The particulate nitrogen (PN) isotopic composition (δ15NPN) plays an important role in quantifying the contribution rate of particulate organic matter sources and indicating water environmental pollution. Estimation of δ15NPN from satellite images can provide significant spatiotemporal continuous data for nitrogen cycling and ecological environment governance. Here, in order to fully understand spatiotemporal dynamic of δ15NPN, we have developed a machine learning algorithm for retrieving δ15NPN. This is a successful case of combining nitrogen isotopes and remote sensing technology. Based on the field observation data of Zhanjiang Bay in May and September 2016, three machine learning retrieval models (Back Propagation Neural Network, Random Forest and Multiple Linear Regression) were constructed using optical indicators composed of in situ remote sensing reflectance as input variable and δ15NPN as output variable. Through comparative analysis, it was found that the Back Propagation Neural Network (BPNN) model had the better retrieval performance. The BPNN model was applied to the quasi-synchronous Ocean and Land Color Imager (OLCI) data onboard Sentinel-3. The determination coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) of satellite-ground matching point data based on the BPNN model were 0.63, 1.63‰, and 20.10%, respectively. From the satellite retrieval results, it can be inferred that the retrieval value of δ15NPN had good consistency with the measured value of δ15NPN. In addition, independent datasets were used to validate the BPNN model, which showed good accuracy in δ15NPN retrieval, indicating that an effective model for retrieving δ15NPN has been built based on machine learning algorithm. However, to enhance machine learning algorithm performance, we need to strengthen the information collection covering diverse coastal water bodies and optimize the input variables of optical indicators. This study provides important technical support for large-scale and long-term understanding of the biogeochemical processes of particulate organic matter, as well as a new management strategy for water quality and environmental monitoring.

中文翻译：

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基于机器学习算法的Sentinel-3 OLCI数据遥感估算湛江湾δ15NPN

颗粒氮 (PN) 同位素组成 (δ15氮PN）对于量化颗粒物有机物来源贡献率、指示水环境污染具有重要作用。 δ 的估计15氮PN卫星图像可以为氮循环和生态环境治理提供重要的时空连续数据。在这里，为了充分了解δ的时空动态15氮PN，我们开发了一种机器学习算法来检索 δ15氮PN。这是氮同位素与遥感技术结合的成功案例。基于2016年5月和9月湛江湾的实地观测数据，以原位遥感反射率作为输入变量，δ组成的光学指标构建了三种机器学习检索模型（反向传播神经网络、随机森林和多元线性回归）。15氮PN作为输出变量。通过对比分析发现，反向传播神经网络（BPNN）模型具有更好的检索性能。 BPNN 模型应用于 Sentinel-3 上的准同步海洋和陆地彩色成像仪 (OLCI) 数据。决定系数（R2基于BPNN模型的星地匹配点数据的均方根误差(RMSE)和平均绝对百分比误差(MAPE)分别为0.63%、1.63%和20.10%。从卫星反演结果可以推断δ的反演值15氮PN与δ测量值具有良好的一致性15氮PN。此外，使用独立数据集来验证BPNN模型，在δ方面表现出良好的准确性15氮PN检索，表明检索 δ 的有效模型15氮PN是基于机器学习算法构建的。然而，为了增强机器学习算法的性能，我们需要加强对沿海多样化水体的信息采集，并优化光学指标的输入变量。该研究为大规模、长期了解颗粒有机物的生物地球化学过程提供了重要的技术支撑，也为水质和环境监测提供了新的管理策略。