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Dive into the research topics where Zhongping Lee is active.

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Featured researches published by Zhongping Lee.


Applied Optics | 2002

Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters.

Zhongping Lee; Kendall L. Carder; Robert Arnone

For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.


Applied Optics | 1999

Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization

Zhongping Lee; Kendall L. Carder; Curtis D. Mobley; Robert G. Steward; Jennifer S. Patch

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.


Applied Optics | 1998

Hyperspectral remote sensing for shallow waters. I. A semianalytical model.

Zhongping Lee; Kendall L. Carder; Curtis D. Mobley; Robert G. Steward; Jennifer S. Patch

For analytical or semianalytical retrieval of shallow-water bathymetry and/or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance (K(d)), one for the upwelling radiance of the water column (K(u)(C)), and one for the upwelling radiance from bottom reflection (K(u)(B)). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above (R(rs)) and below (r(rs)) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r(rs) of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption (a) and backscattering (b(b)). For remote-sensing inversion, parameters connecting R(rs) and r(rs) are also derived. It is found that r(rs) values determined by the SA model agree well with the exact values computed by Hydrolight (~3% error), even for Hydrolight r(rs) values calculated with different particle phase functions. The Hydrolight calculations included b(b)/a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.


Applied Optics | 1994

Model for the interpretation of hyperspectral remote-sensing reflectance

Zhongping Lee; Kendall L. Carder; Steve K. Hawes; Robert G. Steward; Thomas G. Peacock; Curtiss O. Davis

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/m(3) and gelbstoff absorption at 440 nm from 0.02-0.4 m(-1). Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.


Applied Optics | 1996

METHOD TO DERIVE OCEAN ABSORPTION COEFFICIENTS FROM REMOTE-SENSING REFLECTANCE

Zhongping Lee; Kendall L. Carder; Thomas G. Peacock; Curtiss O. Davis; James L. Mueller

A method to derive in-water absorption coefficients from total remote-sensing reflectance (ratio of the upwelling radiance to the downwelling irradiance above the surface) analytically is presented. For measurements made in the Gulf of Mexico and Monterey Bay, with concentrations of chlorophyll-a ranging from 0.07 to 50 mg/m(3), comparisons are made for the total absorption coefficients derived with the suggested method and those derived with diffuse attenuation coefficients. For these coastal to open-ocean waters, including regions of upwelling and the Loop Current, the results are as follows: at 440 nm the difference between the two methods is 13.0% (r(2) = 0.96) for total absorption coefficients ranging from 0.02 to 2.0 m(-1); at 488 nm the difference is 14.5% (r(2) = 0.97); and at 550 nm the difference is 13.6% (r(2) = 0.96). The results indicate that the method presented works very well for retrieval of in-water absorption coefficients exclusively from remotely measured signals, and that this method has a wide range of potential applications in oceanic remote sensing.


Applied Optics | 2002

Effect of spectral band numbers on the retrieval of water column and bottom properties from ocean color data

Zhongping Lee; Kendall L. Carder

Using an optimization technique, we derived subsurface properties of coastal and oceanic waters from measured remote-sensing reflectance spectra. These data included both optically deep and shallow environments. The measured reflectance covered a spectral range from 400 to 800 nm. The inversions used data from each 5-, 10-, and 20-nm contiguous bands, including Sea-viewing Wide Field-of-view Sensor (SeaWiFS), moderate-resolution imaging spectrometer (MODIS), and a self-defined medium-resolution imaging spectrometer (MERIS) channels, respectively. This study is designed to evaluate the influence of spectral resolution and channel placement on the accuracy of remote-sensing retrievals and to provide guidance for future sensor design. From the results of this study, we found the following: (1) use of 10-nm-wide contiguous channels provides almost identical results as found for 5-nm contiguous channels; (2) use of 20-nm contiguous channels and MERIS provides comparable results with those with 5-nm contiguous channels for deep waters, but use of contiguous 20-nm channels perform better than MERIS for optically shallow waters; and (3) SeaWiFS or MODIS channels work fine for deep, clearer waters (total absorption coefficient at 440 nm < 0.3 m(-1)), but introduce more errors in bathymetry retrievals for optically shallow waters. The inclusion of the 645-nm MODIS land band in its channel set improves inversion returns for both deep and shallow waters.


