Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning

• Published in: Remote Sensing
• Main Authors: Zhiting Jiang, Zigeng Song, Yan Bai, Xianqiang He, Shujie Yu, Siqi Zhang, Fang Gong
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: random forest model; global sea surface pH; remote sensing inversion; total alkalinity; carbonate system; Science; Q; Ocean acidification
• Online Access: https://doi.org/10.3390/rs14102366; https://doaj.org/article/54207f813c2545f4a727f630aa275ac4

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 ( p CO 2 ) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway p CO 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 °C and Chla ≤ ±20%; RMSE SST ≤ 0.011 and RMSE Chla ≤ 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° × 0.25° 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 ...