Remote Sensing Reflectance Model Research Articles

Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

Current water color remote sensing algorithms typically do not consider the vertical variations of phytoplankton. Ecolight with a radiative transfer program was used to simulate the underwater light field of vertical inhomogeneous waters based on the optical properties of a eutrophic lake (i.e., Lake Chaohu, China). Results showed that the vertical distribution of chlorophyll-a (Chla(z)) can considerably affect spectrum shape and magnitude of apparent optical properties (AOPs), including subsurface remote sensing reflectance in water (rrs(λ, z)) and the diffuse attenuation coefficient (Kx(λ, z)). The vertical variations of Chla(z) changed the spectrum shapes of rrs(λ, z) at the green and red wavelengths with a maximum value at approximately 590 nm, and changed the Kx(λ, z) from blue to red wavelength range with no obvious spectral variation. The difference between rrs(λ, z) at depth z m and its asymptotic value (Δrrs(λ, z)) could reach to ~78% in highly stratified waters. Diffuse attenuation coefficient of downwelling plane irradiance (Kd(λ, z)) had larger vertical variations, especially near water surface, in highly stratified waters. Three weighting average functions performed well in less stratified waters, and the weighting average function proposed by Zaneveld et al., (2005) performed best in highly stratified waters. The total contribution of the first three layers to rrs(λ, 0−) was approximately 90%, but the contribution of each layer in the water column to rrs(λ, 0−) varied with wavelength, vertical distribution of Chla(z) profiles, concentration of suspended particulate inorganic matter (SPIM), and colored dissolved organic matter (CDOM). A simple stratified remote sensing reflectance model considering the vertical distribution of phytoplankton was built based on the contribution of each layer to rrs(λ, 0−).

Deriving inherent optical property for highly turbid productive inland water from MERIS data by semi-analytical model: A case study in Taihu Lake, China

Remote estimation of inherent optical properties was greatly challenged by significant spatial-temporal variation and the extreme complexity of bio-optical properties in inland turbid water. The multiband quasi-analytical algorithm has advantages over traditional band ratio and semi-analytical algorithm in that it is based on the remote sensing reflectance model derived from radiative transfer equation and does not need the parameterization of absorption coefficients. An improved model, which used, was developed to retrieve inherent optical properties in high turbid inland water. As a first step, the backscattering coefficient at reference wavelength [bbp(λ0)] was retrieved directly by support vector machine optimization algorithm instead of step 2 in the quasi analytical algorithm for the high correlation between bbp(λ0) and remote sensing reflectance at the near-infrared wavelength. The second step, a semi-analytical support vector machine algorithm, was used to retrieve spectral shape of bbp(λ) instead the step 4 in the quasi analytical algorithm. Part of field-measured dataset collected on November 2006, November 2007, November 2008 and April 2009 in Taihu Lake was used to train the support vector machine model, and the other part was used to test this algorithm. Results indicated that the mean square root of percentage between the derived and measured value of bbp(532 nm) was less than 3.73% and root mean square percentage of ap(442 nm) and ap(532 nm) were 15.29% and 30.45%, respectively. Furthermore, the potential application of this algorithm to MERIS data was investigated by the reduced resolution MERIS satellite image. The result shows that satellite-derived data using the support vector machine model is consistent with in situ measured data. This study advances the semi-analytical model and broadens the application of MERIS data in highly turbid inland waters.

