmospheric conditions. Studies on the accuracy of XCH4 obtained from different inversion algorithms also indicate that XCH4 results for ice-covered surfaces often exhibit significant bias, which is likely related to the inversion system itself. This necessitates the updating and optimization of the surface reflectance model within the inversion system. Due to the low albedo of the ocean surface, CH4 detection over the ocean still suffers from substantial errors, causing significant uncertainty in the estimation of air-sea exchange processes and ocean CH4 flux. Current research indicates that marine CH4 emissions mainly originate from nearshore shallow waters. Obtaining accurate CH4 concentration distribution in nearshore areas is crucial for improving marine CH4 flux estimation. Therefore, further development and improvement of nearshore CH4 satellite remote sensing inversion methods are particularly necessary.

Furthermore, future high spatiotemporal resolution global CH4 detection will primarily be achieved through satellite networking. The detector performance of different satellite types may vary significantly, including spatiotemporal and spectral resolution, signal-to-noise ratio, etc. Different inversion algorithms also employ different error and uncertainty assessment schemes, limiting the cross-validation of data products and their comprehensive application in studies such as flux estimation. To meet the data application needs of spaceborne CH4 detection under the goal of carbon neutrality, it is necessary to establish a standardized data quality evaluation system to provide scientific guidance for the comprehensive application of spaceborne detection data.

CH4 flux inversion development trend

CH4 flux inversion based on data assimilation is a crucial method for validating CH4 emission inventories. It primarily utilizes atmospheric CH4 concentration data from ground-based and satellite observations, combined with global and regional atmospheric chemical transport models to estimate emissions. Existing research indicates that CH4 fluxes in tropical wetlands are stronger than before, and the melting of permafrost in high latitudes has also accelerated CH4 release; both regions provide important feedback information on climate change. However, due to the low detection efficiency of current satellites at local scales and the sparse distribution of ground-based observation stations, the amount of effective observational data for these important CH4 source regions is severely insufficient, leading to significant uncertainties in flux estimation. Combining the advantages of wide satellite coverage and high accuracy of ground-based data can provide a solid data foundation for accurate estimation of regional CH4 fluxes. Furthermore, current coarse-grid atmospheric chemical transport models exhibit significant errors in spatial scale, transport processes, and chemical mechanism simulations, resulting in substantial discrepancies between CH4 flux inversion results and actual observations, severely limiting our understanding of CH4 source-sink distribution characteristics. To meet the requirements for flux calculations covering local to national scales, atmospheric chemical transport models should establish multi-model schemes including global, mesoscale, urban, and point source scales, construct model error correction schemes, achieve accurate estimation of CH4 fluxes at different scales, and support emission inventory verification at different spatial scales.

Emission reduction in industries such as petrochemicals and oil and gas is a crucial pathway to achieving carbon neutrality. Dynamic monitoring and quantification of CH4 based on high spatiotemporal resolution spaceborne detection requires support from rapid CH4 plume identification and emission estimation methods. Currently, due to limitations in the computational efficiency of inversion algorithms, shortwave infrared detection of CH4 plumes cannot achieve rapid extraction of emission anomalies. Combining multi-band detection and neural network methods to analyze the spectral characteristics of the detection can accelerate CH4 plume identification and emission estimation to some extent. Furthermore, CH4 plume emission estimation is significantly affected by uncertainties in regional wind speed and direction. How to further refine the model and reduce the uncertainties in the model, wind field, and satellite observation data is also a critical issue that urgently needs to be addressed.

Currently, there are relatively few satellites and related research on CH4 detection in China. As a fundamental tool supporting scientific conclusions, spaceborne detection is related to the progress of achieving carbon neutrality goals and China's voice in international negotiations. Therefore, it is necessary to build spaceborne CH4 detection capabilities to achieve high spatiotemporal resolution and high precision in three-dimensional monitoring of atmospheric CH4 concentration, and to construct a satellite application system integrating "detection-data-accounting" to provide space-based technological support for China's low-carbon and sustainable development strategy.

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