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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Coastal Water Remote Sensing From Sentinel-2 Satellite Data Using Physical, Statistical, and Neural Network Retrieval Approach
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Coastal Water Remote Sensing From Sentinel-2 Satellite Data Using Physical, Statistical, and Neural Network Retrieval Approach

机译：来自Sentinel-2卫星数据的沿海水遥感，使用物理，统计和神经网络检索方法

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Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination ${R}^{2}$ up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg $ext{m}^{-3}$ , using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with $R^{2}= 0.52$ and RMSE = 1.95 g/m 3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores.
机译：最近的光学遥感卫星任务，如Hentinel-2，在板载中，允许使用非常高的空间分辨率（下至10米）估计沿海水密钥参数。在本文中，提出了使用基于实证和模型的框架来设计回归和神经网络的亚得里亚特和Tyrrhenian海岸沿着亚得里亚特和Tyrrhenian海岸检索叶绿素-A（CHL-A）和总悬浮物（TSM）的多种方法来设计回归和神经网络（NN ）估算方法。后者在区域规模上被证明是更准确的，其中标准的海洋颜色物理模型由于观察到的场景的复杂光谱特性而在本地参数化中表现出高的不确定性。 Reproval结果是令人鼓舞的CHL-A，其中确定系数<内联公式XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http：//www.w3 .org / 1999 / xlink“> $ {R} ^ {2} $ 高达0.72，具有根均方错误（RMSE）0.33 mg <内联公式XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http://www.w3.org/1999/xlink” > $ text {m} ^ { - 3} $ ，使用实证nn。 TSM算法表现出更高的不确定性，主要是由于<斜体XMLNS：MML =“http://www.w3.org/1998/math/mathml”xmlns：xlink =“http://www.w3.org/ 1999 / xlink“>原位测量和模型参数化，带有 $ r ^ {2} = 0.52 $ 和Rmse = 1.95 g / m 3 使用nns 。用于开发基于模型的算法的生物光学模型，表明了代表案例研究区域的固有和表观光学性质的缺点，特别是考虑到寡替氏菌蒂伦海和富营养化亚洲海之间的不同光谱特征。本研究证实了Sentinel-2 MSI产品用于沿海水监测的潜力，但它还突出了要进一步解决的关键问题，例如大气校正影响，可靠的<斜体XMLNS：MML =“http：//www。 w3.org/1998/math/mathml“xmlns：xlink =”http://www.w3.org/1999/xlink“>原位测量，以及海岸附近的可能的沐浴效果。

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