Assessment of a bidirectional reflectance distribution correction of above-water and satellite water-leaving radiance in coastal waters

Water-leaving radiances, retrieved from in situ or satellite measurements, need to be corrected for the bidirectional properties of the measured light in order to standardize the data and make them comparable with each other. The current operational algorithm for the correction of bidirectional effects from the satellite ocean color data is optimized for typical oceanic waters. However, versions of bidirectional reflectance correction algorithms specifically tuned for typical coastal waters and other case 2 conditions are particularly needed to improve the overall quality of those data. In order to analyze the bidirectional reflectance distribution function (BRDF) of case 2 waters, a dataset of typical remote sensing reflectances was generated through radiative transfer simulations for a large range of viewing and illumination geometries. Based on this simulated dataset, a case 2 water focused remote sensing reflectance model is proposed to correct above-water and satellite water-leaving radiance data for bidirectional effects. The proposed model is first validated with a one year time series of in situ above-water measurements acquired by collocated multispectral and hyperspectral radiometers, which have different viewing geometries installed at the Long Island Sound Coastal Observatory (LISCO). Match-ups and intercomparisons performed on these concurrent measurements show that the proposed algorithm outperforms the algorithm currently in use at all wavelengths, with average improvement of 2.4% over the spectral range. LISCO's time series data have also been used to evaluate improvements in match-up comparisons of Moderate Resolution Imaging Spectroradiometer satellite data when the proposed BRDF correction is used in lieu of the current algorithm. It is shown that the discrepancies between coincident in-situ sea-based and satellite data decreased by 3.15% with the use of the proposed algorithm. This confirms the advantages of the proposed model over the current one, demonstrating the need for a specific case 2 water BRDF correction algorithm as well as the feasibility of enhancing performance of current and future satellite ocean color remote sensing missions for monitoring of typical coastal waters.

Remote sensing Extraction model of redtide biomass by air-borne hyperspectral technique

Our work is based on the known research results of inherent optical quality of ocean color constituents. According to optimized parameters and induced fluorescence term of chlorophyll, this paper puts forward a remote sensing reflectance model of sea water, which is fitted in Liaodong Bay of Bohai. An inverse model that can evaluate redtide biomass according to chlorophyll retrieval is provided by inducing a functional extreme problem. The calculation example of the model indicates that the inversion model has explicit mathematic and physical meaning, but its practicability needs to be verified.

Model of remote-sensing reflectance including bidirectional effects for case 1 and case 2 waters

A remote-sensing reflectance model based on a lookup table is proposed for use in analyzing satellite ocean color data in both case 1 and case 2 waters. The model coefficients are tabulated for grid values of three angles--solar zenith, sensor zenith, and relative azimuth--to take account of directional variation. This model also requires, as input, a phase function parameter defined by the contribution of suspended particles to the backscattering coefficient. The model is generated from radiative transfer simulations for a wide range of inherent optical properties that cover both case 1 and 2 waters. The model uncertainty that is due to phase function variability is significantly reduced from that in conventional models. Bidirectional variation of reflectance is described and explained for a variety of cases. The effects of wind speed and cloud cover on bidirectional variation are also considered, including those for the fully overcast case in which angular variation can still be considerable (approximately 10%). The implications for seaborne validation of satellite-derived water-leaving reflectance are discussed.

Retrieval of water optical properties for optically deep waters using genetic algorithms

Retrieval of water optical properties and concentrations can be identified as a nonlinear optimization problem. This problem may be difficult to solve by conventional optimization methods owing to its multimodel nonconvex nature. This letter explores the potential of genetic algorithms as the optimization scheme in such a problem. A remote sensing reflectance model for optically deep waters was used to illustrate the performance of the algorithms. The superiority of genetic algorithms over conventional optimization methods was demonstrated by experiments on a field dataset.

In situ determination of the remotely sensed reflectance and the absorption coefficient: closure and inversion

We tested closure between in situ radiometric and absorption coefficient measurements by using a nearly backscattering-independent remote-sensing reflectance model that employs the remote-sensing reflectance at three wavelengths. We show that only a small error is introduced into the closure model when the proper functional relationships of f/Q and the backscattering is taken to be a constant when using the sea-viewing wide field-of-view sensor wavelengths 443, 490, and 555 nm. A method of inverting the model to obtain the absorption coefficient by use of simple linear spectral relationships of the absorption coefficient is provided. The results of the model show that the independent measurements of reflectance and absorption obtain closure with a high degree of accuracy.

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

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.