This study collects phytoplankton absorption and chlorophyll a data and examines their correlation in coastal waters around China measured between 2003 and 2017. Single-parameter model is built to construct phytoplankton absorption from pigment concentration. Gaussian decomposition technique extracts center wavelengths and bandwidths of 13 Gaussian functions, which constitute the modified Gaussian model, or so-called multiple-parameter model. Both models can reproduce measured phytoplankton absorption very well. In terms of the mean absolute relative error, single-parameter model can reproduce measured absorption within 30% and 60% between 380nm and 700nm for 40% and 75% of data, respectively. Meanwhile, multiple-parameter model can reproduce measured absorption within 15% for 80% of data, and within 20% for the majority of wavelengths between 380nm and 700nm.
For decades, the global remotely sensed significant wave heights have been from altimeters and/or synthetic aperture radars in wave mode, which both suffer from spatial and temporal sampling limitations. In contrast, spaceborne scatterometers are with large swath and high temporal revisit frequency at a global scale, but so far are routinely providing ocean winds rather than waves. This paper addresses the ocean sea state retrieval algorithm by applying state-of-the-art machine learning technology to European Advanced Scatterometer (ASCAT). A huge collocation database (< 6 million) has been built between L1b/L2 ASCAT products and WaveWatch III (ww3) ocean wave hindcasts within the spatio-temporal criteria of 0.1 degree and 0.5 h for the period of three years, followed by the mining of this big data by means of machine learning (i.e., multi-hidden layer neural network here). The neural network proposed here includes layers: the input layer (13 ASCAT variables), four hidden layers, and the output layer (wave heights). The performance of machine learning based approach for ocean wave height estimation from scatterometer is evaluated using two independent match-ups: ASCAT-WW3 and ASCAT-buoy. The statistical assessment against SWH hindcast shows the root mean square error of 0.55 m and scatter index of 23%, respectively. Results indicate that the data driven algorithm is reasonable for sea state estimation from wide-swath scatterometers, and encouraging for operational implementation in the future.
In 2016, the first Chinese synthetic aperture radar (SAR), the Gaofen-3 (GF-3) satellite, was launched. Unlike the single-polarized
wave mode SARs in Europe, GF-3 is the first satellite acquiring the quad-polarized SAR data in wave mode
configuration, which could benefit the ocean wave estimation. Here, the ocean wave spectra estimation from quad-polarized
GF-3 wave mode is presented and its performances are evaluated. For the period from January to October in
2017, the ocean wave spectra were inverted from GF-3 wave mode data. The quad-polarized SAR-ocean spectra inversion
scheme was utilized, in which the azimuthal and range wave slopes are obtained from the vertically and linearly polarized
normalized radar cross sections and then converted to ocean wave slope spectra. The validation was also performed through
comparisons against directional wave buoy observations. The spatio-temporal criteria of 100 km and 0.5 h, yield 87 matchups.
Two representative cases illustrate the consistency between the GF-3 SAR ocean wave spectra and buoy
measurements. Statistical assessment shows the root mean square error (RMSE) of 0.35 m, 19.52 m and 24.89° for the
significant wave height, peak wavelength and wave direction, respectively. Evaluation results indicate that the quad-polarized
algorithm is suitable for spectral ocean wave estimation from GF-3 wave mode, and encouraging for operational
implementation.
Sentinel-1 A now routinely acquires data over the ocean since 2014. Data are processed by ESA through the Payload Data Ground Segment up to Level-2 for Copernicus users. Level-2 products consist of geo-located geophysical parameters related to wind, waves and ocean current. In particular, Sentinel-1A wave measurements provide 2D ocean swell spectra (2D wave energy distribution as a function of wavelength and direction) as well as integrated parameters such as significant wave height, dominant wavelength and direction for each partition. In 2016, Sentinel-1 B will be launched by ESA and GF-3 by CNSA. Then in 2018, CFOSAT (China France Oceanography Satellite project), a joint mission from the Chinese and French Space Agencies, will be launched. They will also provide 2D Ocean waves spectra. This paper focuses on the techniques used to validate 2D-ocean waves as measured by satellite and the challenges and opportunities of such a program for ocean waves measurements from space.
The first Chinese Ku-band scatterometer (SCAT), carrying on the satellite HY-2, has provided the global ocean surface wind vector products since its successful launch in August 2011. The HY-2 SCAT wind products are estimated based on the geophysical model function (GMF), where only the wind vector, while no wave information is taken into consideration. Thus, it is still an open issue that the contribution to HY-2 SCAT wind product errors owing to the swell influence. In this paper, the swell impact on HY-2 SCAT wind speed and direction product quality is presented through the comparison of the one year HY-2 products with buoy data. The NDBC buoy wave spectra were reconstructed from measurements, partitioned using watershed algorithm, and categorized into 4 groups of pure swell, swell and wind sea dominated mixed sea, and pure wind sea. The wave dependency of wind residuals between the HY-2 and the buoy were investigated. The comparison results on different swell components indicate the significant influence of the swell present on HY-2 SCAT quality in terms of both wind speed and wind direction. It is recommended that the information on sea state should be integrated into the wind retrieval algorithm for Ku-band scatterometers (HY-2 SCAT and also the future scatterometer on CFOSAT), in order to improve the accuracy.
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