FN Archimer Export Format
PT J
TI Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning
BT 
AF JIANG, Zhiting
   SONG, Zigeng
   BAI, Yan
   HE, Xianqiang
   YU, Shujie
   ZHANG, Siqi
   GONG, Fang
AS 1:1;2:2,3;3:1,2,4;4:1,2,5;5:2,5;6:2;7:2,6;
FF 1:;2:;3:;4:;5:;6:;7:;
C1 Shanghai Jiao Tong Univ, Sch Oceanog, Shanghai 200030, Peoples R China.
   State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
   College of Oceanography, Hohai University, Nanjing 210098, China
   Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
   Ocean College, Zhejiang University, Zhoushan 316000, China
   National Earth System Science Data Center, Beijing 100101, China
C2 UNIV SHANGHAI JIAO TONG, CHINA
   MINIST NAT RESOURCES, CHINA
   UNIV HOHAI, CHINA
   SOUTHERN MARINE SCI & ENGN GUANGDONG LAB GUANGZHO, CHINA
   UNIV ZHEJIANG, CHINA
   NATL EARTH SYST SCI DATA CTR, CHINA
IN DOAJ
IF 5
TC 5
UR https://archimer.ifremer.fr/doc/00775/88660/94366.pdf
LA English
DT Article
CR OISO - OCÉAN INDIEN SERVICE D'OBSERVATION
DE ;random forest model;global sea surface pH;remote sensing inversion;total alkalinity;carbonate system
AB Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO(2)) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO(2) and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R-2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R-2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST <= +/- 0.5 degrees C and Chla <= +/- 20%; RMSESST <= 0.011 and RMSEChla <= 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25 degrees x 0.25 degrees were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO(2) products to understand the carbonate system change and the ocean ecological environments responding to the global change. 
PY 2022
PD MAY
SO Remote Sensing
SN 2072-4292
PU Mdpi
VL 14
IS 10
UT 000802567800001
DI 10.3390/rs14102366
ID 88660
ER

EF