author：Nan Jiang, Yan Xu, Tianhe Xu*, Song Li, and Zhaorui Gao

来源出版物：IEEE Transactions on Geoscience and Remote Sensing 文献号：https://doi.org/10.1109/TGRS.2022.3162222 出版年：March 2022

abstract：In precipitable water vapor (PWV) retrieval, results with high spatial coverage but low temporal resolution can be achieved through satellite-borne sensors, such as the Advanced Microwave Scanning Radiometer 2 (AMSR2). Conversely, the ground-based global navigation satellite system (GNSS) can provide PWV with high temporal resolution and high precision but low spatial coverage. To combine the advantages of these two technologies, we introduce a back propagation neural network (BPNN) to realize PWV retrieval from AMSR2 with ground-based GNSS data. We first detect the optimal configuration for the BPNN. Then, based on the results of the retrieval accuracy from different types of orbits, we find that the decreasing (De) orbit has the highest retrieval accuracy, with a root mean square error (RMSE) of 3.25 mm. Afterward, the influence of brightness temperature (Tb) data at different frequencies on PWV retrieval is analyzed. The results of GNSS-PWV verification indicate that the 18+23 GHz frequency combination has the highest PWV retrieval accuracy, and the mean RMSE of all 82 test stations distributed globally can reach 3.53 mm. We also analyze the influence of differently located stations on retrieval accuracy, and the results show that the accuracy of high-latitude and polar regions is remarkably higher than that of other areas but with a lower relative error. Finally, we use radiosonde data as another external verification method to assess PWV retrieval accuracy. The results reveal that RMSE can reach 3.87 mm. Through a BPNN approach, we have creatively realized PWV retrieval from AMSR2 using ground-based GNSS data on a global scale.

keywords：Precipitable Water Vapor (PWV), Combined PWV retrieving, Satellite-borne brightness temperature data, Ground-based GNSS, Advanced Microwave Scanning Radiometer 2 (AMSR2)

citations：Nan Jiang, Yan Xu, Tianhe Xu*, Song Li, and Zhaorui Gao. Land Water Vapor Retrieval for AMSR2 using a Deep Learning Method. IEEE Transactions on Geoscience and Remote Sensing, 2022.