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

Geo-spatial Information Science ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/tgsi20 A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface Cheng Jing, Weiqiang Li, Wei Wan, Feng Lu, Xinliang Niu, Xiuwan Chen, Antonio Rius, Estel Cardellach, Serni Ribó, Baojian Liu, Zhizhou Guo & Yang Nan To cite this article: Cheng Jing, Weiqiang Li, Wei Wan, Feng Lu, Xinliang Niu, Xiuwan Chen, Antonio Rius, Estel Cardellach, Serni Ribó, Baojian Liu, Zhizhou Guo & Yang Nan (2024) A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface, Geo-spatial Information Science, 27:3, 638-652, DOI: 10.1080/10095020.2024.2330547 To link to this article: https://doi.org/10.1080/10095020.2024.2330547 © 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 16 Apr 2024. Submit your article to this journal Article views: 1399 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tgsi20 A review of the BuFeng-1 GNSS-R mission: calibration and validation results of sea surface and land surface Cheng Jing a,b , Weiqiang Li c,d , Wei Wan e , Feng Lu f , Xinliang Niu a,b , Xiuwan Chen e , Antonio Rius c,d , Estel Cardellach c,d , Serni Ribó c,d , Baojian Liu g , Zhizhou Guo e and Yang Nan h a National Key Laboratory of Science and Technology on Space Microwave, China Academy of Space Technology (Xi’an), Xi’an, China; b Xi’an Institute of Space Radio Technology, China Academy of Space Technology (Xi’an), Xi’an, China; c Institute of Space Sciences, Spanish National Research Council (ICE, CSIC), Cerdanyola del Vallès, Spain; d Department of Earth Observation, Institut d’Estudis Espacials de Catalunya, Barcelona, Spain; e The Institute of Remote Sensing and Geographic Information System (IRSGIS), Peking University, Beijing, China; f The National Satellite Meteorological Center (NSMC), China Meteorological Administration (CMA), Beijing, China; g School of Soil and Water Conservation, Beijing Forestry University, Beijing, China; h School of Marine Science and Technology, Tianjin University, Tianjin, China ABSTRACT 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. ARTICLE HISTORY Received 6 June 2023 Accepted 8 March 2024 KEYWORDS Global Navigation Satellite System-Reflectometry (GNSS-R); Delay-Doppler Map (DDM); sea surface height (SSH); soil moisture; sea surface wind speed; Bufeng-1; Immediate Frequency (IF) 1. Introduction The Global Navigation Satellite System Reflectometry (GNSS-R) as a concept was firstly proposed independently by Hall and Cordey for multistatic ocean scatterometry in 1988 (Hall and Cordey 1988) and by Martin-Neira for ocean altimetry with the concept of Passive Reflectometry and Interferometry System (PARIS) (Martin-Neira 1993). Later in 1998, GNSS-R was firstly demonstrated through an airborne experiment for sea surface roughness sensing (Garrison, Katzberg, and Hill 1998). Subsequently, GNSS-R as a cost-effective technique has been widely investigated for over two decades in fields including sea surface winds, sea surface altimetry, inland soil moisture, sea ice, etc. The GNSS-R space mission was started in 2003, the UK-Disaster Monitoring Constellation (UK-DMC) mission conducted the first in-orbit test of the GNSS-R technique, which resulted in the first delay Doppler map (DDM) obtained from low Earth orbit (LEO) (Unwin, Gleason, and Brennan 2003). Then, a technology demonstration GNSS-R satellite, TechDemoSat-1 (TDS-1), equipped with the Space GPS Receiver Remote Sensing Instrument (SGRReSI), was launched in July 2014 and operated for 4.5 years, collecting valuable in-orbit data on sea surface winds, height, inland soil moisture, and sea ice (Unwin et al. 2013). The results from TDS-1 provide robust support for future GNSS-R operational use in global sea surface wind speed retrieval and sea ice detection (Alonso-Arroyo, Zavorotny, and Camps 2017; Cartwright, Banks, and Srokosz 2019; Gleason 2013; Li et al. 2017; Rodriguez-Alvarez et al. 2019; Unwin et al. 2010, 2013; van Steenwijk, Unwin, and Jales 2010; Yan and Huang 2016; Zhu et al. 2017). In CONTACT Feng Lu [email protected] GEO-SPATIAL INFORMATION SCIENCE 2024, VOL. 27, NO. 3, 638–652 https://doi.org/10.1080/10095020.2024.2330547 © 2024 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. December 2016, NASA launched the Cyclone GNSS (CYGNSS) mission with a constellation of 8 microsatellites for observing tropical cyclones with high spatial and temporal resolution. The CYGNSS mission has successfully demonstrated the GNSS-R technique’s capability to sense the tropical cyclone (TC) eyes in high temporal resolution, optimize TC forecasts, provide monthly high-resolution