阿尔戈
卫星
海面温度
均方误差
算法
温盐度图
遥感
经度
计算机科学
纬度
气象学
盐度
数学
环境科学
地质学
地理
大地测量学
气候学
统计
海洋学
物理
天文
作者
Qifan Hu,Xiaoyan Chen,Yan Bai,Xianqiang He,Teng Li,Delu Pan
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-13
被引量:2
标识
DOI:10.1109/tgrs.2022.3233385
摘要
We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll $a$ (Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with $R^{2} = 0.962$ , root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.
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