Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Qiu, Hui; Jin, Shuanggen

doi:10.3390/rs12030356

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…However, the relative rate of SL change is not globally identical because it depends on different spatial and temporal parameters (see Figure 7). Conventionally, SL estimation was based on coastal monitoring stations, tide gauges, buoys, and ship surveys [212]. However, the high cost and sparse observations of these approaches make them inappropriate for SL measurements in most cases.…”

Section: Sea Level (Sl)mentioning

confidence: 99%

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Amani¹,

Moghimi

Mirmazloumi

et al. 2022

Water

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Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.

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Section: Sea Level (Sl)mentioning

confidence: 99%

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Amani¹,

Moghimi

Mirmazloumi

et al. 2022

Water

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“…The first three levels of products are open to the public, and now available in NASA's Physical Oceanography Distributed Active Archive Center (PO. DAAC) on (https://podaac.jpl.nasa.gov/) [46], with the data being in NetCDF format. In our wind speed retrieval, we use level 1 V2.1 product, downloaded from the website (https://podaac-tools.jpl.nasa.gov/drive/files/allData/cygnss/L1/v2.1).…”

Section: Data Setmentioning

confidence: 99%

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Zhu

et al. 2020

Remote Sensing

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This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.

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“…In particular, the Cyclone Global Navigation Satellite System (CYGNSS) mission, launched on December 15, 2016, consisting of eight microsatellites constellation jointly operated by the NASA and the University of Michigan (UM), provided a huge amount of space-borne GNSS-R data and made a number of progress in oceans, e.g. wind speed, sea surface, and significant wave height (Cardellach et al, 2011;Foti et al, 2017;Qiu & Jin, 2020;Wang et al, 2019;Yang et al, 2021a) and the land, e.g., soil moisture and soil freeze/ thaw cycles (Dong & Jin, 2021;Wu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing and its applications using GNSS reflected signals: advances and prospects

Jin,

Camps,

Jia

et al. 2024

Satell Navig

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The Global Navigation Satellite Systems (GNSS), including the US’s GPS, China’s BDS, the European Union’s Galileo, and Russia’s GLONASS, offer real-time, all-weather, any-time, anywhere and high precision observations by transmitting L band signals continuously, which have been widely used for positioning, navigation and timing. With the development of GNSS technology, it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of opportunity. In this paper, the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry (GNSS-R) in theory, methods, techniques and observations. New developments and progresses in GNSS-R instruments, theoretical modeling, and signal processing, ground and space-/air-borne experiments, parameters retrieval (e.g. wind speed, sea surface height, soil moisture, ice thickness), sea surface altimetry and applications in the atmosphere, oceans, land, vegetation, and cryosphere are given and reviewed in details. Meanwhile, the challenges in the GNSS-R development of each field are also given. Finally, the future applications and prospects of GNSS-R are discussed, including multi-GNSS reflectometry, new GNSS-R receivers, GNSS-R missions, and emerging applications, such as mesoscale ocean eddies, ocean phytoplankton blooms, microplastics detection, target recognition, river flow, desert studies, natural hazards and landslides monitoring.

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Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry

Cited by 11 publications

References 25 publications

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Developing and Testing Models for Sea Surface Wind Speed Estimation with GNSS-R Delay Doppler Maps and Delay Waveforms

Remote sensing and its applications using GNSS reflected signals: advances and prospects

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