Remote sensing of water clarity in Tampa Bay

We examined the spatial and temporal variability of the Secchi Disk Depth (SDD) within Tampa Bay, Florida, using the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) satellite imagery collected from September 1997 to December 2005. SDD was computed using a two-step process, first estimating the diffuse light attenuation coefficient at 490 nm, K_d(490), using a semi-analytical algorithm and then SDD using an empirical relationship with K_d(490). The empirical SDD algorithm (SDD = 1.04 × K_d(490)^− 0.82, 0.9 < SDD < 8.0 m, r² = 0.67, n = 80) is based on historical SDD observations collected by the Environmental Protection Commission of Hillsborough County (EPCHC) in Tampa Bay. SeaWiFS derived SDD showed distinctive seasonal variability, attributed primarily to chlorophyll concentrations and color in the rainy season and to turbidity in the dry season, which are in turn controlled by river runoff and winds or wind-induced sediment resuspension, respectively. The Bay also experienced strong interannual variability, mainly related to river runoff variability. As compared to in situ single measurements, the SeaWiFS data provide improved estimates of the “mean” water clarity conditions in this estuary because of the robust, frequent, and synoptic coverage. Therefore we recommend incorporation of this technique for routine monitoring of water quality in coastal and large estuarine waters like Tampa Bay.     

Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery

A retrieval algorithm, of total suspended matter (TSM) concentration in the Yellow Sea (YS) and East China Sea (ECS) was developed using observations made in the 2003 Spring and Autumn cruises over the YS and the ECS. Analysis of the in-situ backscattering coefficients of the suspended particles (b_bp) indicates that the accuracy becomes worse when the concentration of TSM (C_TSM) is higher than 20 mg/l. The accuracy of the b_bp is improved by using a bio-optical model in which b_bp is optimized with a non-linear least-square Levenberg-Marquardt method. The remote sensing reflectance (R_rs) is obtained by means of the optimization. The optimized R_rs for waters with C_TSM higher than 20 mg/l, together with the measured R_rs for waters with C_TSM lower than 20 mg/l, are used to establish the relationships between R_rs(748), R_rs(869) and R_rs(645), which are used in the iterative method for atmospheric correction. Two atmospheric correction algorithms are switched according to the water turbidity. The shortwave infrared wavelengths (SWIR) method is used for waters with high-turbidity, and the iterative method is used otherwise. Results of the atmospheric correction were then applied to the Tassan model modified in this paper to compute the C_TSM. Comparison between the retrieval results from MODIS imagery and the in-situ measurements indicates that the algorithms described in this paper can provide a reliable estimation of the C_TSM distributions in the YS and ECS.     

Estimating chlorophyll a concentrations from remote-sensing reflectance in optically shallow waters

A multi-spectral classification and quantification technique is developed for estimating chlorophyll a concentrations, Chl, in shallow oceanic waters where light reflected by the bottom can contribute significantly to the above-water remote-sensing reflectance spectra, R_rs(λ). Classification criteria for determining bottom reflectance contributions for shipboard R_rs(λ) data from the west Florida shelf and Bahamian waters (1998-2001; n = 451) were established using the relationship between R_rs(412)/R_rs(670) and the spectral curvature about 555 nm, [R_rs(412) ? R_rs(670)]/R_rs(555)². Chlorophyll concentrations for data classified as “optically deep” and “optically shallow” were derived separately using best-fit cubic polynomial functions developed from the band-ratios R_rs(490)/R_rs(555) and R_rs(412)/R_rs(670), respectively. Concentrations for transitional data were calculated from weighted averages of the two derived values. The root-mean-square error (RMSE_log10) calculated for the entire data set using the new technique was 14% lower than the lowest error derived using the best individual band-ratio. The standard blue-to-green, band-ratio algorithm yields a 26% higher RMSE_log10 than that calculated using the new method. This study demonstrates the potential of quantifying chlorophyll a concentrations more accurately from multi-spectral satellite ocean color data in oceanic regions containing optically shallow waters.     

