Spatiotemporal Variability of Global Atmospheric Methane Observed from Two Decades of Satellite Hyperspectral Infrared Sounders

Zhou, Lihang; Warner, J. X.; Nalli, Nicholas R.; Wei, Zigang; Oh, Youmi; Liu, Xingpin; Divakarla, Murty; Pryor, Kenneth L.; Kalluri, Satya; Goldberg, Mitchell D.

doi:10.3390/rs15122992

Cited by 3 publications

(1 citation statement)

References 72 publications

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“…Among the existing thermal infrared (TIR) spaceborne instruments devoted to tropospheric remote sensing, the Infrared Atmospheric Sounding Interferometer (IASI), the Atmospheric Infrared Sounder (AIRS) and the Crosstrack Infrared Sounder (CrIS) use methane TIR absorption bands for observations [16][17][18]. Zhou et al also have analyzed the spatiotemporal distributions and changes of methane in atmosphere globally using data from these hyperspectral TIR instruments [19]. Additionally, passive remote sensing satellites, such as GOSAT, GHGSat, TROPOMI, GOSAT-2, GF-5, and Prisma also monitored methane gas [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar

Zhang,

et al. 2023

Remote Sensing

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Methane (CH4) is recognized as the second most important greenhouse gas. An accurate and precise monitoring of methane gas globally has a vital role in studying the carbon cycle and global warming. The spaceborne integrated path differential absorption (IPDA) lidar is one of the most effective payload for methane detection. The simulation and optimization of the lidar system parameters can create an important base for the development of spaceborne payloads. However, previous IPDA lidar simulations have mostly used standard atmospheric models at simulation conditions, and to the best of our knowledge, there is no literature yet which applies a wavelength optimization to the IPDA system. In this study, we have investigated the relationship between the IPDA lidar system, based on wavelength optimization, and error measurement for CH4 column-averaged concentration. By selecting the wavelengths with the lowest comprehensive error as on-line and off-line, the error has been minimized by 10 ppb approximately relative to before optimization. We have proposed an IPDA simulation model at real atmospheric conditions, combining with ERA-5 reanalysis data, to simulate methane concentration globally, and present the distribution of errors. Finally, after the optimization of the lidar system parameters, we have ensured that the maximum inversion error for CH4 measurement is less than 10 ppb, to provide a reference for designing spaceborne IPDA lidar systems for high-precision CH4 column-averaged concentration detection.

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Section: Introductionmentioning

confidence: 99%

Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar

Zhang,

et al. 2023

Remote Sensing

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Optical design and development of an underwater dual-channel microlens array integral field snapshot hyperspectral imager

Lu,

Ma,

et al. 2024

Appl. Opt.

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Compared to push-scan hyperspectral imagers, snapshot hyperspectral imagers offer an advantage by minimizing sensitivity to attitude jitter in underwater mobile platforms. Here we present the optical design and development of an underwater microlens array integral field hyperspectral imager. The system comprises a panchromatic imaging channel with a high spatial resolution and a spectral imaging channel with a lower spatial resolution. Through the fusion of high-resolution panchromatic images and low-resolution spectral images, we achieve high spatial resolution hyperspectral images. Both the panchromatic imaging channel and the spectral imaging channel share a common front objective, featuring a 25 mm focal length and a wide 36° field of view angle. Utilizing prism dispersion, the spectral imaging system spans a band range from 465 to 700 nm with a spectral resolution of less than 10 nm. Specialized algorithms for spectral image reconstruction and image fusion have been developed. The experimental results across diverse scenes confirm the exemplary spectral imaging performance of the system, positioning it as a robust solution for underwater snapshot hyperspectral imaging.

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Anomaly Detection of Sensor Arrays of Underwater Methane Remote Sensing by Explainable Sparse Spatio-Temporal Transformer

Zhang,

Ni,

Zhu

et al. 2024

Remote Sensing

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The increasing discovery of underwater methane leakage underscores the importance of monitoring methane emissions for environmental protection. Underwater remote sensing of methane leakage is critical and meaningful to protect the environment. The construction of sensor arrays is recognized as the most effective technique to increase the accuracy and sensitivity of underwater remote sensing of methane leakage. With the aim of improving the reliability of underwater methane remote-sensing sensor arrays, in this work, a deep learning method, specifically an explainable sparse spatio-temporal transformer, is proposed for detecting the failures of the underwater methane remote-sensing sensor arrays. The data input into the explainable sparse block could decrease the time complexity and the computational complexity (O (n)). Spatio-temporal features are extracted on various time scales by a spatio-temporal block automatically. In order to implement the data-driven early warning system, the data-driven warning return mechanism contains a warning threshold that is associated with physically disturbing information. Results show that the explainable sparse spatio-temporal transformer improves the performance of the underwater methane remote-sensing sensor array. A balanced F score (F1 score) of the model is put forward, and the anomaly accuracy is 0.92, which is superior to other reconstructed models such as convolutional_autoencoder (CAE) (0.81) and long-short term memory_autoencoder (LSTM-AE) (0.66).

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Spatiotemporal Variability of Global Atmospheric Methane Observed from Two Decades of Satellite Hyperspectral Infrared Sounders

Cited by 3 publications

References 72 publications

Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar

Simulation and Error Analysis of Methane Detection Globally Using Spaceborne IPDA Lidar

Optical design and development of an underwater dual-channel microlens array integral field snapshot hyperspectral imager

Anomaly Detection of Sensor Arrays of Underwater Methane Remote Sensing by Explainable Sparse Spatio-Temporal Transformer

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