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Suspended Sediment Modeling
Research Team: Jason Hamel (M.S. student), Donald Taylor (M.S. student), Minsu Kim (Cornell University), Rolando Raqueño
Task Scope: Quantitative hyperspectral remote sensing of coastal and inland water resources has been hindered by inadequate performance of atmospheric compensation methods. The techniques used for oceanic conditions operate on the premise that there is negligible water leaving radiance in the near infrared region (NIR 750-950 [nm]) and any sensor reaching radiance is mainly due to the atmosphere. The problem stems from the abundance of suspended materials from anthropogenic and benthic sources in Case II waters not usually observed in the deep ocean. The scattering effects of suspended sediments greatly affect the water leaving radiance in the visible to the NIR region resulting in overestimation of the atmospheric contribution to the radiance reaching the sensor. In order to adapt current atmospheric compensation methodologies, a means of estimating the water leaving radiance in the NIR region needs to be devised. This work describes a set of numerical modeling tools that have been integrated to predict optical properties of suspended minerals and their effects on the water volume spectral reflectance.
Figure 1.3-1: Process flow of Suspended Sediment IOP Modeling
Task Status: The most recent development in this area is the extension of the suspended modeling efforts to actual atmospheric compensation. The area of research involving
Suspended Sediment IOP Modeling process is graphically summarized in Figure 1.3-1. Other information can be found here.
The atmospheric compensation work is dependent on suspended IOP modeling (utilizing Minsu Kim's OOPS model) results to address the quandary of estimating upwelling radiance in the NIR region due to significant suspended concentrations in Case II waters.
Figure 1.3-2a: Reflectance spectra of TSS only concentration
Figure 1.3-2b: Reflectance spectra of Lake Ontario, a low concentration water body
Figure 1.1.3-2c: Reflectance spectra of Long Pond, a high concentration water body
The Ocean Optical Phytoplankton Simulator (OOPS) is a suite of scattering codes tailored for hydrologic use. Given particle characteristics and size distributions, OOPS computes the associated IOP (scattering coefficients and scattering phase functions and absorption cross sections). Originally developed to predict phytoplankton optical properties based on taxa-specific internal cellular morphology, pigment composition and distribution, it uses T-matrix and Mie scattering codes to compute the IOPs for different descriptions of phytoplankton species in the 400-700 [nm] regime. This wavelength region matches the operating limits of field instrumentation where the effects of coloring agents dominate. OOPS generates these IOP descriptions in a format compatible with the HYDROLIGHT radiative transfer code. This allows material and optical property descriptions to be readily propagated into an estimate of volume spectral reflectance. In order to predict the effects of suspended sediments modifications were made to OOPS in order to extend the spectral range beyond 700 [nm] into the NIR in order to couple HYDOLIGHT predictions with atmospheric propagation codes such as MODTRAN. Five different mineral compositions resulting in 7 different refractive indices were used as input into OOPS along with ~21 different particle size distributions to generate a data set of varying total suspended solids (TSS) inherent optical properties. These were used as input into Hydrolight along with 5 different concentrations to generate over 700 reflectance spectra of water leaving radiance (c.f. Figures 1.3-2a, b, c).
The variability inherent in these data sets made typical statistical analysis of the results difficult. Instead, the spectra were arranged into a typical image cube (Figure 1.3-3) and ENVI's n-Dimensional Visualizer was used to analyze the spectral shape of these spectra (example in Figure 1.3-4). Spectral trends of the simulations are being analyzed using this visualization method.
Figure 1.3-3: Spectral data set built into a typical image cube for spectral analysi
Figure 1.3-4: n-Dimensional Visualizer of 3 concentration sets; Lake Ontario (green), Conesus Lake(fuchsia), and Long Pond (blue). This is a visualization of 3 spectral bands illustrating the common spectral character of the individual water bodies
