Satellite Retrieval of LiDAR Attenuation Coefficient From ICESat-2 and Sentinel-3 Based on Machine Learning: Inland Waters

LiDAR diffuse attenuation coefficient ( {K}_{\text {lidar}} ) describes the laser attenuation degree in water, and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is expected to extend the observation of this parameter to satellites. However, the ICESat-2 observation is limited to the satellit...

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Veröffentlicht in:IEEE geoscience and remote sensing letters 2024, Vol.21, p.1-5
Hauptverfasser: Chen, Li, Zhang, Guoping, Xing, Shuai, Wang, Zheng, Kong, Ruiyao, Xu, Qing
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Xu, Qing
description LiDAR diffuse attenuation coefficient ( {K}_{\text {lidar}} ) describes the laser attenuation degree in water, and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is expected to extend the observation of this parameter to satellites. However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve K_{\text {lidar}} of inland waters. First, K_{\text {lidar}} is retrieved from ICESat-2 water column photons. Second, the retrieved K_{\text {lidar}} and Sentinel-3 images are used to train machine learning models to generate K_{\text {lidar}} covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. The results show that the performance of CatBoost is better than that of random forest (RF) and XGBoost, and the consistency reaches 68.42%. Compared with in situ data, the root mean square error (RMSE) of the proposed method is 0.0555~\text {m}^{-1} , which is 61.05% higher than the RMSE of the Sentinel-3 product. By combining laser data with multispectral images, water quality observation close to field measurement can be retrieved from satellite platforms.
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However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> of inland waters. First, <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> is retrieved from ICESat-2 water column photons. Second, the retrieved <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> and Sentinel-3 images are used to train machine learning models to generate <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. The results show that the performance of CatBoost is better than that of random forest (RF) and XGBoost, and the consistency reaches 68.42%. Compared with in situ data, the root mean square error (RMSE) of the proposed method is <inline-formula> <tex-math notation="LaTeX">0.0555~\text {m}^{-1} </tex-math></inline-formula>, which is 61.05% higher than the RMSE of the Sentinel-3 product. 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However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> of inland waters. First, <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> is retrieved from ICESat-2 water column photons. Second, the retrieved <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> and Sentinel-3 images are used to train machine learning models to generate <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. 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However, the ICESat-2 observation is limited to the satellite trajectories, and it is hard to realize the coverage observation of inland water. Therefore, this study proposes a method fusing active and passive remote sensing data based on machine learning and uses ICESat-2 data and Sentinel-3 images to retrieve <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> of inland waters. First, <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> is retrieved from ICESat-2 water column photons. Second, the retrieved <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> and Sentinel-3 images are used to train machine learning models to generate <inline-formula> <tex-math notation="LaTeX">K_{\text {lidar}} </tex-math></inline-formula> covering water bodies. Experiments were carried out in Xiaolangdi Reservoir using multitemporal data. The results show that the performance of CatBoost is better than that of random forest (RF) and XGBoost, and the consistency reaches 68.42%. Compared with in situ data, the root mean square error (RMSE) of the proposed method is <inline-formula> <tex-math notation="LaTeX">0.0555~\text {m}^{-1} </tex-math></inline-formula>, which is 61.05% higher than the RMSE of the Sentinel-3 product. By combining laser data with multispectral images, water quality observation close to field measurement can be retrieved from satellite platforms.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2024.3436833</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-4063-0114</orcidid><orcidid>https://orcid.org/0000-0003-2505-7188</orcidid></addata></record>
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subjects and Land Elevation Satellite-2 (ICESat-2)
Attenuation
Attenuation coefficients
CatBoost
Cloud
Extinction coefficient
Ice
Image quality
Inland waters
Laser radar
Lasers
Learning algorithms
Lidar
LiDAR diffuse attenuation coefficient
Machine learning
Observational learning
Photonics
Photons
Remote sensing
Reservoirs
Root-mean-square errors
Satellite imagery
Satellite observation
Satellites
Sea measurements
Sentienl-3
Water circulation
Water column
Water quality
title Satellite Retrieval of LiDAR Attenuation Coefficient From ICESat-2 and Sentinel-3 Based on Machine Learning: Inland Waters
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