An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

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Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

Remote Sensing ◽

10.3390/rs13163246 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3246

Author(s):

Yanqing Xie ◽

Zhengqiang Li ◽

Weizhen Hou ◽

Jie Guang ◽

Yan Ma ◽

...

Keyword(s):

Water Vapor ◽

Near Infrared ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

China Meteorological Administration ◽

Medium Resolution ◽

The One ◽

Spectral Imager ◽

Meteorological Satellite

The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm2), whereas the systematic error of the P940 dataset (−0.02 g/cm2) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within ±(0.05+0.10∗PWVAERONET) (PER10) of the P940 PWV dataset are 0.24 g/cm2, 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm2, 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm2), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed.

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Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2932847 ◽

2019 ◽

Vol 57 (12) ◽

pp. 10252-10262 ◽

Cited By ~ 2

Author(s):

Jia He ◽

Zhizhao Liu

Keyword(s):

Remote Sensing ◽

Water Vapor ◽

Near Infrared ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Infrared Remote Sensing

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Global evaluation of the precipitable-water-vapor product from MERSI-II (Medium Resolution Spectral Imager) on board the Fengyun-3D satellite

Atmospheric Measurement Techniques ◽

10.5194/amt-14-7821-2021 ◽

2021 ◽

Vol 14 (12) ◽

pp. 7821-7834

Author(s):

Wengang Zhang ◽

Ling Wang ◽

Yang Yu ◽

Guirong Xu ◽

Xiuqing Hu ◽

...

Keyword(s):

Water Vapor ◽

Hydrological Cycle ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Spatial Distance ◽

The Black Sea ◽

Radiation Balance ◽

Global Evaluation ◽

Medium Resolution ◽

Spectral Imager

Abstract. Atmospheric water vapor plays a key role in Earth's radiation balance and hydrological cycle, and the precipitable-water-vapor (PWV) product under clear-sky conditions has been routinely provided by the advanced Medium Resolution Spectral Imager (MERSI-II) on board Fengyun-3D since 2018. The global evaluation of the PWV product derived from MERSI-II is performed herein by comparing it with PWV from the Integrated Global Radiosonde Archive (IGRA) based on a total of 462 sites (57 219 matchups) during 2018–2021. The monthly averaged PWV from MERSI-II presents a decreasing distribution of PWV from the tropics to the polar regions. In general, a sound consistency exists between PWV values of MERSI-II and IGRA; their correlation coefficient is 0.951, and their root mean squared error (RMSE) is 0.36 cm. The histogram of mean bias (MB) shows that the MB is concentrated around zero and mostly located within the range from −1.00 cm to 0.50 cm. For most sites, PWV is underestimated with the MB between −0.41 and 0.05 cm. However, there is also an overestimated PWV, which is mostly distributed in the area surrounding the Black Sea and the middle of South America. There is a slight underestimation of MERSI-II PWV for all seasons with the MB value below −0.18 cm, with the bias being the largest magnitude in summer. This is probably due to the presence of thin clouds, which weaken the radiation signal observed by the satellite. We also find that there is a larger bias in the Southern Hemisphere, with a large value and significant variation in PWV. The binned error analysis revealed that the MB and RMSE increased with the increasing value of PWV, but there is an overestimation for PWV smaller than 1.0 cm. In addition, there is a higher MB and RMSE with a larger spatial distance between the footprint of the satellite and the IGRA station, and the RMSE ranged from 0.33 to 0.47 cm. There is a notable dependency on solar zenith angle of the deviations between MERSI-II and IGRA PWV products.

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