soil moisture products, and map inland waterways (CarrenoLuengo et al. 2021; Clarizia et al. 2019; Jing et al. 2016; Mayers and Ruf 2019; Ruf et al. 2013; Ruf et al. 2017; Zhang et al. 2017). In the same year of 2016, The Universitat Politècnica de Catalunya (UPC) launched their advanced spaceborne CubeSats with GNSS-R instruments called 3Cat-2 mission (Carreno-Luengo et al. 2016). In 2020, an improved version of 3Cat-2 was developed and launched by the FSSCat mission sponsored by ESA (Camps et al. 2018). On the other side, a space-to-cloud data analytics company, SPIRE Global Inc, launched two batches of 3 U CubeSats with GNSS-R instruments on board in 2019 and 2020, respectively (Freeman et al. 2020; Jales et al. 2020). The Chinese spaceborne GNSS-R era began with the Bufeng-1 (BF-1) mission in 2019, which was developed by the China Aerospace Science and Technology Corporation (CASC) and launched by the Chinese first-time sea platform (Guo et al. 2022; Jing et al. 2019, 2021; Wan et al. 2021). In 2021, the China Meteorological Administration (CMA) FY-3E mission integrated a GNSS-R receiver to perform a near realtime Earth surface observation with a polar orbit (Sun et al. 2019; Zhang et al. 2022). This paper presents a scientific investigation conducted by a collaborative research team comprising members from China and Europe. The research primarily centers around the BF-1 mission GNSS-R, with a specific focus on sea surface height measurement, sea surface wind retrieval, and in-land soil moisture. The results pertain to the published results on in-land soil moisture retrieval and sea surface winds retrieval under different sea states, and novel unpublished ML sea surface wind derivation and sea surface height measurements in both group delay and carrier phase mode. Additionally, the ongoing/future works and conclusions will be discussed in the conclusion section. 2. Mission overview The BF-1 mission represents the initial Chinese GNSS-R mission, comprising a pair of dedicated demonstration satellites (Jing et al. 2019, 2021). The mission was successfully deployed in Low Earth Orbit (LEO) via the Chinese first-time sea platform launch on 5 June 2019, as is shown in Figure 1(a). The onboard GNSS-R instruments were developed by the China Aerospace Science and Technology Corporation (CASC). The main client is China Meteorological Administration (CMA), who requires the main objective of observation of sea surface winds, including hurricane conditions. After three months in-orbit operation, a Geophysical Model Function (GMF) for global sea surface wind speed was built and achieved the clients’ requirement that root mean square error (RMSE) is better than 2 m/s under 20 m/s and 10% above 20 m/s. Then, the GMF was deployed in the client’s server to produce global sea surface winds product. The client also developed their own GMF by machine learning method, which will be described in the following section. The twin space vehicles (SVs) operate in LEO with altitude of 579 km and inclination of 45 °. The instruments of each satellite consist of one direct antenna, two 26-degree tilt reflection high-gain antennas, a GNSS-R receiver, and others. BF-1 mission has two modes: the DDM mode and raw IF data mode. The operational routine DDM mode collects four Earthreflected GNSS-R signals from both sides of the satellites. The global coverage of the specular points is between latitude 53°S to 53°N. The collected DDMs have a size of 128 × 21 in resolution of 0.25 delay chip and 500 Hz doppler frequency. On the other hand, each satellite’s diagnostic mode will start the raw IF data collecting about 12 s within every pass, including data from GPS L1 C/A channels and BDS B1I channels. The specifications are as shown in Table 1. Figure 1. (a) The news picture of Chinese first-time sea platform launch carrying the Chinese first-generation spaceborne GNSS-R instruments. (b) the BF-1 GNSS-R instruments developed and photographed by CASC. GEO-SPATIAL INFORMATION SCIENCE 639 3. Data, methodology, and results This section focuses on five specific aspects of researches, describing the applications of the BF-1 mission during the period between the year of 2020 and 2023. The studies encompass power calibration and BDS EIRP acquisition, first-time sea surface height measurements, sea surface wind derivation by ML method, sea surface winds under different conditions, and soil moisture retrieval. The contents include detailed information regarding instrumental calibration/validation, data use, methodology, and validation to demonstrate how the BF-1 mission has performed in meeting ocean and land Earth observation. 