Remote Estimation of Sea Surface Nitrate in the California Current System From Satellite Ocean Color Measurements
Remote Estimation of Sea Surface Nitrate in the California Current System From Satellite Ocean Color Measurements
复制标题
通过卫星海洋颜色测量远程估算加州海流系统中的海面硝酸盐
DOI：
10.1109/tgrs.2021.3095099
复制
发表时间：
2021-07
影响因子：
8.2
通讯作者：
Chai Fei
中科院分区：
文献类型：
--
作者：
Yu Xiaolei;Chen Shuangling;Chai Fei
海面硝酸盐（SSN）是表征物理和生物地球化学过程，特别是量化海洋新初级生产的重要参数，但由于环境变量与SSN之间的复杂关系，其卫星遥感估算一直存在困难。在加利福尼亚洋流系统（CSCCS）的中部和南部，通过建模、验证和在不同海洋情景下的广泛测试来尝试这一挑战。具体而言，利用40年（1978-2018）多次巡航收集的大量SSN数据集和中分辨率成像光谱仪（MODIS）估算的海表温度（SST）和叶绿素-a (Chl)，建立了SSN的堆叠随机森林（SRF）模型，并在4 km的空间分辨率下进行了验证。模型总体表现为均方根差(RMSD) <inline-formula> < text -math notation="LaTeX"> $=0.83\,\,\mu $ </ text -math></inline-formula>mol/kg，决定系数<inline-formula> < text -math notation="LaTeX"> $R^{2}) =0.87$ </ text -math></inline-formula>，平均偏差<inline-formula> < text -math notation="LaTeX"> $= -0.11\,\,\mu $ </ text -math></inline-formula>mol/kg，SSN在0.05和<inline-formula> < text -math notation="LaTeX"> $19.90~\mu $ </ text -math></inline-formula>mol/kg （<inline-formula> < text -math notation="LaTeX"> $N =1034$ </ text -math></inline-formula>）之间的平均比值为1.15。此外，对模型原始参数化后的上升期、海期和冬季的试验均显示出满意的结果，总体RMSD为1.95 <inline-formula> < text -math notation="LaTeX"> $\mu $ </ text -math></inline-formula>mol/kg。SRF模型对MODIS海表温度和Chl不确定性的敏感性进行了检验，诱导的不确定性为<inline-formula> < text -math notation="LaTeX"> $\le 2.22~\mu $ </ text -math></inline-formula>mol/kg。广泛的评价和敏感性试验表明，SRF模型在CSCCS研究区估算SSN具有稳健性，一旦有足够的<斜体>原位</斜体> SSN数据可供模型校准，它可以作为其他地区的稳健性方法。
Sea surface nitrate (SSN) is an important parameter to characterize physical and biogeochemical processes, particularly to quantify oceanic new primary production, yet its remote estimation from satellite has been difficult due to the complex relationships between environmental variables and SSN. In the central and southern sections of the California Current System (CSCCS), this challenge is attempted through modeling, validation, and extensive tests in different oceanic scenarios. Specifically, using extensive SSN datasets collected by many cruises spanning 40 years (1978–2018) and Moderate Resolution Imaging Spectroradiometer (MODIS) estimated sea surface temperature (SST) and chlorophyll-a (Chl), a stacking random forest (SRF) model of SSN has been developed and validated with a spatial resolution of ~4 km. The model showed an overall performance of root mean square difference (RMSD) <inline-formula> <tex-math notation="LaTeX">$=0.83\,\,\mu $ </tex-math></inline-formula>mol/kg, with coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}) =0.87$ </tex-math></inline-formula>, mean bias <inline-formula> <tex-math notation="LaTeX">$= -0.11\,\,\mu $ </tex-math></inline-formula>mol/kg, and mean ratio = 1.15 for SSN ranging between 0.05 and <inline-formula> <tex-math notation="LaTeX">$19.90~\mu $ </tex-math></inline-formula>mol/kg (<inline-formula> <tex-math notation="LaTeX">$N =1034$ </tex-math></inline-formula>). Furthermore, tests of the model with its original parameterization for the upwelling period, oceanic period, and winter period all showed satisfactory performance with an overall RMSD of 1.95 <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>mol/kg. The sensitivity of the SRF model to uncertainties of MODIS SST and Chl was examined, with induced uncertainties of <inline-formula> <tex-math notation="LaTeX">$\le 2.22~\mu $ </tex-math></inline-formula>mol/kg. The extensive evaluation and sensitivity tests indicated the robustness of the SRF model in estimating SSN in the study area of the CSCCS, and it could serve as a robust approach for other regions once sufficient <italic>in situ</italic> SSN data are available for model calibration.
登录
查看更多内容
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
Florian Bruns;S. Knust;N. V. Shakhlevich
通讯作者：
Florian Bruns;S. Knust;N. V. Shakhlevich
影响因子：
16.6
作者：
Rafter PA;Sigman DM;Mackey KRM
通讯作者：
Mackey KRM
影响因子：
64.8
作者：
PAULY, D;CHRISTENSEN, V
通讯作者：
CHRISTENSEN, V
影响因子：
1.9
作者：
I. Ioannou;A. Gilerson;B. Gross;F. Moshary;Samir A. Ahmed
通讯作者：
I. Ioannou;A. Gilerson;B. Gross;F. Moshary;Samir A. Ahmed
影响因子：
4.1
作者：
Chavez, FP;Pennington, JT;Collins, CA
通讯作者：
Collins, CA