Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates
Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates
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DOI：
10.1002/2015gb005314
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发表时间：
2016-05-01
影响因子：
5.2
通讯作者：
Cassar, Nicolas
中科院分区：
文献类型：
--
作者：
Li, Zuchuan;Cassar, Nicolas
我们提出了两种统计算法，用于根据卫星观测预测全球海洋网络群落生产（NCP）。为了校准这两种算法，我们编制了原位 O-2/Ar-NCP 和遥感观测的大型数据集，包括海面温度 (SST)、净初级生产力 (NPP)、浮游植物大小组成和固有光学特性。第一种算法基于遗传规划 (GP)，它同时搜索 NCP 方程的最优形式和系数。我们发现几个 GP 解与 NPP 和 SST 是 NCP 的强预测因子一致。第二种算法使用支持向量回归 (SVR) 来优化 O-2/Ar-NCP 测量值和卫星观测值之间的数值关系。两种统计算法都能较好地预测NCP，GP的决定系数（R-2）为0.68，SVR的决定系数为0.72，与文献中的其他算法相当。然而，我们的新算法预测世界海洋的年度 NCP 分布在空间上更加均匀，南大洋和五个寡营养环流的年度 NCP 值更高。
We present two statistical algorithms for predicting global oceanic net community production (NCP) from satellite observations. To calibrate these two algorithms, we compiled a large data set of in situ O-2/Ar-NCP and remotely sensed observations, including sea surface temperature (SST), net primary production (NPP), phytoplankton size composition, and inherent optical properties. The first algorithm is based on genetic programming (GP) which simultaneously searches for the optimal form and coefficients of NCP equations. We find that several GP solutions are consistent with NPP and SST being strong predictors of NCP. The second algorithm uses support vector regression (SVR) to optimize a numerical relationship between O-2/Ar-NCP measurements and satellite observations. Both statistical algorithms can predict NCP relatively well, with a coefficient of determination (R-2) of 0.68 for GP and 0.72 for SVR, which is comparable to other algorithms in the literature. However, our new algorithms predict more spatially uniform annual NCP distribution for the world's oceans and higher annual NCP values in the Southern Ocean and the five oligotrophic gyres.