A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface

• Published in: Geo-spatial information science 2024-05, Vol.27 (3), p.638-652
• Main Authors: Jing, Cheng, Li, Weiqiang, Wan, Wei, Lu, Feng, Niu, Xinliang, Chen, Xiuwan, Rius, Antonio, Cardellach, Estel, Ribó, Serni, Liu, Baojian, Guo, Zhizhou, Nan, Yang
• Format: Article
• Language: English
• Subjects: Bufeng-1; Calibration; Data processing; Delay-Doppler Map (DDM); Global navigation satellite system; Global Navigation Satellite System-Reflectometry (GNSS-R); Group delay; Hurricanes; Immediate Frequency (IF); Intermediate frequencies; Machine learning; Retrieval; sea surface height (SSH); sea surface wind speed; Soil moisture; Soil surfaces; Weather forecasting; Wind speed; Workflow
• ISSN: 1009-5020; 1993-5153
• DOI: 10.1080/10095020.2024.2330547

In this paper, we will conclude the results of Bufeng-1 (BF-1) A/B data processing, calibration workflow, and validation of the calibrated sea surface winds, land surface soil moisture, and sea surface height measurements. Since 2019, the BF-1 mission has operated in-orbit for over 4 years. The Earth reflected delay Doppler maps (DDMs) are continuously collected to perform global sea surface and land observations. At the same time, the intermediate frequency (IF) raw data are also obtained for 12 seconds every pass in diagnostic mode. To begin with, a brief description of the spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technique will be provided in the introduction. Next, we will present the overview of Chinese BF-1 mission and the data specifications used in our research. In the next section, the BF-1 mission-related spaceborne power calibration and validation are presented to show the support to power DDM observable production for sea surface and land surface applications. Then, the status of Chinese Beidou System (BDS) Equivalent Isotropic Radiated Power (EIRP) acquisition programme is then introduced. Furthermore, the latest sea surface height (SSH) measurements results including two modes (group delay and carrier phase) and wind speed derivation based on machine learning (ML) method will be spatial-temporal aligned and validated with auxiliary datasets including Denmark Technology University (DTU) mean sea surface (MSS) products and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. The previous published results of sea surface winds retrieval under Hurricane conditions and soil moisture retrieval are also reviewed for the BF-1 mission applications. Finally, the conclusion of BF-1 derived results will be discussed to draw out ongoing/future works.