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Your search keyword '"Guo, Zhizhou"' showing total 12 results
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12 results on '"Guo, Zhizhou"'

5. Super Resolution Mapping of Scatterometer Ocean Surface Wind Speed Using Generative Adversarial Network: Experiments in the Southern China Sea.

Author

Wan, Xianci, Liu, Baojian, Guo, Zhizhou, Xia, Zhenghuan, Zhang, Tao, Ji, Rui, and Wan, Wei

Subjects
GENERATIVE adversarial networks, WIND speed, STANDARD deviations, SYNTHETIC aperture radar, OCEAN
Abstract

This paper designed a Generative Adversarial Network (GAN)-based super-resolution framework for scatterometer ocean surface wind speed (OSWS) mapping. An improved GAN, WSGAN, was well-trained to generate high-resolution OSWS (~1/64 km) from low-resolution OSWS (~12.5 km) retrieved from scatterometer observations. The generator of GAN incorporated Synthetic Aperture Radar (SAR) information in the training phase. Therefore, the pre-trained model could reconstruct high-resolution OSWS with historical local spatial and texture information. The training experiments were executed in the South China Sea using the OSWS generated from the Advanced SCATterometer (ASCAT) scatterometer and Sentinel-1 SAR OSWS set. Several GAN-based methods were compared, and WSGAN performed the best in most sea states, enabling more detail mining with fewer checkerboard artifacts at a scale factor of eight. The model reaches an overall root mean square error (RMSE) of 0.81 m/s and an overall mean absolute error (MAE) of 0.68 m/s in the collocation region of ASCAT and Sentinel-1. The model also exhibits excellent generalization capability in another scatterometer with an overall RMSE of 1.11 m/s. This study benefits high-resolution OSWS users when no SAR observation is available. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

Author

National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Guo, Zhizhou, Liu, Baojian, Wan, Wei, Lu, Feng, Niu, Xinliang, Ji, Rui, Jing, Cheng, Li, Weiqiang, Chen, Xiuwan, Yang, Jun, Bai, Zhaoguang, National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Guo, Zhizhou, Liu, Baojian, Wan, Wei, Lu, Feng, Niu, Xinliang, Ji, Rui, Jing, Cheng, Li, Weiqiang, Chen, Xiuwan, Yang, Jun, and Bai, Zhaoguang

Abstract

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm-3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm-3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

Published
2022

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