CHLTSSCDO M Suspended Sediment Only Lake Ontario Long Pond Lake Conesus Genesee River Plume APPROACH and PROCESS The Ocean Optical Phytoplankton Simulator (OOPS) (Kim, 2000) 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 inherent optical properties (IOPs) for different descriptions of phytoplankton species in the [nm] regime. This wavelength region matches the operating limits of field in-water optical instrumentation where the effects of coloring agents dominate. OOPS generates these IOP descriptions in a format compatible with the HYDROLIGHT radiative transfer code (Mobley, 1994). 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, however, 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 (Berk, et al. 1999). The main inputs into the OOPS model are the particle material composition, refractive index, and particle size distribution. To thoroughly exercise the model and define the extent to which the NIR reflectance is affected by these factors, a set of measurements from the literature was gathered for each of these parameters. Our analysis began by selecting categories of suspended minerals to group material characteristics in terms material density and a refractive index. The selection was based on studies by Eisma (1993) to sample a representative range of suspended mineral characteristics found in coastal and estuarine environments. After specifying particle material type and refractive indices (1), the critical particle property of particle size distribution was defined (2). Three major types, Junge, Log-normal, and Gaussian, were used in this analysis. Junge distributions were set to 14 slope and size range parameter levels to represent Case I waters (Simpson, 1982) while Log-normal distributions (7 levels) and Gaussian (2 levels) were used to represent Case II coastal and estuarine waters. Given all the defined particle properties, OOPS can compute a library of IOPs (3) to be used as suspended sediment inputs into HYDROLIGHT. One of the side benefits of the OOPS generated IOPs is the complete definition of absorption and scattering cross-sections (4) and the scattering phase functions (5). Digital Imaging and Remote Sensing Laboratory Center for Imaging Science Rochester, NY ~dirs/ Model-based estimation of suspended mineral inherent optical properties and volume spectral reflectance for atmospheric compensation of spectral imagery in Case II waters Varying CDOM concentrations (red, cyan, and violet vectors) for two different CHL concentrations (dotted and dashed vectors) Varying CHL concentrations (solid, dotted and dashed vectors) for two different CDOM concentrations (red and cyan vectors) Varying TSS concentrations for two different CDOM concentrations Three main vectors from the synthetic spectral data set Jason Hamel, Rolando Raqueno, John Schott CompositionRefractive Index Quartz1.544 Albite1.527 Kaolinite1.549 Calcite1.486/1.658/Spectral Opal INTRODUCTION Quantitative hyperspectral remote sensing of coastal and inland water resources has been hindered by inadequate performance of atmospheric compensation methods. These techniques used for oceanic conditions operate on the premise that there is negligible water leaving radiance in the near infrared region (NIR [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; a condition not usually observed in the deep ocean. The scattering effects of suspended sediments greatly affect the water leaving radiance in 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. We outline the experimental inputs into these models and highlight preliminary results that show the potential to improve Case II atmospheric compensation. To complete the simulation effects of these sediment properties, constituent concentrations were defined as additional inputs into HYDROLIGHT (6). A grouping of constituent concentrations were chosen to represent a range of water types that represent an ideal case and cases that have been observed in field samplings near the Rochester Embayment and Finger Lakes region (New York, USA). Using these constituents and the OOPS generated IOPs (7), the final output from Hydrolight is the water leaving radiance just above the surface of the water body and was converted into surface water reflectance (8). As can easily be seen in the TSS only and Long Pond cases, there can be significant reflectance in the NIR spectral region for certain TSS IOP combinations. Water Concentrations OOPS DISCUSSION The statistical analysis of the water surface reflectance did produce separability criteria that could distinguish between the concentrations of CHL, TSS, and CDOM, but could not separate the effect of the varying TSS IOPs like refractive index or particle size distribution. The spectral features of the data set were then examined in ENVI’s n- Dimensional Visualizer by plotting the grey values of different bands on different axes of a plot. For real imagery (A), all the associated ground, atmospheric and sensor variability produces clouds of pixels (B) and the different regions of the plot can often be separated into various features within the image itself (C). While our synthetically generated spectral data set (D) can be analyzed in a similar manner, it does not contain all this natural variability and produces more linear features (E) that can be used to analyze trends for what was varied (F); TSS refractive index, particle size distribution, and concentrations of CHL, TSS, and CDOM. However, the use of this visualization of simulated data provides important insight into spectral trends of constituent effects. 4. REFERENCES A. Berk, G. Anderson, P. Acharya, J. Chetwynd, L. Bernstein, E. Shettle, M. Matthew, and S. Adler-Golden. MODTRAN4 USER’S MANUAL. HANSCOM AFB, MA , June Eisma,D. Suspended Matter in the Aquatic Environment. Springer-Verlag, Kim, M. Investigation of the effects of natural variations of phytoplankton on ocean color. PhD thesis, Cornell University, Ithaca, NY, Mobley, C. Light and Water: Radiative Transfer in Natural Waters, Academic Press, Boston, MA, ISBN Simpson, W. R. Particulate Matter in the Oceans-Sampling Methods, Concentration, Size Distribution, and Particle Dynamics. Oceanography and Marine Biology, 20: , The magnitude of the vectors seen in the plots are created by varying TSS component refractive indices, particle size distributions, and concentration. These effects are not separable and two of them must be known to be able to quantify the third. 2.CHL and CDOM concentration control the direction of the spectral vectors. Analyzing this spectral space resulted in several trends that can be used to better quantify the various particle components within the water body. Real ImagerySynthetically Generated “Imagery” Junges Log-Normals UFI Measured TSS Only Lake Ontario Genesee River Plume Conesus Lake Long Pond TSS Only Lake Ontario Genesee River Plume Conesus Lake Long Pond A B C D E F