Chl-a Estimation Research Articles

An OLCI-based algorithm for semi-empirically partitioning absorption coefficient and estimating chlorophyll a concentration in various turbid case-2 waters

Accurate remote assessment of phytoplankton chlorophyll-a (Chla) concentration in turbid case-2 waters is a challenge, owing largely to terrestrial substances (such as minerals and humus) that are optically significant but do not co-vary with phytoplankton. Here, we propose an improved Quasi-Analytical Algorithm (QAA) (denoted as TC2) for retrieving Chla concentrations from remote sensing reflectance (Rrs(λ)) which can be applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in turbid case-2 waters. TC2 has two main extensions when compared with QAA. First, TC2 makes an additional assumption to separate the total non-water absorption at 665 nm (anw(665)) into phytoplankton absorption (aph(665)) and yellow matter (aym(665)), which is the sum of colored dissolved matter (CDOM) and detritus. Second, for selecting the position of the near-infrared (NIR) band which is used to estimate the signal of total backscattering coefficient (bb(λ0)) at QAA reference band (λ0), we take into account the assumption that the absorption of pure water should be dominant at this band, as well as the impact of the signal-to-noise ratio (SNR) in the NIR band on the Chla concentration estimating model. When applied to in situ Rrs(λ) and OLCI match-up Rrs(λ) data in this study, TC2 provided more accurate Chla estimation than previous Cha concentration retrieval algorithms for turbid case-2 waters. TC2 has the potential for use as a simple and effective algorithm for monitoring Chla concentrations in the turbid case-2 waters at a global scale from space.

An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

Spectral Feature Selection Optimization for Water Quality Estimation

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95 to 6.37 , and the Pearson’s correlation coefficients between the predicted and in situ Chl- improve from 0.56 to 0.89.

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.

ESTIMATION OF CHLOROPHYLL-A CONCENTRATION IN LAGUNA DE BAY USING SENTINEL-3 SATELLITE DATA

Abstract. The mission of the European Space Agency’s Sentinel-3 satellite is to provide data for land and ocean monitoring purposes. Sentinel- 3’s Ocean and Land Colour Instrument (OLCI) data are being used largely for monitoring offshore and coastal waters but can also be used for inland waters including lakes. It has a spatial resolution of 300 meters, a temporal resolution of 2–3 days and contains 21 spectral bands. Laguna de Bay, with a surface area of around 930 km2, suffers from periodic algal blooms resulting from excessive nutrient inputs from surrounding watersheds. This study aims to assess the applicability of Sentinel-3 OLCI for estimating chlorophylla (chl-a) concentration in Laguna Lake. Several chl-a estimation algorithms (i.e., band ratios and indices) were tested for Sentinel 3 OLCI images and compared with in-situ data obtained using a chl-a sensor. A regression model comprising of individual spectral bands, band ratios, and band indices for chl-a estimation as independent variables was developed, yielding an adjusted R2 of 0.759 and RMSE of 1.19 ug/L. The model consists of R620, R674/R708, and RED/NIR. A map was produced showing the spatial distribution of chl-a in Laguna Lake, with most of the portion of the lake having a concentration ranging from 7.5 ug/L to 15 ug/L. This shows that Sentinel 3 OLCI images can be utilized for accurately estimating chl-a in Laguna Lake.

Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges

Eutrophication and increasing prevalence of potentially toxic cyanobacterial blooms among global inland water bodies is becoming a major concern and requires direct attention. The European Space Agency recently launched the Ocean and Land Color Instrument (OLCI) aboard the Sentinel 3 satellite. The success of the mission will depend on extensive validation efforts for the development of accurate and robust in-water algorithms. In this study, four full atmospheric correction methods are assessed over four inland water reservoirs in South Africa, along with a suite of red/NIR based semi-analytic and band difference models for chl-a estimation which were applied to both full and partial atmospherically corrected data. In addition, we tested a novel duplicate pixel correction method to account for duplicate pixels induced by high observation zenith angles. Radiometric errors associated with OLCI Top of Atmosphere (TOA) radiances over small water targets were also investigated by modeling in situ reflectance measurements to at-sensor radiances using MODTRAN. Of the four atmospheric corrections, the 6SV1 radiative transfer code showed the most promise for producing reasonable reflectances when compared to in-situ measurements. Empirically derived band difference models outperformed all other chl-a retrieval methods on both partially and fully corrected reflectances. The Maximum Peak Height (MPH) algorithm applied to Bottom of Rayleigh Reflectance (BRR) performed best overall (R2 = 0.55, RMSE(%) = 99), while the Maximum Chlorophyll Index (MCI) performed best on atmospherically corrected data using 6SV1 (R2 = 0.35, RMSE(%) = 107). Semi-analytic chl-a retrieval methods proved very successful when applied to in situ Rrs, however, are not reliable when applied to low quality reflectance data. The SIMilarity Environment Correction (SIMEC), an adjacency correction applied in conjunction with the image correction for atmospheric effects (iCOR) processor, did not improve retrieval results for these small water targets.