Spatial coherence between remotely sensed ocean color data and vertical distribution of lidar backscattering in coastal stratified waters

Detection of sub-surface optical layers in marine waters has important applications in fisheries management, climate modeling, and decision-based systems related to military operations. Concurrent changes in the magnitude and spatial variability of remote sensing reflectance (R_rs) ratios and submerged scattering layers were investigated in coastal waters of the northern Gulf of Alaska during summer of 2002 based on high resolution and simultaneous passive (MicroSAS) and active (Fish Lidar Oceanic Experimental, FLOE) optical measurements. Principal Component Analysis revealed that the spatial variability of total lidar backscattering signal (S) between 2.1 and 20 m depth was weakly associated with changes in the inherent optical properties (IOPs) of surface waters. Also based on a 250-m footprint, the vertical attenuation of S was inversely related to the IOPs (Spearman Rank Correlation up to −0.43). Low (arithmetic average and standard deviation) and high (skewness and kurtosis) moments of R_rs(443)/R_rs(490) and R_rs(508)/R_rs(555) ratios were correlated with vertical changes in total lidar backscattering signal (S) at different locations. This suggests the use of sub-pixel ocean color statistics to infer the spatial distribution of sub-surface scattering layers in coastal waters characterized by stratified conditions, well defined S layers (i.e., magnitude of S maximum comparable to near surface values), and relatively high vertically integrated phytoplankton pigments in the euphotic zone (chlorophyll a concentration > 150 mg m^− 2).     

Estimating phytoplankton biomass in coastal waters of Alaska using airborne remote sensing

Empirical airborne remote-sensing relationships were examined to estimate chlorophyll a concentration in the first optical depth (chl_FOD) of coastal waters of Afgonak/Kodiak Islands during July-August 2002. Band-ratio and spectral-curvature models were tested using satellite remote-sensing reflectance (R_rs(λ)) measurements. Additional shipboard and airborne R_rs(λ) data were also analysed to evaluate consistency of proposed chl_FOD-R_rs(λ) relationships. Validation of chlorophyll algorithms was performed using data collected in the northern-part of the Gulf of Alaska and Bering Sea during 1996, 2002, and 2003 cruises. Likewise, oceanographic conditions during the surveys were typified to interpret variability of chl_FOD fields. The SeaWiFS band-ratio algorithm OC2d was the most sensitive R_rs combination (R_rs(509)/R_rs(553)) to detect chl_FOD variability. Conversely, OC2a (R_rs(412)/R_rs(553)) had the lowest performance to derive chl_FOD values. No valid statistical regressions were established for spectral-curvature relationships in the blue spectrum (< 500 nm). Fertile waters (> 5 mg m^− 3) were preferentially located over shallow banks (∼50 m) and at the entrance of the bays. The approach used in this study to derive chl_FOD values could be universal for Alaskan coastal waters. However, chl_FOD-R_rs(λ) relationships must be calibrated locally for a given season.     

An assessment of chlorophyll-a algorithms available for SeaWiFS in coastal and open areas of the Bay of Bengal and Arabian Sea

Three ocean colour algorithms, OC4v6, Carder and OC5 were tested for retrieving Chlorophyll-a (Chla) in coastal areas of the Bay of Bengal and open ocean areas of the Arabian Sea. Firstly, the algorithms were run using ~ 80 in situ Remote Sensing Reflectance, (R_rs(λ)) data collected from coastal areas during eight cruises from January 2000 to March 2002 and the output was compared to in situ Chla. Secondly, the algorithms were run with ~ 20 SeaWiFS R_rs(λ) and the results were compared with coincident in situ Chla. In both cases, OC5 exhibited the lowest log₁₀-RMS, bias, had a slope close to 1 and this algorithm appears to be the most accurate for both coastal and open ocean areas. Thirdly the error in the algorithms was regressed against Total Suspended Material (TSM) and Coloured Dissolved Organic Material (CDOM) data to assess the co-variance with these parameters. The OC5 error did not co-vary with TSM and CDOM. OC4v6 tended to over-estimate Chla > 2 mg m⁻³ and the error in OC4v6 co-varied with TSM. OC4v6 was more accurate than the Carder algorithm, which over-estimated Chla at concentrations > 1 mg m⁻³ and under-estimated Chla at values < 0.5 mg m⁻³. The error in Carder Chla also co-varied with TSM. The algorithms were inter-compared using > 5500 SeaWiFS R_rs(λ) data from coastal to offshore transects in the Northern Bay of Bengal. There was good agreement between OC4v6 and OC5 in open ocean waters and in coastal areas up to 2 mg m⁻³. There was a strong divergence between Carder and OC5 in open ocean and coastal waters. OC4v6 and Carder tended to over-estimate Chla in coastal areas by a factor of 2 to 3 when TSM > 25 g m⁻³. We strongly recommend the use of OC5 for coastal and open ocean waters of the Bay of Bengal and Arabian Sea. A Chla time series was generated using OC5 from 2000 to 2003, which showed that concentrations at the mouths of the Ganges reach a maxima (~ 5 mg m⁻³) in October and November and were 0.08 mg m⁻³ further offshore increasing to 0.2 mg m⁻³ during December. Similarly in early spring from February to March, Chla was 0.08 to 0.2 mg m⁻³ on the east coast of the Bay.     

