| TAO Zui,MA Sheng,YANG Xiaofeng,WANG Yan. 2017. Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity. Acta Oceanologica Sinica, 36(6):112－121 |
| Assessing the uncertainties of phytoplankton absorption-based model estimates of marine net primary productivity |
| 基于浮游植物吸收的海洋初级生产力模型的不确定性分析 |
| Received:April 08, 2016 Revised:July 28, 2016 |
| DOI：10.1007/s13131-017-1047-8 |
| Key words:marine net primary production phytoplankton pigment absorption satellite remote sensing uncertainty analysis Monte Carlo simulation |
| 中文关键词: 海洋初级生产力 浮游植物吸收 卫星遥感 不确定性 蒙特卡洛模拟 |
| 基金项目:The National Natural Science Fundation of China under contract No. 41501389; the Foundation of State Key Laboratory of Remote Sensing Science in China under contract No. OFSLRSS201509. |
| Author Name | Affiliation | E-mail | | TAO Zui | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | MA Sheng | Beijing North-Star Digital Remote Sensing Technology Co. Ltd., Beijing 100120, China | masheng_1987@163.com | | YANG Xiaofeng | State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China | | | WANG Yan | State Environmental Protection Key Laboratory of Numerical Modeling for Environment Impact Assessment, Beijing 100012, China | |
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| Abstract: |
| Satellite-derived phytoplankton pigment absorption (aph) has been used as a key predictor of phytoplankton photosynthetic efficiency to estimate global ocean net primary production (NPP). In this study, an aph-based NPP model (AbPM) with four input parameters including the photosynthetically available radiation (PAR), diffuse attenuation at 490 nm (Kd(490)), euphotic zone depth (Zeu) and the phytoplankton pigment absorption coefficient (aph) is compared with the chlorophyll-based model and carbon-based model. It is found that the AbPM has significant advantages on the ocean NPP estimation compared with the chlorophyll-based model and carbon-based model. For example, AbPM greatly outperformed the other two models at most monitoring sites and had the best accuracy, including the smallest values of RMSD and bias for the NPP estimate, and the best correlation between the observations and the modeled NPPs. In order to ensure the robustness of the model, the uncertainty in NPP estimates of the AbPM was assessed using a Monte Carlo simulation. At first, the frequency histograms of simple difference (δ), and logarithmic difference (δLOG) between model estimates and in situ data confirm that the two input parameters (Zeu and PAR) approximate the Normal Distribution, and another two input parameters (aph and Kd(490)) approximate the logarithmic Normal Distribution. Second, the uncertainty in NPP estimates in the AbPM was assessed by using the Monte Carlo simulation. Here both the PB (percentage bias), defined as the ratio of △NPP to the retrieved NPP, and the CV (coefficient of variation), defined as the ratio of the standard deviation to the mean are used to indicate the uncertainty in the NPP brought by input parameter to AbPM model. The uncertainty related to magnitude is denoted by PB and the uncertainty related to scatter range is denoted by CV. Our investigations demonstrate that PB of NPP uncertainty brought by all parameters with an annual mean of 5.5% covered a range of -5%-15% for the global ocean. The PB uncertainty of AbPM model was mainly caused by aph; the PB of NPP uncertainty brought by aph had an annual mean of 4.1% for the global ocean. The CV brought by all the parameters with an annual mean of 105% covered a range of 98%-134% for global ocean. For the coastal zone of Antarctica with higher productivity, the PB and CV of NPP uncertainty brought by all parameters had annual means of 7.1% and 121%, respectively, which are significantly larger than those obtained in the global ocean. This study suggests that the NPPs estimated by AbPM model are more accurate than others, but the magnitude and scatter range of NPP errors brought by input parameter to AbPM model could not be neglected, especially in the coastal area with high productivity. So the improving accuracy of satellite retrieval of input parameters should be necessary. The investigation also confirmed that the SST related correction is effective for improving the model accuracy in low temperature condition. |
| 中文摘要: |
| 基于卫星数据反演获取的浮游植物吸收系数（aph）被认为是反映浮游植物光合作用效率的关键指标，常用于全球海洋初级生产力的定量反演。本文选择以光合有效辐射、490nm水体漫衰减系数、真光层厚度和浮游植物吸收系数这四种遥感反演产品为输入参数的光吸收模型（AbPM）为研究对象，与叶绿素基模型及碳基模型进行了对比分析。相对其他两种模型，AbPM模型有着其自身的优势。对比其他两个模型，AbPM模型反演的初级生产力具有更高的精度，包括在大多数实测站点取得了最小的RMSD和bias值，与实测数据相关性也更好。为了验证模型的鲁棒性，蒙特卡洛方法被用于模型的不确定性分析。首先，利用四种参数对应的现场实测数据，计算各参数误差，根据其误差分布选择最合适的误差表达方式。结果显示，真光层深度（Zeu）与光合有效辐射（PAR）数据的遥感反演误差的最优表达方式为线性绝对误差，而浮游植物吸收系数（aph）和水体490nm漫衰减系数（Kd（490））最有误差表达方式为对数误差。接着，利用蒙特卡洛方法模拟计算AbPM模型在反演NPP中的不确定性。这里，百分比偏差（PB）和变异系数（CV）用于评估模型输入参数给模型带来的不确定性。其中，PB用于描述不确定性的量级，CV用于描述不确定性的发散程度。根据我们的研究，在全球初级生产力的反演结果中，四种输入参数带来的PB范围在-5%~15%之间，年平均PB为5.5%；CV范围则在98%~134%之间，年平均CV为105%。而对于南极圈附近高生产力海域来说，年平均PB和年平均CV分别为7.1%和121%，显著高于全球的平均水平。本文研究表明，AbPM模型反演初级生产力具有较高的精度。但输入参数的不确定性给模型带来的影响不可忽略，尤其是在近海和高生产力海域。因此提升输入参数产品的卫星遥感反演精度是必不可少的。另外，研究还确认了对模型进行海温修正有助于提升模型在低温海域的精度。 |
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