nhai Li

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R2 = 0.94, 0.91) than their performance on Chl-a concentration (R2 = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R2 = 0.91, 0.82) and TSM (R2 = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R2 = 0.75 and 0.90 for Chl-a; R2 = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.

Hyperspectral Remote Sensing of Total Phosphorus (TP) in Three Central Indiana Water Supply Reservoirs

The connection between nutrient input and algal blooms for inland water productivity is well known but not the spatial pattern of water nutrient loading and algae concentration. Remote sensing provides an effective tool to monitor nutrient abundances via the association with algae concentration. Twenty-one field campaigns have been conducted with samples collected under a diverse range of algal bloom conditions for three central Indiana drinking water bodies, e.g., Eagle Creek Reservoir (ECR), Geist Reservoir (GR), and Morse Reservoir (MR) in 2005, 2006, and 2008, which are strongly influenced anthropogenic activities. Total phosphorus (TP) was estimated through hyperspectral remote sensing due to its close association with chlorophyll a (Chl-a), total suspended matter, Secchi disk transparency (SDT), and turbidity. Correlation analysis was performed to determine sensitive spectral variables for TP, Chl-a, and SDT. A hybrid model combining genetic algorithms and partial least square (GA-PLS) was established for remote estimation of TP, Chl-a, and SDT with selected sensitive spectral variables. The result indicates that TP has close association with diagnostic spectral variables with R2 ranging from 0.55 to 0.72. However, GA-PLS has better performance with an average R2 of 0.87 for aggregated dataset. GA-PLS was applied to the airborne imaging data (AISA) to map spatial distribution of TP, Chl-a, and SDT for MR and GR. The eutrophic status was evaluated with Carlson trophic state index using TP, Chl-a, and SDT maps derived from AISA images. Mapping results indicated that most MR belongs to mesotrophic (48.6%) and eutrophic (32.7%), while the situation was more severe for GR with 57.8% belongs to eutrophic class, and more than 40% to hypereutrophic class due to the high turbidity resulting from dredging practices.

A semi-analytical algorithm for remote estimation of phycocyanin in inland waters

Phycocyanin (PC) is the unique and important accessory pigment for monitoring toxic cyanobacteria in inland waters. In this study, a semi-analytical algorithm combining both three band indices and a baseline algorithm (TBBA) was developed to estimate PC concentrations and then tested in three eutrophic and turbid reservoirs. TBBA does not need to optimize wavelengths as either the traditional baseline algorithm or three-band algorithms does when it is used across different study sites. TBBA evidently corrects some effects of absorptions due to colored detritus matter and other pigments and backscattering of water column. TBBA accurately estimated PC concentrations with R(2)=0.8573 and rRMSE=31.4% for water samples with the PC range from 1.4 mgm(-3) to 146.1 mgm(-3). Particularly, TBBA outperformed three-band algorithms and a previously proposed semi-empirical algorithm for the prediction of low PC (PC ≤ 50 mgm(-3)) concentration. Further analysis reveals that both the variations of PC:Chl-a and PC:TSM are important factors influencing the performance of all PC algorithms examined in this study and more efforts are required to improve the performance of TBBA on water samples with low PC concentration.

An improved analytical algorithm for remote estimation of chlorophyll-a in highly turbid waters

An analytical three-band algorithm for spectrally estimating chlorophyll-a (Chl-a) has been proposed recently and the model does not need to be trained. However, the model did not consider the effects of the absorption due to colored detritus matter (CDM) and backscattering of the water column, resulting in an overestimation when Chl-a < 50 mg m −3 and an underestimation when Chl-a 50 mg m −3 . In this letter, an improved three-band algorithm is proposed by integrating both backscattering and CDM absorption coefficients into the model. The results demonstrate that the improved three-band model resulted in more accurate estimation of Chl-a than the previously used three-band model when they were applied to water samples collected from five highly turbid water bodies with Chl-a ranging from 2.54 to 285.8 mg m −3 . The best results, after model modification, were observed in three Indiana reservoirs with R 2 = 0.905 and relative root mean square error of 20.7%, respectively.

Effects of inelastic radiative processes on the determination of water-leaving spectral radiance from extrapolation of underwater near-surface measurements.