Remote sensing assessment of sediment re-suspension during Hurricane Frances in Apalachicola Bay, USA

Assessments of hurricane-induced environmental impacts are important to coastal management and risk analysis of ecosystems. In this study, a previously-developed remote sensing model for non-hurricane conditions by Wang et al. [Wang, H. Q., Hladik, C. M., Milla, K., Huang, W. R., Edmiston, L., Harwell, M. A., & Schalles, J. F. (in press). Detecting and mapping water quality indicators in Apalachicola Bay, Florida using MODIS Terra 250-m imagery. International Journal of Remote Sensing] has been substantially enhanced to investigate the impact of Hurricane Frances on total suspended solid (TSS) concentrations in Apalachicola Bay, Florida, USA. The remote sensing model uses 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) to map TSS concentrations in the Bay. Eleven additional satellite imageries of MODIS were used in the model improvement and calibration. TSS concentration computation in the present model has been substantially improved by using a two-step process: firstly producing atmospheric correction intercept by an approach of in-water reflectance regression, and then building the regression model (R² = 0.8534, n = 25) between 250-m MODIS reflectance and observed TSS concentrations, which includes an extreme high TSS concentration data of 208 mg/L for severe storm or hurricane condition. Also, we carried out the validation of model (RMSE = 5.5 mg/L, n = 21). MODIS-derived TSS maps show substantial increases of TSS concentrations in the Bay during the passage of Hurricane Frances (the average TSS and maximum concentration about 54.3 mg/L and 165 mg/L in the Bay respectively) compared to under no-storm or -hurricane condition ( the average TSS and maximum concentration were approximately 24-27 mg/L and 58-64 mg/L). In comparison to those before and 5-days after the passage of the hurricane, the average TSS concentration in the Bay was twice higher while the maximum TSS concentration increased almost three times during the hurricane. This indicates that strong winds during the hurricane have caused strong sediment re-suspension. The spatial variations of TSS concentrations were analyzed by applying the hydrodynamic characteristics of wind-induced flow and tidal currents as described by Huang [Huang, W., Jones, K., & Wu, T. (2002). Modeling surface wind effects on subtidal salinity in Apalachicola Bay. Estuarine, Coastal and Shelf Science, 55(1), 33−46; Huang, W., Sun, H., Nnaji, S., & Jones, K. (2002). Tidal hydrodynamics in a multiple inlet estuary: Apalachicola Bay. International Journal of Coastal Research, 18(4), 674−684], which show westward currents in the Bay under westward wind condition. Therefore, the southwestward wind (about 50° from the north) during the hurricane induced southwestward currents and transport that resulted in the high TSS concentrations near West Pass in the Bay and the Gulf. Within the Bay, TSS concentrations were generally higher in the southern portion of the Bay, which was due mainly to transport by the combination of southwestward wind and southward residual flow from the Apalachicola River.     

A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation

Accurate assessment of phytoplankton chlorophyll-a (chla) concentrations in turbid waters by means of remote sensing is challenging due to the optical complexity of case 2 waters. We have applied a recently developed model of the form [R_rs^? 1(λ₁) ? R_rs^? 1(λ₂)] × R_rs(λ₃) where R_rs(λ_i) is the remote-sensing reflectance at the wavelength λ_i, for the estimation of chla concentrations in turbid waters. The objectives of this paper are (a) to validate the three-band model as well as its special case, the two-band model R_rs^? 1(λ₁) × R_rs(λ₃), using datasets collected over a considerable range of optical properties, trophic status, and geographical locations in turbid lakes, reservoirs, estuaries, and coastal waters, and (b) to evaluate the extent to which the three-band model could be applied to the Medium Resolution Imaging Spectrometer (MERIS) and two-band model could be applied to the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate chla in turbid waters.The three-band model was calibrated and validated using three MERIS spectral bands (660–670 nm, 703.75–713.75 nm, and 750?757.5 nm), and the 2-band model was tested using two MODIS spectral bands (λ₁ = 662–672, λ₃ = 743–753 nm). We assessed the accuracy of chla prediction in four independent datasets without re-parameterization (adjustment of the coefficients) after initial calibration elsewhere. Although the validation data set contained widely variable chla (1.2 to 236 mg m^? 3), Secchi disk depth (0.18 to 4.1 m), and turbidity (1.3 to 78 NTU), chla predicted by the three-band algorithm was strongly correlated with observed chla (r² > 0.96), with a precision of 32% and average bias across data sets of ? 4.9% to 11%. Chla predicted by the two-band algorithm was also closely correlated with observed chla (r² > 0.92); however, the precision declined to 57%, and average bias across the data sets was 18% to 50.3%. These findings imply that, provided that an atmospheric correction scheme for the red and NIR bands is available, the extensive database of MERIS and MODIS imagery could be used for quantitative monitoring of chla in turbid waters.     