ESTIMATING AND MAPPING CHLOROPHYLL-A CONCENTRATION OF PHEWA LAKE OF KASKI DISTRICT USING LANDSAT IMAGERY

Abstract. Water is a major component in the living ecosystem. As water quality is degrading due to human intervention, continuous monitoring is necessary. One of the indicators is Chlorophyll-a (Chl-a) which indicates algal blooms which are often driven by eutrophication phenomena in freshwater. Lakes should be monitored for Chl-a because Chla-a is related to eutrophication phenomena which are an enrichment of water by nutrients salt. When the environment becomes enriched with nutrients the excessive growth can lead to the death of fish. In this study, the Remote Sensing (RS) and Geographic Information System (GIS) techniques were utilized to determine Chl-a concentration of Phewa Lake of Kaski district. We used Landsat 8 satellite imagery for estimation and mapping of the Chl-a concentration. In-situ measurements from different sample points were taken and used to form a regression model for Chl-a and its concentration over the water body was calculated. The preceding year’s (2016) in situ measurement data of Chl-a concentration at a specific location were assessed with the one evaluated from the regression model thus produced for the succeeding year (2017) using Root Mean Square Error (RMSE) technique. As a result, we concluded that the estimation and mapping of Chl-a of a lake in Nepal can be done with the help of RS and GIS techniques.

Variabilidade Temporal das Concentrações de Clorofila -A e da Temperatura Superficial do Mar na Costa Central de Santa Catarina

O fenômeno das florações de algas é conhecido mundialmente devido aos impactos diretos na saúde dos ecossistemas aquáticos, nos recursos pesqueiros e aquícolas, nas atividades recreacionais e na saúde humana. O presente estudo avaliou o uso de sensores satelitais na quantificação dos parâmetros de temperatura superficial do mar (TSM) e concentração de clorofila-a na superfície do mar (Chl-a) na costa de Santa Catarina, durante o período entre 2002 e 2019, objetivando identificar a variabilidade temporal da TSM e da Chl-a a partir da decomposição das séries temporais em tendência, sazonalidade e resíduos. Para atingir este objetivo foram adquiridas estimativas de TSM e Chl-a oriundas dos sensores orbitais Moderate-Resolution Imaging Spectroradiometer (MODIS) a bordo dos satélites (Aqua e Terra) e Visible Infrared Imaging Radiometer Suite (VIIRS) a bordo dos satélites (SNPP e NOAA-20). Através da extração dos valores de TSM e Chl-a para um ponto ao largo da costa central de Santa Catarina foram geradas séries temporais para o período especificado e aplicada a metodologia de decomposição de séries temporais para identificar oscilações de baixa frequência. Foi possível identificar que os eventos com maior Chl-a estão relacionados com eventos com menores valores de TSM. As componentes encontradas poderão ser utilizadas para modelos preditivos auxiliando assim a minimizar os impactos associados às florações de algas.

Remotely sensing phytoplankton size structure in the Red Sea

Phytoplankton size structure impacts ocean food-web dynamics and biogeochemical cycling, and is thus an important ecological indicator that can be utilised to quantitatively evaluate the state of marine ecosystems. Potential alterations to size structure are predicted to occur in tropical regions under future scenarios of climate change. Therefore, there is an increasing requirement for the synoptic monitoring of phytoplankton size structure in marine systems. The Red Sea remains a comparatively unexplored tropical marine ecosystem, particularly with regards to its large-scale biological dynamics. Using an in situ pigment dataset acquired in the Red Sea, we parameterise a two-component, abundance-based phytoplankton size model and apply it to remotely-sensed observations of chlorophyll-a (Chl-a) concentration, to infer Chl-a in two size classes of phytoplankton, small cells <2 μm in size (picophytoplankton) and large cells >2 μm in size. Satellite-derived estimates of phytoplankton size structure are in good agreement with corresponding in situ measurements and also capture the spatial variability related to regional mesoscale dynamics. Our analysis reveals that, for the estimation of Chl-a in the two size classes, the model performs comparably or in some cases better, to validations in other oceanic regions. Our model parameterisation will be useful for future studies on the seasonal and interannual variability of phytoplankton size classes in the Red Sea, which may ultimately be relevant for understanding trophic linkages between phytoplankton size structure and fisheries, and the development of marine management strategies.

