Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea
Remote estimation of biomass of Ulva prolifera macroalgae in the Yellow Sea
复制标题
黄海石莼大型藻类生物量的遥测
DOI：
10.1016/j.rse.2017.01.037
复制
发表时间：
2017-04-01
影响因子：
13.5
通讯作者：
He, Ming-Xia
中科院分区：
文献类型：
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作者：
Hu, Lianbo;Hu, Chuanmin;He, Ming-Xia
自2008年以来，黄海每年夏季都会发生增殖石莼（Ulva prolifera）水华（又称绿色潮），造成严重的环境和经济问题。一些研究利用卫星观测，通过估计水华的规模和持续时间来估计水华的严重程度。然而，一个关键的水华参数，即生物量，从来没有被客观地确定，由于缺乏测量。在这项研究中，进行了实验室实验，以测量U。通过测量每单位面积的浮游藻类生物量（湿重）和相应的光谱反射率，建立了一个可靠的关系，将每单位面积的生物量与基于反射率的浮游藻类指数（FAI）联系起来。基于实验室的模型已通过现场测量得到验证，对于FAI值< 0.2的藻类（相当于2 Icg/m2生物量，占卫星图像中含藻类像素的99.5%以上），估计相对不确定性< 16%。该模型被进一步转移到中分辨率成像光谱仪瑞利校正反射率（R-rc），气溶胶对模型的影响在各种大气条件下进行了模拟。模拟结果表明，平均6.5%（高达12.3%的极端情况下）的生物量估计的不确定性时，MODIS R-RC数据被用作模型输入。干生物量/湿生物量和碳，氮含量/干生物量也通过实验室实验，从而使他们的估计可能从MODIS R-RC数据。然后将该模型应用于2008年至2015年期间YS的MODIS观测时间序列，以确定这些关键参数的年际变化。结果显示，2015年6月期间最大日生物量> 170万吨，2012年期间最小日生物量< 0.09万吨。估计U.利用近实时中分辨率成像分光仪图像对特定地点的增殖藻生物量进行监测，预计将大大提高现有监测系统为决策提供定量信息的能力。2017作者(C)爱思唯尔公司出版
Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance, through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of < 16% for algae with FAI values < 0.2 (corresponding to 2 Icg/m(2) biomass and accounting for > 99.5% of the algae-containing pixels in satellite images). The model was further transferred to MODIS Rayleigh-corrected reflectance (R-rc), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS R-rc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS R-rc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of < 0.09 million tons during 2012. The ability to estimate U. prolifera biomass at given locations from the near real-time MODIS images is expected to significantly enhance the capacity of an existing monitoring system to provide quantitative information for decision making. 2017 The Authors. (C) Published by Elsevier Inc.