Estimating northern peatland CO2 exchange from MODIS time series data

Studies using satellite sensor-derived data as input to models for CO₂ exchange show promising results for closed forest stands. There is a need for extending this approach to other land cover types, in order to carry out large-scale monitoring of CO₂ exchange. In this study, three years of eddy covariance data from two peatlands in Sweden were averaged for 16-day composite periods and related to data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled photosynthetic photon flux density (PPFD). Noise in the time series of MODIS 250 m vegetation indices was reduced by using double logistic curve fits. Smoothed normalized difference vegetation index (NDVI) showed saturation during summertime, and the enhanced vegetation index (EVI) generally gave better results in explaining gross primary productivity (GPP). The strong linear relationships found between GPP and the product of EVI and modeled PPFD (R² = 0.85 and 0.76) were only slightly stronger than for the product of EVI and MODIS daytime 1 km land surface temperature (LST) (R² = 0.84 and 0.71). One probable reason for these results is that several controls on GPP were related to both modeled PPFD and daytime LST. Since ecosystem respiration (ER) was largely explained by diurnal LST in exponential relationships (R² = 0.89 and 0.83), net ecosystem exchange (NEE) was directly related to diurnal LST in combination with the product of EVI and modeled PPFD in multiple exponential regressions (R² = 0.81 and 0.73). Even though the R² values were somewhat weaker for NEE, compared to GPP and ER, the RMSE values were much lower than if NEE would have been estimated as the sum of GPP and ER. The overall conclusion of this study is that regression models driven by satellite sensor-derived data and modeled PPFD can be used to estimate CO₂ fluxes in peatlands.     

Bio-optical algorithm evaluation for MODIS for western Canada coastal waters: An exploratory approach using in situ reflectance

Biophysical and above-water reflectance measurements collected in 2006 were used to evaluate the OC3M, standard GSM01, and a modified version of the GSM01 algorithms for estimating chlorophyll-a (chl) concentrations in the Strait of Georgia, located off the southwest coast of Canada. The Strait was generally a case 2 water body, transitioning from chromophoric dissolved organic matter (CDOM) dominant in the central region to possibly particulate dominant in Fraser River plume regions. Results showed that the OC3M algorithm was somewhat effective (R² = 0.550) outside the most turbid areas of the Fraser River plume. However, a systematic overestimation of lower chl concentrations was found, which may have been related to the higher CDOM absorption observed throughout the Strait. The standard GSM01 algorithm had moderately good agreement with measured CDOM absorption (R² = 0.593) and total suspended solids (TSS) concentrations (R² = 0.888), but was ineffective at estimating chl concentrations. Localized characterization of the CDOM absorption, through a hyperbolic CDOM model, improved the modified GSM01 results with slightly better agreement with measure CDOM absorption (R² = 0.614) and TSS concentrations (R² = 0.933). When the modified GSM01 algorithm was limited to regions with lower combined CDOM and non-algal particulate absorption (a_dg (443) < 0.7 m^− 1), it was more effective then the OC3M algorithm at estimating chl concentrations. This suggests that a threshold value on the a_dg (443) or b_bp (443) estimated by the GSM01 algorithm may be beneficial for limiting turbidity influence on the algorithm. The further reinterpretation of phytoplankton absorption from the modified GSM01 algorithm with a two-component phytoplankton model resulted in a chl relationship with an R² = 0.677 and a linear slope closer to one.     