Estimation of Chlorophyll-a in Northern Coastal Bay of Bengal Using Landsat-8 OLI and Sentinel-2 MSI Sensors

Chlorophyll-a can be used as a proxy for phytoplankton and thus is an essential water quality parameter. The presence of phytoplankton in the ocean causes selective absorption of light by chlorophyll-a pigment resulting in change of the ocean color that can be identified by ocean colour remote sensing. The accuracy of chlorophyll-a concentration (Chl-a) estimated from remote sensing sensors depends on the bio-optical algorithm used for the retrieval in specific regional waters. In this work, it is attempted to estimate Chl-a from two currently active satellite sensors with relatively good spatial resolutions considering ocean applications. Suitability of two standard bio-optical Ocean Colour (OC) Chlorophyll algorithms, OC-2 (2-band) and OC-3 (3-band) in estimating Chl-a for turbid waters of the northern coastal Bay of Bengal is assessed. Validation with in-situ data showed that OC-2 algorithm gives an estimate of Chl-a with a better correlation of 0.795 and least bias of 0.35 mg/m3. Further, inter-comparison of Chl-a retrieved from the two sensors, Landsat-8 OLI and Sentinel-2 MSI was also carried out. The variability of Chl-a during winter, pre-monsoon and post-monsoon seasons over the study region were inter-compared. It is observed that during pre-monsoon and post-monsoon seasons, Chl-a from MSI is over estimated compared to OLI. This work is a preliminary step towards estimation of Chl-a in the coastal oceans utilizing available better spatially resolved sensors.

Quantifying Spatiotemporal Dynamics of the Column-Integrated Algal Biomass in Nonbloom Conditions Based on OLCI Data: A Case Study of Lake Dianchi, China

Traditional remote sensing observation can only derive chlorophyll-a concentration in surface water (Chlasurf), which is insufficient for water quality monitoring or biogeochemical applications. In this paper, a column-integrated biomass (CIB) estimation algorithm was established in Lake Dianchi, China, and the CIB was estimated by Ocean and Land Color Instrument (OLCI) data from 2016 to 2018. First, the results show that the method is applicable to CIB estimation in nonbloom conditions [mean absolute percentage error (MAPE) = 10.55%, mean ratio (MR) = 0.996]. On the one hand, Chlasurf could be estimated using a universal model of Chla (UMOC) estimation for case-II waters; on the other hand, CIB could be obtained from Chlasurf according to the linear relationship between the Chlasurf and CIB. Second, both the spatial and temporal variabilities of Chlasurf are larger than those of CIB, which indicates that the biomass of the whole lake is relatively stable from day to day. During algal bloom periods, the CIB at nonbloom areas tends to decrease, suggesting that algae from nonbloom regions have migrated to bloom regions. Third, wind speed (WS) is the most critical meteorological factor affecting the water surface biomass, compared with other parameters. The average percentage of surface-to-total biomass in nonbloom conditions is about 7% when the WS is less than 2 m/s.

Comparative analysis of four semi-analytical models for estimating chlorophyll-a concentration in case-2 waters using field hyperspectral reflectance

ABSTRACT Hyperspectral remote sensing can capture the complicated and variable characteristics of inland waters; thus, it is suited for the water quality assessment of Case-2 waters, and it has the potential to attain high estimation accuracy. In the present study, four improved models adapted from published approaches (three-band index, ΔΦ, BNDBI and TCARI) were investigated to estimate chlorophyll-a (chl-a) for the case of Dianshan Lake, China. Calibration and validation were provided from in situ measured chl-a and field hyperspectral measurements. The improved three-band (ITB) model, ΔΦ model, and BNDBI model yielded satisfactory results and enabled the estimation of chl-a for inland Case-2 waters with coefficients of determination (R 2) reaching 0.75, 0.76, and 0.86, respectively. In particular, the TCARI/OSAVI model presented the highest accuracy (R 2 = 0.94) compared to the other models. All of the results provide strong evidence that the hyperspectral models presented in this paper are promising and applicable to estimate chl-a in eutrophic inland Case-2 waters.

Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement

The size and distribution of Phytoplankton populations are indicators of the ecological status of a water body. The chlorophyll-a (Chl-a) concentration is estimated as a proxy for the distribution of phytoplankton biomass. Remote sensing is the only practical method for the synoptic assessment of Chl-a at large spatial and temporal scales. Long-term records of ocean color data from the MODIS Aqua Sensor have proven inadequate to assess Chl-a due to the lack of a robust ocean color algorithm. Chl-a estimation in shallow and coastal water bodies has been a challenge and existing operational algorithms are only suitable for deeper water bodies. In this study, the Ocean Color 3M (OC3M) derived Chl-a concentrations were compared with observed data to assess the performance of the OC3M algorithm. Subsequently, a regression analysis between in situ Chl-a and remote sensing reflectance was performed to obtain a green-red band algorithm for coastal (case 2) water. The OC3M algorithm yielded an accurate estimate of Chl-a for deep ocean (case 1) water (RMSE = 0.007, r2 = 0.518, p &lt; 0.001), but failed to perform well in the coastal (case 2) water of Chesapeake Bay (RMSE = 23.217, r2 = 0.009, p = 0.356). The algorithm developed in this study predicted Chl-a more accurately in Chesapeake Bay (RMSE = 4.924, r2 = 0.444, p &lt; 0.001) than the OC3M algorithm. The study indicates a maximum band ratio formulation using green and red bands could improve the satellite estimation of Chl-a in coastal waters.

Assessment of Satellite-Based Chlorophyll-a Algorithms in Eutrophic Korean Coastal Waters: Jinhae Bay Case Study

Jinhae Bay, one of the most important aquaculture areas in Korean coastal waters, has suffered from serious environmental problems due to intensive anthropogenic activities since the 1970s. Determining the response of coastal ecosystems in Korea to anthropogenic activities requires understanding the characteristics of chlorophyll-a concentration (Chl-a), and the high spatiotemporal resolution of the Geostationary Ocean Color Imager (GOCI) can aid these efforts. However, producing reliable satellite-based Chl-a estimates is challenging in optically complex coastal waters and the Chl-a estimation algorithms must be assessed regionally. Based on in situ Chl-a measurements collected in Jinhae Bay between 2011 and 2016, we evaluated GOCI-derived Chl-a estimates obtained using six ocean color Chl-a algorithms: two standard open ocean algorithms, one GOCI-standard algorithm, and three Tassan’s algorithms regionally modified for Korean waters. All of the algorithms tended to underestimate high Chl-a values >0.9 mg m–3. The Yellow Sea Large Marine Ecosystem Ocean Color Project (YOC) algorithm, one of the modified Tassan’s algorithms, provided the best fit to the in situ Chl-a measurements in Jinhae Bay (r=0.51 , p<0.05 ), including appropriate representations of the spatial and temporal variation. Therefore, this algorithm can be considered a baseline approach for satellite-based long-term coastal monitoring systems in Jinhae Bay.

A global approach for chlorophyll-a retrieval across optically complex inland waters based on optical water types

Numerous algorithms have been developed to retrieve chlorophyll-a (Chla) concentrations (mg m−3) from Earth observation (EO) data collected over optically complex waters. Retrieval accuracy is highly variable and often unsatisfactory where Chla co-occurs with other optically active constituents. Furthermore, the applicability and limitations of retrieval algorithms across different optical complex systems in space and time are often not considered. In the first instance, this paper provides an extensive performance assessment for 48 Chla retrieval algorithms of varying architectural design. The algorithms are tested in their original parametrisations and are then retuned using in-situ remote sensing reflectance (Rrs(λ), sr−1) data (n = 2807) collected from 185 global inland and coastal aquatic systems encompassing 13 different optical water types (OWTs). The paper then demonstrates retrieval performance across the full dataset of observations and within individual OWTs to determine the most effective model(s) of those tested for retrieving Chla in waters with varying optical properties. The results revealed significant variability in retrieval performance when comparing model outputs to in-situ measured Chla for the full in-situ dataset in its entirety and within the 13 distinct OWTs. Importantly, retuning an algorithm to optimise its parameterisation for each individual OWT (i.e. one algorithm, multiple parameterisations) is found to improve the retrieval of Chla overall compared to simply calibrating the same algorithm using the complete in-situ dataset (i.e. one algorithm, one parameterisation). This resulted in a 25% improvement in retrieval accuracy based on relative percentage difference errors for the best performing Chla algorithm. Improved performance is further achieved by allowing model type and specific parameterisation to vary across OWTs (i.e. multiple algorithms, multiple parameterisations). This adaptive framework for the dynamic selection of in-water algorithms is shown to provide overall improvement in Chla retrieval across a continuum of bio-geo-optical conditions. The final dynamic ensemble algorithm produces estimates of (log10-transformed) Chla with a correlation coefficient of 0.89 and a mean absolute error of 0.18 mg m−3. The OWT framework presented in this study demonstrates a unified approach by bringing together an ensemble of algorithms for the monitoring of inland waters at a global scale from space.

MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia.

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

Remote sensing estimation of chlorophyll-a concentration in Taihu Lake considering spatial and temporal variations.

The estimation of chlorophyll-a concentrations (Chla) in lakes using remote sensing is convenient, but its use remains challenging in large eutrophic water bodies due to the great spatial and temporal variations of its optical properties. Combining the sampling location and date information with Chla data, this study divided the lake water into three types, I, II and III, and then built an optimal Chla estimation model for each type based on 11 datasets collected from 2004 to 2012 in Taihu Lake, China. The resultant model expression is Chla = exp (ax2 + bx + c), where x is R701/R677, (1/R686-1/R695) × R710 and (R690/R550-R675/R700)/(R690/R550 + R675/R700). For the Chla ranging from 2 to 192mg/m3, the root-mean-square error (RMSE) of the new model decreased up to 5.1mg/m3 compared to that of previous band combination models, such as band ratio, three-band and four-band models when directly validated. The RMSE of the re-parametrization model (the lowest RMSE < 12mg/m3) is also lower than for those models (the lowest RMSE > 16mg/m3), indicating that the Chla estimation model that considers the spatial and temporal variations has a better performance and validation accuracy and therefore is more effective for remote sensing monitoring of water quality.

Spatio-temporal remote sensing of vertical and seasonal chlorophyll-a:b ratio in Tamarix ramosissima

ABSTRACTRemotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R2 > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRImod, R510–R570 nm) performed better than the original PRI (PRI, R531–R570 nm) with R2 improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRImod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir, Beijing, China.

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R2, RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.

Implications of irradiance exposure and non-photochemical quenching for multi-wavelength (bbe FluoroProbe) fluorometry

Multi-wavelength fluorometers, such as the bbe FluoroProbe (FP), measure excitation spectra of chlorophyll a (Chl-a) fluorescence to infer the abundance and composition of phytoplankton communities as well as the concentration of chromophoric dissolved organic matter (CDOM). Experiments were conducted on laboratory cultures and on natural communities of freshwater phytoplankton to determine how the response of phytoplankton to high irradiance might affect fluorometric estimates of community composition and concentrations of Chl-a and CDOM. Cultures of a representative cyanobacterium, bacillariophyte, synurophyte, cryptophyte, and chlorophyte revealed changes in Chl-a excitation spectra as irradiance was increased to saturating levels and non-photochemical quenching (NPQ) increased. The degree of change and resulting classification error varied among taxa, being strong for the synurophyte and cryptophyte but minimal for the cyanobacterium. Acute-exposure experiments on phytoplankton communities of varying taxonomic composition from five lakes yielded variable results on apparent community composition. There was a consistent decrease in CDOM estimates, whereas Chl-a estimates were generally increased. Subsequent exposure to low PAR relaxed NPQ and tended to reverse the effects of high irradiance on composition, total Chl-a, and CDOM estimates. Relaxation experiments on near-surface communities in a sixth, large lake, Georgian Bay, showed that total Chl-a estimates increased by 44% on average when dark treatments were used to relax NPQ, though, in contrast to the findings from the small lakes, there was little effect on CDOM estimates. We observed a statistically-significant, negative linear relationship between the photon flux density of in situ irradiance and the accuracy of taxonomic assignment by FP in Georgian Bay. Not discounting the correlations between light intensity and the accuracy of the FP that were observed in this study, we conclude that the applicability of the reference spectra to the system under investigation is a more important consideration than variability in natural irradiance conditions.