Tidal measurements in the Gulf of Mexico: intercomparison of coastal tide gauge, insular GNSS reflectometry and SAR altimetry

Martinez-Felix, Carlos A.; Vázquez-Becerra, G. Esteban; Geremia‐Nievinski, Felipe; Millán-Almaraz, Jesús Roberto; Franco-Ochoa, Cuauhtémoc; Melgarejo-Morales, Angela; Gaxiola‐Camacho, J. Ramon

doi:10.1007/s10291-021-01207-x

Cited by 3 publications

(1 citation statement)

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“…However, these methods have their limitations. Tidal gauges can be affected by vertical crustal movements, while spaceborne altimeters have long revisit periods and are cost-intensive [7,8]. GNSS-R technology shows great potential in the field of remote sensing due to its advantages.…”

Section: Introductionmentioning

confidence: 99%

Advancing Sea Surface Height Retrieval through Global Navigation Satellite System Reflectometry: A Model Interaction Approach with Cyclone Global Navigation Satellite System and FengYun-3E Measurements

Xing,

Yang,

Zhang

et al. 2024

Remote Sensing

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The measurement of sea surface height (SSH), which is of great importance in the field of oceanography, can be obtained through the innovative technique of GNSS-R for remote sensing. This research utilizes the dataset from spaceborne GNSS-R platforms, the Cyclone Global Navigation Satellite System (CYGNSS) and FengYun-3E (FY-3E), as the primary source of data for retrieving sea surface height (SSH). The utilization of artificial neural networks (ANNs) allows for the accurate estimation of ocean surface height with a precision of meter-level accuracy throughout the period of 1–17 August 2022. As a traditional machine learning method, an ANN is employed to extract pertinent data features, facilitating the acquisition of precise sea surface height estimations. Additionally, separate models are devised for both GNSS-R platforms, one based on constant velocity (CV) and the other on constant acceleration (CA). The Interactive Multiple Model (IMM) is utilized as the main method to combine the four models and convert the likelihood of each model. The transition between the models allows the filters to effectively adapt to dynamic changes and complex environments. This approach relies on the fundamental notion of the Kalman filter (KF), which showcases robust noise handling capabilities in predicting the SSH, separately. The results demonstrate that the model interaction technology is capable of efficiently filtering and integrating SSH data, yielding a Root Mean Square Error (RMSE) of 1.03 m. This corresponds to a 9.84% enhancement compared to the retrieved height from CYGNSS and a 37.19% enhancement compared to the retrieved height from FY-3E. The model proposed in this paper provides a potential scheme for the GNSS-R data fusion of multiple platforms and multiple models. In the future, more data sources and more models can be added to achieve more accurate adaptive fusion.

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Section: Introductionmentioning

confidence: 99%