Bio-optical properties in waters influenced by the Mississippi River during low flow conditions

Spatial and temporal patterns of bio-optical properties were studied in the Northern Gulf of Mexico during cruises in April and October of 2000, a year during which the discharge volume from the Mississippi River was unusually low. Highly variable surface Chl a concentrations (0.1 to 17 mg m⁻³) and colored dissolved organic matter (CDOM) absorption (0.07 to 1.1 m⁻¹ at 412 nm) were observed in the study region that generally decreased with increasing salinity waters, being highest nearshore and decreasing at offshore stations. The optical properties of absorption, scattering, and diffuse attenuation coefficients reflected these distributions with phytoplankton particles and CDOM contributing to most of the spatial, vertical, and seasonal variability. The diffuse attenuation coefficient K_d(λ) and spectral remote sensing reflectance R_rs(λ) were linear functions of absorption and backscattering coefficients a(λ) and b_b(λ) through the downward average cosine μ_d and the ratio of variables f/Q at the SeaWiFS wavebands for waters with widely varying bio-optical conditions. Although various R_rs(λ) ratio combinations showed high correlation with surface Chl a concentrations and CDOM absorption at 412 nm, power law equations derived using the R_rs(490)/R_rs(555) and R_rs(510)/R_rs(555) ratios provided the best retrievals of Chl a concentrations and CDOM absorption from SeaWiFS reflectance data.     

Variability and classification of remote sensing reflectance spectra in the eastern English Channel and southern North Sea

During spring and summer 2004, intensive field bio-optical campaigns were conducted in the eastern English Channel and southern North Sea to assess the mechanisms regulating the ocean color variability in a complex coastal environment. The bio-optical properties of the sampled waters span a wide range of variability, due to the various biogeochemical and physical processes occurring in this area. In-water hyperspectral remote sensing reflectances (R_rs) were acquired simultaneously with measurements of optically significant parameters at 93 stations. An empirical orthogonal function (EOF) analysis indicates that 74% of the total variance of R_rs is partly explained by particulate backscattering (b_bp), while particulate and dissolved absorption only explain 15% of the ocean color variability. These results confirm, for the first time from in situ backscattering measurements, previous studies performed in other coastal environments. Whereas the amplitude factors of the first EOF mode are well correlated (r = 0.75) with the particulate backscattering coefficient (b_bp), the highest correlation (r = 0.83) is found with the particulate backscattering ratio (b_bp/b_p). This result highlights the fundamental role of the nature of the bulk particulate assemblage in the ocean color variability.An unsupervised hierarchical cluster analysis applied to our data set of normalized R_rs spectra, leads to five spectrally distinct classes. We show that the class-specific mean R_rs spectra significantly differ from one another by their bio-optical properties. Three classes particularly stand out: one class corresponds to a Phaeocystis globosa bloom situation, whereas the two others are associated with water masses dominated by mineral and non-living particles, respectively. Among the different bio-optical parameters, the particulate backscattering ratio, the chlorophyll concentration, and the particulate organic carbon to chlorophyll ratio, are the most class-specific ones. These different results are very encouraging for the inversion of bio-optical parameters from class-specific algorithms.     

Retrieval of total suspended matter concentration from Gaofen-1 Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water – case in Deep Bay

This study proposed a method for developing high spatial resolution Gaofen-1 satellite (GF-1) Wide Field Imager (WFI)-based total suspended matter concentration (C_TSM) retrieval model with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using the Deep Bay in China as a case. Based on long-term calibrated C_TSM measurements of optical backscatter (OBS) 3A turbidity and temperature monitoring system of two stationary stations from January 2007 through November 2008, 33 match-ups were selected to build an exponential retrieval model for MODIS atmospherically corrected remote-sensing reflectance (R_rs) ratio (R_rs,645/R_rs,555). Validation of the MODIS model showed well agreement with the seven in situ C_TSM measurements with a root mean squared error (RMSE) of 5.06 mg l^?1 and a coefficient of determination R² of 0.80. Aided with six MODIS retrieved C_TSM products, different band combinations (single band (R_rc,660), band subtraction (R_rc,660–R_rc,560), band ratio (R_rc,660/R_rc,560), and total suspended matter index at 660 nm band (TSMI₆₆₀) were evaluated for simultaneous GF-1 WFI Rayleigh-corrected reflectance (R_rc). The results showed that the exponential model based on the Rayleigh-corrected reflectance ratio (R_rc,660/R_rc,560) could achieve acceptable accuracy, with RMSE of 14.80 mg l^?1 and R² of 0.62. The proposed method would be helpful for dynamic monitoring in the Deep Bay, and more important could also provide an alternative approach for studies when in situ measurements are unreachable.     

