Estimating primary production at depth from remote sensing.
Estimating primary production at depth from remote sensing.
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DOI：
10.1364/ao.35.000463
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发表时间：
1996-01
期刊：
影响因子：
1.9
通讯作者：
Z. Lee;K. Carder;J. Marra;R. Steward;M. Perry
中科院分区：
文献类型：
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作者：
Z. Lee;K. Carder;J. Marra;R. Steward;M. Perry
利用一个通用的初级生产力模型和相同的光合参数，采用4种不同的方法计算了高纬度北大西洋沃茨深层的量子（Q）和初级生产力（P）。这四种方法之间的差异与上层水柱中色素信息的使用有关。方法1和方法2使用色素生物量（B）作为输入，并使用K（d）（漫衰减系数）和B之间的亚热带经验关系来估计深度Q。方法1使用测量的B，但方法2使用从海岸带彩色扫描仪（亚热带算法）获得的B作为输入。方法3和4使用浮游植物吸收系数（a（ph））而不是B作为输入，并且方法B使用经验得出的a（ph）（440）和K（d）值，并且方法4使用基于与方法2相同的远程测量的分析得出的a（ph）（440）和a（总吸收系数）值。当比较Q（z）和P（z）的计算值和测量值时，方法4提供了最接近的结果[对于P（z），r（2）= 0.95（n = 24），对于Q（z），r（2）= 0.92（n = 11）]。方法1产生最差结果[对于P（z），r（2）= 0.56，对于Q（z），r（2）= 0.81]。这些结果表明，在远程估计的P的最大的不确定性之一，可以来自一个潜在的不匹配的颜料特定的吸收系数（a（ph）*），这是需要隐含在当前的模型或算法的基础上B。我们指出，这种潜在的不匹配可以避免，如果我们安排的模型或算法，使他们基于色素吸收系数（a（ph））。因此，除了光合参数和地表以上光强度的准确性之外，P的远程估计的准确性取决于a（ph）可以被估计得多准确，而不是B可以被估计得多准确。此外，方法来获得（ph）的经验和分析从遥感数据进行了介绍。奇怪的是，将B和K（d）的亚热带算法联合应用于亚北极沃茨，显然在一定程度上补偿了由于它们在计算Q（z）时具有相似和隐含的特定色素吸收系数而产生的影响。
By use of a common primary-production model and identical photosynthetic parameters, four different methods were used to calculate quanta (Q) and primary production (P) at depth for a study of high-latitude North Atlantic waters. The differences among the four methods relate to the use of pigment information in the upper water column. Methods 1 and 2 use pigment biomass (B) as an input and a subtropical, empirical relation between K(d) (diffuse attenuation coefficient) and B to estimate Q at depth. Method 1 uses measured B, but Method 2 uses B derived from the Coastal Zone Color Scanner (subtropical algorithm) as inputs. Methods 3 and 4 use the phytoplankton absorption coefficient (a(ph)) instead of B as input, and Method B uses empirically derived a(ph)(440) and K(d) values, and Method 4 uses analytically derived a(ph)(440) and a (total absorption coefficient) values based on the same remote measurements as Method 2. When the calculated and the measured values of Q(z) and P(z) were compared, Method 4 provided the closest results [for P(z), r(2) = 0.95 (n = 24), and for Q(z), r(2) = 0.92 (n = 11)]. Method 1 yielded the worst results [for P(z), r(2) = 0.56 and for Q(z), r(2) = 0.81]. These results indicate that one of the greatest uncertainties in the remote estimation of P can come from a potential mismatch of the pigment-specific absorption coefficient (a(ph)*), which is needed implicitly in current models or algorithms based on B. We point out that this potential mismatch can be avoided if we arrange the models or algorithms so that they are based on the pigment absorption coefficient (a(ph)). Thus, except for the accuracy of the photosynthetic parameters and the above-surface light intensity, the accuracy of the remote estimation of P depends on how accurately a(ph) can be estimated, but not how accurately B can be estimated. Also, methods to derive a(ph) empirically and analytically from remotely sensed data are introduced. Curiously, combined application of subtropical algorithms for both B and K(d) to subarctic waters apparently compensates to some extent for effects that are due to their similar and implicit pigment-specific absorption coefficients for the calculation of Q(z).