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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Machine learning techniques for regional scale estimation of high-resolution cloud-free daily sea surface temperatures from MODIS data
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Machine learning techniques for regional scale estimation of high-resolution cloud-free daily sea surface temperatures from MODIS data

机译：从MODIS数据的高分辨率无云日常海面温度的区域规模估计机器学习技术

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High-resolution sea surface temperature (SST) estimates are dependent on satellite-based infrared radiometers, which are proven to be highly accurate in the past decades. However, the presence of clouds is a big stumbling block when physical approaches are used to derive SST. This problem is more prominent across tropical regions such as Arabian Sea(AS) and Bay of Bengal(BoB), restricting the availability of high-resolution SST data for ocean applications. The previous studies for developing daily high-resolution cloud-free SST products mainly focus on fusion of multiple satellites and in-situ data products that are computationally expensive and often time consuming. At the same time, it was observed that the capabilities of data-driven approaches are not yet fully explored in the estimation of cloud-free high-resolution SST data. Hence, in this study an attempt has been made for the first time to estimate daily cloud free SST from a single sensor (MODIS Aqua) dataset using advanced machine learning techniques. Here, three distinct machine learning techniques such as Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Random Forest (RF)-based algorithms were developed and evaluated over two different study areas within the AS and BoB using 10 years of MODIS data and in-situ reference data. Among the developed algorithms, the SVR-based algorithm performs consistently better. In AS region, while testing, the SVR-based SST estimates was able to achieve an adjusted coefficient of determination (R-adj(2)) of 0.82 and root mean square error (RMSE) of 0.71 degrees C with respect to the in situ data. Similarly, in BoB too, the SVR algorithm outperforms the other algorithms with R-adj(2) of 0.78 with RMSE of 0.88 degrees C. Further, a spatio-temporal and visual analysis of the results as well as an inter-comparision with NOAA AVHRR daily optimally interpolated global SST (a standard SST product available in practice) the suggest that the proposed SVR-based algorithm has huge potential to produce operational high-resolution cloud-free SST estimates, even if there is cloud cover in the image.
机译：高分辨率海表面温度（SST）估计依赖于卫星基础红外辐射磁区，这在过去几十年中被证明是高度准确的。然而，当使用物理方法衍生SST时，云的存在是一个很大的绊脚石。这个问题在阿拉伯海（AS）和孟加拉湾（BOB）等热带地区更突出，限制了海洋应用的高分辨率SST数据的可用性。以前的开发每日高分辨率无云SST产品的研究主要集中在多种卫星和原位数据产品的融合，这些产品是计算昂贵且往往耗时的。同时，观察到估计无云高分辨率SST数据的数据驱动方法的能力尚未完全探索。因此，在这项研究中，首次尝试了使用先进的机器学习技术从单个传感器（MODIS AQUA）数据集中的每日云SST进行估计。这里，三种不同的机器学习技术，如人工神经网络（ANN），支持向量回归（SVR）和随机森林（RF）基础的算法，并在AS和BOB中的两个不同的研究区域使用10年的MODIS进行评估数据和原位参考数据。在发达的算法中，基于SVR的算法始终如一地执行。在作为区域的同时，在测试时，基于SVR的SST估计能够实现0.82的调整后的确定系数（R-adj（2））和0.71摄氏度的根均线误差（RMSE）相对于原位为原位数据。类似地，在BOB中，SVR算法优于0.78的R-ADJ（2）的其他算法，RMSE为0.88℃。此外，对结果的时空和视觉分析以及与NOAA相互作用AVHRR每日最佳内插全球SST（实际上提供标准SST产品）该建议即使图像中有云盖，所提出的基于SVR的算法也具有巨大的产生运营高分辨率云SST估计。

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