Comparisons of algorithms to estimate water turbidity in the coastal areas of China

Turbidity is an important indicator of water environments and water-quality conditions. Ocean colour remote sensing has proved to be an efficient way of monitoring water turbidity because of its wide synoptic coverage and repeated regular sampling. However, operational tasks are still challenging in high-turbidity waters, especially in estuaries and the coastal regions of China. In these areas, the existing algorithms derived from remote-sensing reflectance (R_rs) are usually invalid because it is difficult to correctly estimate the reflectance R_rs from satellite data such as Moderate Resolution Imaging Spectroradiometer (MODIS) data. A new algorithm that uses Rayleigh-corrected reflectance (R_rc) instead of R_rs has been recently introduced and was used to estimate water turbidity in Zhejiang (ZJ) coastal areas from Geostationary Ocean Color Imager (GOCI) data. The R_rc algorithm has previously shown a capability to estimate water turbidity. However, its performance still requires careful evaluation. In this article, we compared the new R_rc algorithm with two other existing algorithms. Differences among the three algorithms were assessed by comparing the results from using R_rc data and R_rs reflectance data derived from both GOCI and MODIS imagery data. The capability of the new R_rc algorithm to estimate water turbidity in larger areas and extended seasons in the coastal seas of China was also estimated. The results showed that the new R_rc algorithm is suitable for the coastal waters of China, especially for highly turbid waters.     

Ocean sand ridge signatures in the Bohai Sea observed by satellite ocean color and synthetic aperture radar measurements

Satellite measurements from Synthetic Aperture Radar (SAR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua platform are used to study the ocean sand ridges in the eastern Bohai Sea in China. Even though the imaging mechanisms for SAR and MODIS-Aqua remote sensing are different, the sand ridges are shown to have exactly the same patterns in images from both sensors. Therefore, the location, extension and coverage of the ocean sand ridges can be detected and cross-examined by both SAR and MODIS-Aqua observations. Satellite images show quite different sand ridge distribution pattern from the published bathymetry map (based on in situ data) that shows six sand ridges in the area. 10 finger-shaped sand ridges are identified from satellite observations. The tidal-current/sand-ridge interaction driven physical and optical changes are assessed and evaluated. The existence of sand ridges causes enhanced water diffuse attenuation coefficient K_d(490) and elevated normalized water-leaving radiance at the red and near-infrared (NIR) wavelengths. The sea surface over the sand ridges experiences significant seasonal variability of water turbidity and shows remarkable differences from nearby ocean regions. During winter, K_d(490) values are about 2-3 m^− 1 over the ridges, while the maximum K_d(490) in the neighboring oceans is approximately 1.5 m^− 1. In summer, the enhancement of the sea surface turbidity is less significant than that which occurs in winter.     

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Bio-optical algorithms for remote estimation of chlorophyll-a concentration (Chl) in case-1 waters exploit the upwelling radiation in the blue and green spectral regions. In turbid productive waters other constituents, that vary independently of Chl, absorb and scatter light in these spectral regions. As a consequence, the accurate estimation of Chl in turbid productive waters has so far not been feasible from satellite sensors. The main purpose of this study was to evaluate the extent to which near-infrared (NIR) to red reflectance ratios could be applied to the Sea Wide Field-of-View Sensor (SeaWiFS) and the Moderate Imaging Spectrometer (MODIS) to estimate Chl in productive turbid waters. To achieve this objective, remote-sensing reflectance spectra and relevant water constituents were collected in 251 stations over lakes and reservoirs with a wide variability in optical parameters (i.e. 4 ≤ Chl ≤ 240 mg m^− 3; 18 ≤ Secchi disk depth ≤ 308 cm). SeaWiFS and MODIS NIR and red reflectances were simulated by using the in-situ hyperspectral data. The proposed algorithms predicted Chl with a relative random uncertainty of approximately 28% (average bias between − 1% and − 4%). The effects of reflectance uncertainties on the predicted Chl were also analyzed. It was found that, for realistic ranges of R_rs uncertainties, Chl could be estimated with a precision better than 40% and an accuracy better than ± 35%. These findings imply that, provided that an atmospheric correction scheme specific for the red-NIR spectral region is available, the extensive database of SeaWiFS and MODIS images could be used to quantitatively monitor Chl in turbid productive waters.     

Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: Initial results from Tampa Bay, FL

Using Tampa Bay, FL as an example, we explored the potential for using MODIS medium-resolution bands (250- and 500-m data at 469-, 555-, and 645-nm) for estuarine monitoring. Field surveys during 21-22 October 2003 showed that Tampa Bay has Case-II waters, in that for the salinity range of 24-32 psu, (a) chlorophyll concentration (11 to 23 mg m⁻³), (b) colored dissolved organic matter (CDOM) absorption coefficient at 400 nm (0.9 to 2.5 m⁻¹), and (c) total suspended sediment concentration (TSS: 2 to 11 mg L⁻¹) often do not co-vary. CDOM is the only constituent that showed a linear, inverse relationship with surface salinity, although the slope of the relationship changed with location within the bay. The MODIS medium-resolution bands, although designed for land use, are 4-5 times more sensitive than Landsat-7/ETM+ data and are comparable to or higher than those of CZCS. Several approaches were used to derive synoptic maps of water constituents from concurrent MODIS medium-resolution data. We found that application of various atmospheric-correction algorithms yielded no significant differences, due primarily to uncertainties in the sensor radiometric calibration and other sensor artifacts. However, where each scene could be groundtruthed, simple regressions between in situ observations of constituents and at-sensor radiances provided reasonable synoptic maps. We address the need for improvements of sensor calibration/characterization, atmospheric correction, and bio-optical algorithms to make operational and quantitative use of these medium-resolution bands.     

Assessing structural effects on PRI for stress detection in conifer forests

The retrieval of indicators of vegetation stress from remote sensing imagery is an important issue for the accurate assessment of forest decline. The Photochemical Reflectance Index (PRI) has been demonstrated as a physiological index sensitive to the epoxidation state of the xanthophyll cycle pigments and to photosynthetic efficiency, serving as a proxy for short-term changes in photosynthetic activity, stress condition, and pigment absorption, but highly affected by illumination conditions, viewing geometry and canopy structure. In this study, a diurnal airborne campaign was conducted over Pinus sylvestris and Pinus nigra forest areas with the Airborne Hyperspectral Scanner (AHS) to evaluate the effects of canopy structure on PRI when used as an indicator of stress in a conifer forest. The AHS airborne sensor was flown at two times (8:00 GMT and 12:00 GMT) over forest areas under varying field-measured stress levels, acquiring 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Five formulations of PRI (based on R₅₃₁ as a xanthophyll-sensitive spectral band) were calculated using different reference wavelengths, such as PRI₅₇₀ (reference band R_REF = R₅₇₀), and the PRI modifications PRI_m1 (R_REF = R₅₁₂), PRI_m2 (R_REF = R₆₀₀), PRI_m3 (R_REF = R₆₇₀), and PRI_m4 (R_REF = R₅₇₀, R₆₇₀), along with other structural indices such as NDVI, SR, OSAVI, MSAVI and MTVI2. In addition, thermal bands were used for the retrieval of the land surface temperature. A radiative transfer modeling method was conducted using the LIBERTY and INFORM models to assess the structural effects on the PRI formulations proposed, studying the sensitivity of PRI_m indices to detect stress levels while minimizing the effects caused by the conifer architecture. The PRI indices were related to stomatal conductance, xanthophyll epoxidation state (EPS) and crown temperature. The modeling analysis showed that the coefficient of variation (CV) for PRI was 50%, whereas the CV for PRIm1 (band R512 as a reference) was only 20%. Simulation and experimental results demonstrated that PRI_m1 (R_REF = R₅₁₂) was less sensitive than PRI (R_REF = R₅₇₀) to changes in Leaf Area Index (LAI) and tree densities. PRI₅₁₂ was demonstrated to be sensitive to EPS at both leaf (r² = 0.59) and canopy level (r² = 0.40), yielding superior performance than PRI₅₇₀ (r² = 0.21) at the canopy level. In addition, PRI₅₁₂ was significantly related to water stress indicators such as stomatal conductance (Gs; r² = 0.45) and water potential (Ψ; r² = 0.48), yielding better results than PRI₅₇₀ (Gs, r² = 0.21; Ψ, r² = 0.21) due to the structural effects found on the PRI₅₇₀ index at the canopy level.