author：Zhaorui Gao , Nan Jiang , Yan Xu , Tianhe Xu , and Yangfan Liu

来源出版物：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 文献号：https://doi.org/10.1109/TGRS.2022.3151384 出版年：February 2022

abstract：Precipitable water vapor (PWV) is an important parameter reflecting atmospheric water vapor, which plays an important role in the global hydrological cycle. At present, brightness temperatures (Tbs) at frequencies of 18 and 23 GHz from the Advanced Microwave Scanning Radiometer 2 (AMSR2) are commonly applied in PWV data retrieval. Two tools are usually employed to perform the retrieval: physical models and neural networks. In this article, we creatively deduced a more universal physical equation and verified the consistency between theoretical and experimental equations both theoretically and numerically. Then, we fully considered the defects of these two tools and proposed a new integrated method that combines the physical model with a neural network. The Tbs in horizontal and vertical polarizations from AMSR2 and GNSS-derived PWV (GNSS-PWV) data from the SuomiNet global network were used to build the physical model. In the construction of the neural network, we also introduced the related surface parameters from GNSS stations as inputs. Validation results obtained with GNSS-PWV data showed that the accuracy of the test set can reach 2.38 and 2.37 mm in the ascending and descending orbit cases, respectively. Compared with the traditional physical model and neural network model, the improvements of the test set were 24.4% and 17.4% in the ascending orbit and 26.4% and 19.4% in the descending orbit. Radiosonde observation (RAOB) data were applied to carry out another external independent verification, and the accuracy of the test set reached 2.70 and 3.54 mm based on the RAOB data.

keywords：Advanced microwave scanning radiometer 2 (AMSR2), neural network, physical model, precipitable water vapor (PWV).

citations：Zhaorui Gao, Nan Jiang, Yan Xu*, Tianhe Xu, and Yangfan Liu. Precipitable Water Vapor Retrieval Over Land From GCOM-W/AMSR2 Based on a New Integrated Method. IEEE Transactions on Geoscience and Remote Sensing, 2022.