3.1. Calibration, validation and EIRP acquisition BF-1 satellites have similar design (UK TDS-1 and NASA CYGNSS) for power calibration by utilizing blackbody payload and microwave switch in each channel. In (Wan et al. 2020), BF-1 power calibration and system error analysis are introduced, analyzed, and illustrated. On the other hand, BDS EIRP acquisition has been supported by the National Natural Science Foundation (NSFC) of China (Grant no. 42101409). 3.1.1. Power calibration and validation The power calibration is based on the power DDM collected by BF-1 GNSS-R instruments and noise DDM output by blackbody payload every minute (Wan et al. 2020), as shown in Figure 2. The procedure includes DDM total gain equation, DDM noise statistic equation, blackbody noise equation, and blackbody thermal noise equations, etc. (Gleason et al. 2016) The Low Noise Amplifier (LNA) instrument is easily affected by ambient temperatures. The DDM observable supposes to be close under certain sea surface wind speed. Owing to the thermal conditions, the observable values are not aligned in a certain range, as shown in Figure 3(a). After the thermal noise power calibration, the temperature effect can be mitigated to reach a consistency under different sea conditions, as shown in Figure 3(b). This calibration can give a basis for the DDM power quantification for NBRCS and earth reflectivity (ER). 3.1.2. EIRP acquisition According to GNSS-R bistatic scattering radar function (Gleason et al. 2016), the power and antenna gain of GNSSs transmitter also contribute to the quantification of NBRCS. By utilizing a ground observation station, NASA CYGNSS mission has acquired the GPS EIRP, which is available in the L1 products (Wang, Ruf, et al. 2018, Wang, Ruf, Block, McKague, and Gleason 2018). Initially, the preliminary BF-1 GPS wind speed GMF was derived by utilizing the EIRP products obtained from NASA’s CYGNSS mission (Jing et al. 2019). Afterwards, the EIRP acquisition programme of BDS signal is designed and developed by CASC to supply the calculation of BDS NBRCS. This programme is supported by National Natural Science Foundation (NSFC) from 2022 to 2024. At last, the outcomes of the foundation will conclude BDS EIRP ground station observations lookup tables, calibrated spaceborne BDS-R sea surface product, and corresponding validation results. 3.2. Sea surface heights Different from the DDM observables, the sea surface height is calculated from the spaceborne GNSS-R raw IF data. The European research partners of BF-1 Table 1. Specifications of BF-1 mission. GNSS-R receiver Frequency GPS L1 and BDS B1 Antenna Gain ≥14 dBi Mass ≤10 kg Power Consumption ≤30 W DDM Mode Delay Chip Resolution 1/4 chip Doppler Frequency Resolution 500 Hz Specular Points 4 from both sides of the twin SVs Raw Data Mode L1CA Sampling Rate 4.092 MHz B1I Sampling Rate 8.184 MHz Duration 12 sec per pass Figure 2. The design of power calibration instrument including calibration load (blackbody) and microwave switch. 640 C. JING ET AL. mission, Institute of Space Sciences (ICE-CSIC, IEEC), has investigated spaceborne GNSS-R raw IF data including missions like TDS-1, CYGNSS, SPIRE Lemur, etc (Li et al. 2022). Accordingly, after related preprocessing, the BF-1 raw IF data are processed to provide the advanced features of the data products for sea surface altimetry in both the group delay mode and carrier phase mode for the first-time publication. 3.2.1. Data set and processing According to Table 1 Specifications of BF-1 mission, the spaceborne raw IF data can cover both sampling of GPS L1 C/A and BDS B1I. Besides, the bandwidths are ~2 MHz and ~4 MHz, respectively. The data we use is from the year of 2020, as is illustrated in Table 2. At the same time, the data of DTU18 mean sea surface product are introduced to be spatial aligned and to assess the performance and accuracy of the measurements in both group delay and carrier phase modes (Andersen, Knudsen, and Stenseng 2018). One should be noted that the DTU18 is a mean sea surface product so that the validation is between two derived variables. As a result, we use the root mean square deviation (RMSD) and coefficient of determination (R2) to illustrate the performance of the SSH measurements by BF-1 GNSS-R instruments. The processing of BF-1 raw IF samples are highly accorded to the published works by IEEC (Li et al. 2019). Thus, the procedure consists of close-loop processing of the direct signal and open-loop (OP) processing of the reflected signals. The ancillary information, including the positions, velocities, and timing information of the GNSS transmitters and BF-1 satellites are extracted from the GNSS precise ephemeris files, the BF-1 raw IF, and related L1 metadata. In addition, the corrections are also applied to the bistatic delay, including the ionospheric delay, the tropospheric delay, and the tidal corrections. 