文献类型：期刊文献

英文题名：Remote sensing estimates of global sea surface nitrate: Methodology and validation

作者：Zhong, Aifen[1];Wang, Difeng[1,2,3];Gong, Fang[1,2,3];Zhu, Weidong[4];Fu, Dongyang[5];Zheng, Zhuoqi[1,6];Huang, Jingjing[1,7];He, Xianqiang[1,2];Bai, Yan[1,2]

机构：[1]Minist Nat Resources Peoples Republ China, Inst Oceanog 2, State Key Lab Satellite Ocean Environm Dynam, Hangzhou 310012, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Guangzhou, Guangzhou 511458, Peoples R China;[3]Minist Nat Resources, Daya Bay Observat & Res Stn Marine Risks & Hazards, Hangzhou 310012, Peoples R China;[4]Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China;[5]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[6]Nanjing Univ, Geog & Ocean Sci Coll, Nanjing 210023, Peoples R China;[7]Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China

年份：2024

卷号：950

外文期刊名：SCIENCE OF THE TOTAL ENVIRONMENT

收录：SCI-EXPANDED(收录号：WOS:001295627900001)、、EI(收录号：20243316875914)、Scopus(收录号：2-s2.0-85201008656)、WOS

基金：This research was funded by the National Key Research and Devel-opment Program of China under Grant Nos. 2022YFC3104901, 2018YFB0505005 and 2017YFC1405300; the Key Research and Development Plan of Zhejiang Province under Contract No. 2017C03037; the National Natural Science Foundation of China under Contract No. 41476157.

语种：英文

外文关键词：Sea surface nitrate (SSN); Remote sensing inversion; World Ocean Atlas 2018 (WOA18); Photosynthetically active radiation (PAR)

外文摘要：Information about sea surface nitrate (SSN) concentrations is crucial for estimating oceanic new productivity and for carbon cycle studies. Due to the absence of optical properties in SSN and the intricate relationships with environmental factors affecting spatiotemporal dynamics, developing a more representative and widely applicable remote sensing inversion algorithm for SSN is challenging. Most methods for the remote estimation of SSN are based on data-driven neural networks or deep learning and lack mechanistic descriptions. Since fitting functions between the SSN and sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll (Chl) content have been established for the open ocean, it is important to include the remote sensing indicator photosynthetically active radiation (PAR), which is critical in nitrate biogeochemical processes. In this study, we employed an algorithm for estimating the monthly average SSN on a global 1 degrees degrees by 1 degrees degrees resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1 degrees degrees x 1 degrees degrees grid and the estimated monthly SST and PAR datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and MLD from the Hybrid Coordinate Ocean Model (HYCOM). These results indicated that PAR potentially affects SSN. Furthermore, validation of the SSN model with measured nitrate data from different months and locations for the years 2018-2023 yielded a high prediction accuracy (N N = 12,846, R2 2 = 0.93, root mean square difference (RMSE) = 3.12 mu mol/L, and mean absolute error (MAE) = 2.22 mu mol/L). Further independent validation and sensitivity tests demonstrated the validity of the algorithm for retrieving SSN.