Sachidananda Mishra

A Novel Algorithm for Predicting Phycocyanin Concentrations in Cyanobacteria: A Proximal Hyperspectral Remote Sensing Approach

The purpose of this research was to evaluate the performance of existing spectral band ratio algorithms and develop a novel algorithm to quantify phycocyanin (PC) in cyanobacteria using hyperspectral remotely-sensed data. We performed four spectroscopic experiments on two different laboratory cultured cyanobacterial species and found that the existing band ratio algorithms are highly sensitive to chlorophylls, making them inaccurate in predicting cyanobacterial abundance in the presence of other chlorophyll-containing organisms. We present a novel spectral band ratio algorithm using 700 and 600 nm that is much less sensitive to the presence of chlorophyll.

Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters

Phytoplankton pigment absorption data from algal-bloom-dominated waters are highly desirable to better understand the primary productivity and carbon uptake by algal biomass in a regional scale. However, retrieving phytoplankton pigment absorption coefficients, in turbid and hypereutrophic waters, from above-surface remote sensing reflectance (R_rs) is often challenging because of the optical complexity of the water body. In this paper, a quasi-analytical algorithm has been parameterized using in situ data to retrieve inherent optical properties from R_rs(λ) in highly turbid productive aquaculture ponds, where the phytoplankton absorption coefficient (3.44-37.67 m^-1 ) contributes 54 % of the total absorption at 443 nm (4.99-47.21 m^-1). The model was validated using an independent data set by comparing the model-derived optical parameters with in situ measured values. The absolute percentage error (assuming no error in the in situ measurements) of the estimated total absorption coefficient at( λ) varied from 15.22 % to 24.13 % within 413-665 nm, and the overall average error was 19.87 %. Maximum and minimum errors occurred at 443 and 665 nm, respectively. Similarly, the percentage error for the phytoplankton absorption coefficient a_φ(λ) varied from 15.9 % to 41.27 % within the 413-665-nm range, and the average error was 27.24 %. The spectral shape of modeled a_φ(λ) matched very well (R² = 0.97) with the measured a_φ(λ). A supplementary method was also developed to retrieve first-order estimates of colored detrital matter absorption coefficients a_CDM( λ) from subsurface remote sensing reflectance r_rs( λ) using an empirical approach. Results reveal that the retrieval accuracy of a_φ(λ) improved after incorporating the first-order estimates of a_CDM(λ) in the algorithm.

A Performance Review of Reflectance Based Algorithms for Predicting Phycocyanin Concentrations in Inland Waters

We evaluated the accuracy and sensitivity of six previously published reflectance based algorithms to retrieve Phycocyanin (PC) concentration in inland waters. We used field radiometric and pigment data obtained from two study sites located in the United States and Brazil. All the algorithms targeted the PC absorption feature observed in the water reflectance spectra between 600 and 625 nm. We evaluated the influence of chlorophyll-a (chl-a) absorption on the performance of these algorithms in two contrasting environments with very low and very high cyanobacteria content. All algorithms performed well in low to moderate PC concentrations and showed signs of saturation or decreased sensitivity for high PC concentration with a nonlinear trend. MM09 was found to be the most accurate algorithm overall with a RMSE of 15.675%. We also evaluated the use of these algorithms with the simulated spectral bands of two hyperspectral space borne sensors including Hyperion and Compact High-Resolution Imaging Spectrometer (CHRIS) and a hyperspectral air borne sensor, Hyperspectral Infrared Imager (HyspIRI). Results showed that the sensitivity for chl-a of PC retrieval algorithms for Hyperion simulated data were less noticable than using the spectral bands of CHRIS; HyspIRI results show that SC00 could be used for this sensor with low chl-a influence. This review of reflectance based algorithms can be used to select the optimal approach in studies involving cyanobacteria monitoring through optical remote sensing techniques.

A novel remote sensing algorithm to quantify phycocyanin in cyanobacterial algal blooms

We present a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. The water sample and remote sensing reflectance data used for PC3 calibration and validation were acquired from highly turbid productive catfish aquaculture ponds. Since the characteristic PC absorption feature at 620 nm is contaminated with residual chlorophyll-a (Chl-a) absorption, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating Chl-a absorption at 620 nm–665 nm enables PC3 to compensate for the confounding effect of Chl-a at the PC absorption band and considerably increases the accuracy of the PC prediction algorithm. In the current dataset, PC3 produced the lowest mean relative error of prediction among all PC algorithms considered in this research. Moreover, PC3 eliminates the nonlinear sensitivity issue of PC algorithms particularly at high PC range (>100 μg L−1). Therefore, introduction of PC3 will have an immediate positive impact on studies monitoring inland and coastal cyanobacterial harmful algal blooms.

Plume and bloom: effect of the Mississippi River diversion on the water quality of Lake Pontchartrain

The opening of the Bonnet Carré spillway to prevent flood threat to New Orleans in April 2008 created a sediment plume in the Lake Pontchartrain. The nutrient rich plume triggered a massive algal bloom in the lake. In this article, we have quantified the spatio-temporal distribution of the plume (suspended solids) and the bloom (chlorophyll-a (chl-a)) in the lake using remotely-sensed data. We processed the Moderate-resolution Imaging Spectroradiometer satellite data for mapping the total suspended solids (TSS) and chl-a concentrations. An existing algorithm was used for estimating TSS whereas a novel slope model was developed to predict the per-pixel chl-a concentration. Both algorithms were successful in capturing the spatio-temporal trend of TSS and chl-a concentrations, respectively. Algal growth was found to be inversely related to TSS concentrations and a time lag of ∼45 days existed between the spillway opening and the appearance of the first algal bloom at an observation location.

Preface: Remote Sensing of Water Resources

The Special Issue (SI) on “Remote Sensing of Water Resources” presents a diverse range of papers studying remote sensing tools, methods, and models to better monitor water resources which include inland, coastal, and open ocean waters. The SI is comprised of fifteen articles on widely ranging research topics related to water bodies. This preface summarizes each article published in the SI.

Hyperspectral remote sensing of Cyanobacteria: successes and challenges

Cyanobacterial harmful algal blooms (CHABs) is a major water quality issue in surface water bodies because of its scum and bad odor forming and toxin producing abilities. Terminations of blooms also cause oxygen depletion leading to hypoxia and widespread fish kills. Therefore, continuous monitoring of CHABs in recreational water bodies and surface drinking water sources is highly required for their early detection and subsequent issuance of a health warning and reducing the economic loss. We present a comparative study between a modified quasi-analytical algorithm (QAA) and a novel three-band algorithm (PC3) to retrieve phycocyanin (PC) pigment concentration in cyanobacteria laden inland waters. An extensive dataset, consisting of radiometric measurements, absorption measurements of phytoplankton, organic matter, detritus, and pigment concentration, was used to optimize the algorithms. The QAA algorithm isolates the PC signal from the remote sensing reflectance data using a set of radiative transfer equations and retrieves PC concentration in the water bodies through bio-optical inversion. Validation of the QAA algorithm, using an independent dataset, produced a mean relative error (MRE) of 34%. For the PC3 algorithm, we propose a coefficient (ψ) for isolating the PC absorption component at 620 nm. Results show that inclusion of the model coefficient relating chlorophyll-a (chla) absorption at 620 nm to 665 nm enables PC3 to compensate for the confounding effect of chl-a and considerably increases the accuracy of the PC prediction algorithm. The MRE of prediction for PC3 was 27%. Moreover, PC3 eliminates the nonlinear sensitivity issue of PC algorithms at high range.