3.2.2. Altimetry results The BF-1 raw data altimetry results include group delay measurements abstracted from delay waveforms and carrier phase measurements from IQ (In-phase and Quadrature components) sampling. 3.2.2.1. Group delay. According to Table 2, four experiments centered at the frequency of 1561.098 MHz are illustrated to observe the specular points within the field-of-view of the science antennas, as is shown in Figure 4. Then, combined with the BF-1 L1 metadata, each BF1 spaceborne GNSS-R raw IF data with the latency of 12 seconds are processed and spatial aligned with the DTU18 mean sea surface products, as shown in Figure 5. Table 2. BF-1 spaceborne GNSS-R raw if data of 2020. Date Start Time Resource Code Mode 2020/2/12 17:42:17 GPS PRN17 L1 C/A Group Delay 2020/2/13 16:02:22 BDS PRN23 B1C Group Delay 2020/2/16 16:04:26 BDS PRN03 B1I Group Delay 2020/2/23 12:47:58 GAL PRN26 E1 Group Delay 2020/2/13 16:02:22 GAL PRN21 / Carrier Phase 2020/8/23 21:07:00 GPS PRN13 / Carrier Phase Figure 3. The design of power calibration instrument including calibration load (blackbody) and microwave switch. GEO-SPATIAL INFORMATION SCIENCE 641 For the next step, we summed up the four experiments, sea surface height measurements from GPS, BDS, and Galileo, to assess the total performances compared with the DTU 18 products of two different spatial resolutions. The scatter plots are as shown in Figure 6. As a result, the group delay sea surface height measurements by BF-1 spaceborne raw IF data have a high agreement with the DTU18 mean sea surface models. The accuracies are at a meter-level, which also meet the signal-to-noise (SNR) model proposed by Li et al. (2018). 3.2.2.2. Carrier phase. Normally, sea surface altimetry precision of spaceborne GNSS-R is typically one to two orders of magnitude lower than dedicated radar altimeters. However, coherent scattering allows tracking of the electromagnetic phase of the carrier signal, enabling accurate ranging measurements. At the same time, the coherent scattering can be optimized in grazing angle (GA) geometries, facilitating carrier phase-delay altimetric techniques over sea waters (Cardellach et al. 2019). In this case, BF-1 raw IF data with the GA feature are abstracted to track and measure the sea level within the calm condition of high probability in the proposed global map (Cardellach et al. 2019). Thus, as shown in Table 2 Carrier Phase mode, two datasets are Figure 4. The start positions of the specular points labeled with the GNSS PRNs, which are related to information of date and time in Table 2 group delay mode. Figure 5. Spatial alignments with DTU18 1-min and 20-min products. The red circles are BF-1 raw if data 1 hz observations of the 12 seconds. The blue curves show the DTU 18 1-min mean sea surface heights, and the green curves show the 20-min. (a) GPS PRN17 with the RMSD of 1.06 meter, (b) BDS PRN23 with the RMSD of 1.01 meter, (c) BDS PRN03 with the RMSD of 0.81 meter, (d) galileo PRN26 with the RMSD of 0.86 meter. 642 C. JING ET AL. processed with the same procedures in raw acquisition phase-delay altimetry (Li et al. 2017; Li et al. 2018). As is shown in Figures 7, 8 , and 9, the two sets of 20 Hz carrier phase results from BF-1 raw IF samples Figure 6. Scatter plots of the total four set observations mentioned in Figure 5. (a) Results compared with DTU18 1-min mean sea surface measurements. The total RMSD is 0.93 meter with the R 2 of 99.17%. (b) results compared with DTU18 20-min products. The parameters of RMSD and R 2 are 0.94 meter and 99.15%, respectively. Figure 7. BF-1 spaceborne carrier phase sea surface height measurements versus DTU 18 mean sea surface products with two spatial resolutions. The blue solid lines are of 1-min resolution, the green ones are of 20-min resolution, respectively. (a) distributions of the specular points. (b) the spatial alignment plots by the signal of Galileo PRN 21. (c) plots from GPS PRN13. GEO-SPATIAL INFORMATION SCIENCE 643 are assessed with a precision of 5 cm order of magnitude when compared with the DTU MSS products. Notably, the probability of the carrier phase ranging by GNSS-R highly depends on the elevation angle and sea surface roughness. It has been studied that this precise spaceborne GNSS-R ranging method can only be achieved under certain water surface conditions, quantified with a wind speed of less than 6 m/s and wave of less than 1.5 m (Cardellach et al. 2019; Li et al. 2018). 