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.
This paper summarizes two approaches for estimating remote sensing reflectance by above-water method. One, represented by M99 and R06, is used to estimating the sea-surface reflectance ρ, while the other, represented by G01 and L10, is applied in estimating and eliminating the contribution of residual surface-reflected light and sun glint ε. Base on the second approach, this paper proposes a new approach (HY approach), which use the quasi-analytical algorithm to estimate the remote sensing reflectance of near infrared band, and then estimate and eliminate ε. Given the preliminary estimates of 𝑅𝑟𝑠(λ), we can use quasi-analytical algorithm to estimate the inherent optical properties (IOP) of the visible spectrum. With IOP model, IOP of the near infrared band can be calculated, which then can be used to estimate the 𝑅𝑟𝑠(NIR) of near infrared wave band. And ultimately the estimating of the ε can be realized. The in-situ data were gathered in September, 2018 in the East China Sea and the South China Sea, from 76 stations. The remote sensing reflectance (Rrs) was calculated simultaneously by above-water and in-water methods. Comparing the data analyzing result of HY approach in this paper with other approaches, the variation coefficient of HY and L10 is within 5% for most stations, and within 10% for HY and R06. Compared with the results of Rrs measured by in-water method, the variation coefficient of 80% of the stations is within 15%, and the results of the two methods have a good consistency. HY method avoids the problem of extra measurement of sea surface wind in methods such as R06. This method has simple and clear steps, fast iterative convergence and better calculation speed than L10 method.
In order to ensure the reliability of satellite data, it is necessary to test the authenticity of satellite products during the operation of ocean color satellite in orbit. Therefore, it is significant to obtain accurate sea surface field data, which can provide source data for the authenticity test of satellite products. At present, the main means of acquiring data at sea in China is still large-scale voyage test on board ships. This method needs high cost and requires a lot of manual operation, and the efficiency of acquiring data is extremely limited. However, a large amount of observation data can be obtained by establishing long-term automatic observation stations at sea, and the cost is low. In this paper, the continuous observation data of atmospheric optical parameters obtained by CE318 solar photometer installed on Wenzhou offshore platform in Zhejiang Province are analyzed based on the data processing method of AERONET. Combined with the actual situation, the automatic observation data of atmospheric optical parameters at sea are qualitatively controlled and verified by satellite data. Finally, a data quality control scheme for automatic observation of atmospheric optical parameters at sea is proposed.