Visible-infrared remote-sensing model and applications for ocean waters. Ph.D. Thesis
Visible-infrared remote-sensing model and applications for ocean waters. Ph.D. Thesis
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发表时间：
1994-12
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通讯作者：
Z. Lee
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作者：
Z. Lee
遥感在海洋科学中，特别是在涉及大空间尺度的研究中已变得非常重要。为了通过卫星或飞机进行的遥感来估计水中成分，从海面下发射的信号，即所谓的离水辐射（L(w)）是最重要的。L(w)的大小取决于两项：一项是太阳输入的强度，另一项是水中成分的反射率。海面上方的离水辐射与下沉辐射（E(d)）之比（遥感反射率R(sub rs)）与入射辐射强度无关，主要是水中成分光学性质的函数。在此工作中，建立了一个模型来解释可见光-红外范围内海水的r（sub rs）。除了分子和粒子散射辐射的术语外，该模型还包括描述底部反射率、gelbstoff或彩色溶解有机物（CDOM）的荧光和水拉曼散射的术语。通过使用该模型，可以很好地解释从西佛罗里达陆架到密西西比河羽流的水体R(sub rs)，覆盖（叶绿素a浓度）0.07 - 50 mg/cu m的范围。实测R（sub rs）和模拟R（sub rs）之间的平均百分比差（a.p.d.）为3.4%，对于浅水，模型要求的水深在图表深度的10%以内。对于浮游植物色素吸收系数（a(sub theta)），建议使用R（sub rs）模型进行简单的数学模拟。R（sub rs）的反问题，即仅从R（sub rs）数据解析导出水中成分的问题，可以使用a（sub θ）函数来解决，而无需事先了解水中光学性质。更重要的是，这种方法避免了需要了解叶绿素特定吸收系数的形状和值的问题。模拟测试了广泛的水类型，包括来自蒙特利湾，西佛罗里达大陆架和密西西比河羽流的水。通过模拟，R（sub rs）导出的水中吸收系数与水中测量值一致（R（exp 2）大于0.94，斜率约为1.0）。在遥感应用中，提出了一种基于遥感估算初级产量的新方法。使用该方法，基于遥感数据计算的初级产量（PP）值与光区的实测值非常接近（r(exp 2) = 0.95，斜率为1.26，平均差值为32%），而传统的基于颜料的PP模型提供的值仅为实测值的三分之一。这表明有可能大大提高基于遥感的初级生产估计的准确性。
Remote sensing has become important in the ocean sciences, especially for research involving large spatial scales. To estimate the in-water constituents through remote sensing, whether carried out by satellite or airplane, the signal emitted from beneath the sea surface, the so called water-leaving radiance (L(w)), is of prime importance. The magnitude of L(w) depends on two terms: one is the intensity of the solar input, and the other is the reflectance of the in-water constituents. The ratio of the water-leaving radiance to the downwelling irradiance (E(d)) above the sear surface (remote-sensing reflectance, R(sub rs)) is independent of the intensity of the irradiance input, and is largely a function of the optical properties of the in-water constituents. In this work, a model is developed to interpret r(sub rs) for ocean water in the visible-infrared range. In addition to terms for the radiance scattered from molecules and particles, the model includes terms that describe contributions from bottom reflectance, fluorescence of gelbstoff or colored dissolved organic matter (CDOM), and water Raman scattering. By using this model, the measured R(sub rs) of waters from the West Florida Shelf to the Mississippi River plume, which covered a (concentration of chlorophyll a) range of 0.07 - 50 mg/cu m, were well interpreted. The average percentage difference (a.p.d.) between the measured and modeled R(sub rs) is 3.4%, and, for the shallow waters, the model-required water depth is within 10% of the chart depth. Simple mathematical simulations for the phytoplankton pigment absorption coefficient (a(sub theta)) are suggested for using the R(sub rs) model. The inverse problem of R(sub rs), which is to analytically derive the in-water constituents from R(sub rs) data alone, can be solved using the a(sub theta) functions without prior knowledge of the in-water optical properties. More importantly, this method avoids problems associated with a need for knowledge of the shape and value of the chlorophyll-specific absorption coefficient. The simulation was tested for a wide range of water types, including waters from Monterey Bay, the West Florida Shelf, and the Mississippi River plume. Using the simulation, the R(sub rs)-derived in-water absorption coefficients were consistent with the values from in-water measurements (r(exp 2) greater than 0.94, slope approximately 1.0). In the remote-sensing applications, a new approach is suggested for the estimation of primary production based on remote sensing. Using this approach, the calculated primary production (PP) values based upon remotely sensed data were very close to the measured values for the euphotic zone (r(exp 2) = 0.95, slope 1.26, and 32% average difference), while traditional, pigment-based PP model provided values only one-third the size of the measured data. This indicates a potential to significantly improve the accuracy of the estimation of primary production based upon remote sensing.