Applied Optics | 1996

Estimating primary production at depth from remote sensing

Zhongping Lee; Kendall L. Carder; John Marra; Robert G. Steward; Mary Jane Perry

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).


Proceedings of SPIE, the International Society for Optical Engineering | 1997

Remote sensing reflectance and inherent optical properties of oceanic waters derived from above-water measurements

Zhongping Lee; Kendall L. Carder; Robert G. Steward; Thomas G. Peacock; Curtiss O. Davis; James L. Mueller

Remote-sensing reflectance and inherent optical properties of oceanic properties of oceanic waters are important parameters for ocean optics. Due to surface reflectance, Rrs or water-leaving radiance is difficult to measure from above the surface. It usually is derived by correcting for the reflected skylight in the measured above-water upwelling radiance using a theoretical Fresnel reflectance value. As it is difficult to determine the reflected skylight, there are errors in the Q and E derived Rrs, and the errors may get bigger for high chl_a coastal waters. For better correction of the reflected skylight,w e propose the following derivation procedure: partition the skylight into Rayleigh and aerosol contributions, remove the Rayleigh contribution using the Fresnel reflectance, and correct the aerosol contribution using an optimization algorithm. During the process, Rrs and in-water inherent optical properties are derived at the same time. For measurements of 45 sites made in the Gulf of Mexico and Arabian Sea with chl_a concentrations ranging from 0.07 to 49 mg/m3, the derived Rrs and inherent optical property values were compared with those from in-water measurements. These results indicate that for the waters studied, the proposed algorithm performs quite well in deriving Rrs and in- water inherent optical properties from above-surface measurements for clear and turbid waters.


Applied Optics | 2004

Effects of molecular and particle scatterings on the model parameter for remote-sensing reflectance

Zhongping Lee; Kendall L. Carder; Ke-Ping Du

For optically deep waters, remote-sensing reflectance (r(rs)) is traditionally expressed as the ratio of the backscattering coefficient (b(b)) to the sum of absorption and backscattering coefficients (a + b(b)) that multiples a model parameter (g, or the so-called f/Q). Parameter g is further expressed as a function of b(b)/(a + b(b)) (or b(b)/a) to account for its variation that is due to multiple scattering. With such an approach, the same g value will be derived for different a and b(b) values that provide the same ratio. Because g is partially a measure of the angular distribution of upwelling light, and the angular distribution from molecular scattering is quite different from that of particle scattering; g values are expected to vary with different scattering distributions even if the b(b)/a ratios are the same. In this study, after numerically demonstrating the effects of molecular and particle scatterings on the values of g, an innovative r(rs) model is developed. This new model expresses r(rs) in two separate terms: one governed by the phase function of molecular scattering and one governed by the phase function of particle scattering, with a model parameter introduced for each term. In this way the phase function effects from molecular and particle scatterings are explicitly separated and accounted for. This new model provides an analytical tool to understand and quantify the phase-function effects on r(rs), and a platform to calculate r(rs) spectrum quickly and accurately that is required for remote-sensing applications.


Applied Optics | 2000

Retrieval of chlorophyll from remote-sensing reflectance in the China seas

Mingxia He; ZhiShen Liu; Ke-Ping Du; Li-Ping Li; Rui Chen; Kendall L. Carder; Zhongping Lee

The East China Sea is a typical case 2 water environment, where concentrations of phytoplankton pigments, suspended matter, and chromophoric dissolved organic matter (CDOM) are all higher than those in the open oceans, because of the discharge from the Yangtze River and the Yellow River. By using a hyperspectral semianalytical model, we simulated a set of remote-sensing reflectance for a variety of chlorophyll, suspended matter, and CDOM concentrations. From this simulated data set, a new algorithm for the retrieval of chlorophyll concentration from remote-sensing reflectance is proposed. For this method, we took into account the 682-nm spectral channel in addition to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) channels. When this algorithm was applied to a field data set, the chlorophyll concentrations retrieved through the new algorithm were consistent with field measurements to within a small error of 18%, in contrast with that of 147% between the SeaWiFS ocean chlorophyll 2 algorithm and the in situ observation.

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Kendall L. Carder

University of South Florida

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Robert G. Steward

University of South Florida

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Thomas G. Peacock

University of South Florida

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Wesley Goode

United States Naval Research Laboratory

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David English

University of South Florida

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F. Robert Chen

University of South Florida

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Jennifer S. Patch

University of South Florida

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