Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations

• An all-weather PWV retrieval algorithm that synergizes near-infrared and thermal infrared observations is proposed. • The new algorithm is efficient as it only requires Sentinel-3B observations. • The first traceable, observation-based PWV data with a 300 m resolution is generated. • The new algorithm notably improves the accuracy and spatial continuity of Sentinel-3B water vapor data. Near-infrared (NIR) and thermal infrared (TIR) remote sensing are primary methods for monitoring large-scale, high-resolution precipitable water vapor (PWV); however, their application is limited to clear-sky conditions. To overcome this limitation and enable all-weather PWV retrieval, this study develops a new satellite-based PWV retrieval model to derive all-weather PWV at a spatial resolution of 300 m based on the synergistic use of NIR and TIR data from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard the Sentinel-3B satellite. The proposed model consists of two sub-models for PWV derivation: one each for clear- and cloudy-sky conditions. The clear-sky PWV retrieval model integrates water vapor information from both NIR and TIR observations, whereas the cloudy-sky PWV retrieval model uses cloud-top properties derived from TIR data to correct the NIR water vapor within or above the clouds to the ground. Tested against the GNSS PWV, the new model has a root-mean-square error (RMSE) of 1.01 mm and bias of 0.09 mm for clear-sky conditions, representing a 51 % improvement in accuracy over the operational OLCI NIR products. For cloudy-sky conditions, the RMSE and bias are 2.66 mm and 0.02 mm, respectively, thereby filling the gap in high-quality PWV retrieval products under cloudy-sky conditions. The combined all-weather PWV product demonstrates robust spatiotemporal performance, with an RMSE and Bias of 1.92 mm and 0.16 mm, respectively. Given that the GNSS PWV has an accuracy of 1.37 mm, the accuracy achieved in this study is satisfactory.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-06-27
DOI
https://doi.org/10.1016/j.jag.2025.104699
Citations
1
Primary Topic
Spectroscopy and Laser Applications
Type
article
Field-Weighted Citation Impact
0.76
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article

Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations

Quanyu Chen, Yibin Yao, Chaoqian Xu, Qi Zhang et al.
1 citations
International Journal of Applied Earth Observation and Geoinformation
Spectroscopy and Laser Applications
0.76
article

Retrieving all-weather precipitable water vapor using near-infrared and thermal infrared observations

Quanyu Chen, Yibin Yao, Chaoqian Xu, Qi Zhang, Hongmin Li, Zhao Qingzhi, Zheng Du, Bao Zhang
article en
1 citations

Abstract

• An all-weather PWV retrieval algorithm that synergizes near-infrared and thermal infrared observations is proposed. • The new algorithm is efficient as it only requires Sentinel-3B observations. • The first traceable, observation-based PWV data with a 300 m resolution is generated. • The new algorithm notably improves the accuracy and spatial continuity of Sentinel-3B water vapor data. Near-infrared (NIR) and thermal infrared (TIR) remote sensing are primary methods for monitoring large-scale, high-resolution precipitable water vapor (PWV); however, their application is limited to clear-sky conditions. To overcome this limitation and enable all-weather PWV retrieval, this study develops a new satellite-based PWV retrieval model to derive all-weather PWV at a spatial resolution of 300 m based on the synergistic use of NIR and TIR data from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) instruments onboard the Sentinel-3B satellite. The proposed model consists of two sub-models for PWV derivation: one each for clear- and cloudy-sky conditions. The clear-sky PWV retrieval model integrates water vapor information from both NIR and TIR observations, whereas the cloudy-sky PWV retrieval model uses cloud-top properties derived from TIR data to correct the NIR water vapor within or above the clouds to the ground. Tested against the GNSS PWV, the new model has a root-mean-square error (RMSE) of 1.01 mm and bias of 0.09 mm for clear-sky conditions, representing a 51 % improvement in accuracy over the operational OLCI NIR products. For cloudy-sky conditions, the RMSE and bias are 2.66 mm and 0.02 mm, respectively, thereby filling the gap in high-quality PWV retrieval products under cloudy-sky conditions. The combined all-weather PWV product demonstrates robust spatiotemporal performance, with an RMSE and Bias of 1.92 mm and 0.16 mm, respectively. Given that the GNSS PWV has an accuracy of 1.37 mm, the accuracy achieved in this study is satisfactory.

International Journal of Applied Earth Observation and GeoinformationVol. 142
Xi'an University of Science and Technology (CN), Wuhan University (CN)
Openalex Percentile: Top 29%
Spectroscopy and Laser Applications
0.76
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