3.3. Sea surface winds under fully developed seas As the client of BF-1 mission, the National Satellite Meteorological Center (NSMC) of CMA developed an ensemble ML method to derive sea surface wind speed by taking advantage of the high-performance computing cluster (Lu et al. 2023). In the study, BF-1 wind speed retrieval is based on an open-source autoML framework named AutoGluon (Erickson et al. 2020). In the framework, the Tabular Prediction function contains a series of customized models. Lastly, we chose the WeightedEnsemble, LightGBM, XGBoost, and ExtraTrees (Caruana et al. 2004; Chen and Guestrin 2016; Geurts, Ernst, and Wehenkel 2006; Ke et al. 2017). The DDM observable is the Normalized Bistatic Radar Cross Section (NBRCS). The specifications of the parameters for ML are listed in Table 3. The global wind speed training set time was June 2020, while the validation time was July 2020. One should be noted that the NWP (Numeric Weather Prediction) forecast wind speed is only used for quality control of outliers. Because the power adjustment of GPS and Radio Frequency Interference (RFI) are not informed to users timely, the derived wind speed, influenced by alterations in transmitted Figure 8. Scatter plots of BF-1 SSH versus DTU18 1-min product. The RMSD is unbiased 4.05 cm with the R 2 of 99.98% (a) galileo PRN21 scatter plots. (b) GPS PRN13 scatter plots. Figure 9. Scatter plots of BF-1 SSH versus DTU18 20-min product. The RMSD is unbiased 6.65 cm with the R 2 of 99.93% (a) galileo PRN21 scatter plots. (b) GPS PRN13 scatter plots. 644 C. JING ET AL. power, will exhibit extremely abnormal characteristics (Setti and Dam 2022). To address this issue, quality control adheres to a criterion of 10 m/s or a 100% disparity between the derived outcomes and NWP predictions. When compared with the previous developed GMF described in Jing et al. (2019), the updated method exhibits enhanced performance in terms of accuracy (RMSE) and coefficient of determination (R2). This improvement is evident when evaluating the matchup between the sea surface wind speeds derived from the ML method and ECMWF ERA5 reanalysis dataset, as is illustrated in Figure 10. The related distributions of BF-1 derived global wind speed are shown in Figure 11. Furthermore, the ML method is operated to observe the wind speed distributions in Indian Ocean and Pacific Ocean during the Typhoon season in 2020. Despite the lack of coverage of the BF-1 observations, the areas under the severe sea state conditions are perceptible as shown in Figure 12. 3.4. Sea surface winds under hurricane condition In the case of sea surface winds under Hurricane conditions, we aligned the measurements obtained from BF-1 with the Hurricanes observed by the Stepped Frequency Microwave Radiometer (SFMR) mounted on NOAA aircraft during the 2019 Hurricane Season. The SFMR measures radiative emissions from six frequencies range from 4.6 GHz to 7.2 GHz. The SFMR is a reliable precise microwave instrument for measuring surface wind under hurricane condition, which has been testified a precision of less than 1 m/s above wind speed of 10 m/s (Uhlhorn and Black 2003). Subsequently, we present a power function relationship between the Normalized Radar Cross Section (NBRCS) obtained from BF-1 and the wind speeds experienced under severe sea states (Jing et al. 2016, 2021). In the research, after applying spatial-temporal alignment criteria (0.25-degree latitude/longitude and 2 hours), there are 83 files included in the study, as is detailed in Table 4. The total number of matchup Table 3. BF-1 specifications of the parameters for ML. Input for Training Wind speed retrieval Result NWP forecast windspeed NWP forecast windspeed Derived sea surface wind speed Specular point latitude Specular point latitude Specular point longitude Specular point longitude Incident angle Incident angle Receiver latitude Receiver latitude Receiver longitude Receiver longitude DDM SNR DDM SNR NBRCS NBRCS ERA5 reanalysis windspeed Figure 10. Scatter density plots of BF-1 derived global winds by CMA versus the ECMWF ERA-5 reanalysis in Indian Ocean region. The RMSE is 1.461 m/s with a R 2 of 0.748. Figure 11. Distributions of BF-1 derived sea surface wind speed on a global scale. GEO-SPATIAL INFORMATION SCIENCE 645 Unwin, M., S. Gleason, and M. Brennan. 2003. “The Space GPS Reflectometry Experiment on the UK Disaster Monitoring Constellation Satellite.” Paper presented at the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS 2003), Portland, OR. Unwin, M., P. Jales, P. Blunt, S. 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