Extrapolation of near-surface underwater measurements is the most common method to estimate the water-leaving spectral radiance, Lw(λ) (where λ is the light wavelength in vacuum), and remote-sensing reflectance, Rrs(λ), for validation and vicarious calibration of satellite sensors, as well as for ocean color algorithm development. However, uncertainties in Lw(λ) arising from the extrapolation process have not been investigated in detail with regards to the potential influence of inelastic radiative processes, such as Raman scattering by water molecules and fluorescence by colored dissolved organic matter and chlorophyll-a. Using radiative transfer simulations, we examine high-depth resolution vertical profiles of the upwelling radiance, Lu(λ), and its diffuse attenuation coefficient, KLu (λ), within the top 10 m of the ocean surface layer and assess the uncertainties in extrapolated values of Lw(λ). The inelastic processes generally increase Lu and decrease KLu in the red and near-infrared (NIR) portion of the spectrum. Unlike KLu in the blue and green spectral bands, KLu in the red and NIR is strongly variable within the near-surface layer even in a perfectly homogeneous water column. The assumption of a constant KLu with depth that is typically employed in the extrapolation method can lead to significant errors in the estimate of Lw. These errors approach ∼100% at 900 nm, and the desired threshold of 5% accuracy or less cannot be achieved at wavelengths greater than 650 nm for underwater radiometric systems that typically take measurements at depths below 1 m. These errors can be reduced by measuring Lu within a much shallower surface layer of tens of centimeters thick or even less at near-infrared wavelengths longer than 800 nm, which suggests a requirement for developing appropriate radiometric instrumentation and deployment strategies.

Deep-sea low-light radiometer system.

Two single-waveband low-light radiometers were developed to characterize properties of the underwater light field relevant to biological camouflage at mesopelagic ocean depths. Phenomena of interest were vertical changes in downward irradiance of ambient light at wavelengths near 470 nm and 560 nm, and flashes from bioluminescent organisms. Depth profiles were acquired at multiple deep stations in different geographic regions. Results indicate significant irradiance magnitudes at 560 nm, providing direct evidence of energy transfer as described by Raman scattering. Analysis of a night profile yielded multiple examples of bioluminescent flashes. The selection of high-sensitivity, high-speed silicon photomultipliers as detectors enabled measurement of spectrally-resolved irradiance to greater than 400 m depth.

A Bio-optical Approach to Estimating Chlorophyll-a Concentration from Hyperspectral Remote Sensing

Eagle Creek Reservoir is one of three central Indiana reservoirs supplying drinking water for the residents of Indianapolis. The occurrence of blue-green algae blooms resulting from high nutrient input has been a major public concern so that estimation of chlorophyll-a concentration of this reservoir is significantly important for assessing the reservoirs water quality. Empirical and semi-empirical methods were used in our previous studies for estimating CHL. Due to limitations to empirical and semi-empirical methods, a bio-optical model is tested in this study. Field campaigns were carried out in Eagle Creek Reservoir in central Indiana, and water samples analyzed for water quality parameter concentrations and their inherent optical properties (IOPs). A bio-optical model parameterized with these derived IOPs is used to estimate CHL concentration through a matrix inversion of hyperspectral data, and its performance is compared with those for empirical and semi-empirical models. The result demonstrates that the bio-optical model results in a higher correlation than empirical and semi-empirical models do.

Sunlight induced chlorophyll fluorescence in the near‐infrared spectral region in natural waters: Interpretation of the narrow reflectance peak around 761 nm

Sunlight induced chlorophyll a fluorescence (SICF) can be used as a probe to estimate chlorophyll a concentrations (Chl) and infer phytoplankton physiology. SICF at ∼685 nm has been widely applied to studies of natural waters. SICF around 740 nm has been demonstrated to cause a narrow reflectance peak at ∼761 nm in the reflectance spectra of terrestrial vegetation. This narrow peak has also been observed in the reflectance spectra of natural waters, but its mechanism and applications have not yet been investigated and it has often been treated as measurement artifacts. In this study, we aimed to interpret this reflectance peak at ∼761 nm and discuss its potential applications for remote monitoring of natural waters. A derivative analysis of the spectral reflectance suggests that the 761 nm peak is due to SICF. It was also found that the fluorescence line height (FLH) at 761 nm significantly and linearly correlates with Chl. FLH(761 nm) showed a tighter relationship with Chl than the relationship between FLH(∼685 nm) and Chl mainly due to weaker perturbations by nonalgal materials around 761 nm. While it is not conclusive, a combination of FLH(761 nm) and FLH(∼685 nm) might have some potentials to discriminate cyanobacteria from other phytoplankton due to their different fluorescence responses at the two wavelengths. It was further found that reflectance spectra with a 5 nm spectral resolution are adequate to capture the spectral SICF feature at ∼761 nm. These preliminary results suggest that FLH(761 nm) need to be explored more for future applications in optically complex coastal and inland waters.