Optical closure for remote-sensing reflectance based on accurate radiative transfer approximations: the case of the Changjiang (Yangtze) River Estuary and its adjacent coastal area,China

Optical closure exercises are pivotal for evaluating the accuracy of water quality remote-sensing techniques. The agreement between radiometrically derived and inherent optical property (IOP)-derived above-water spectral remote-sensing reflectance R_rs(λ) is necessary for resolving IOPs, the diffuse attenuation coefficient, and biogeochemical parameters from space. We combined spectral radiometric and IOP measurements to perform an optical closure exercise for two optically contrasting Chinese waters – the Changjiang (Yangtze) River Estuary and its adjacent coastal area in the East China Sea. The final aim of our investigation was to compare two derivations of R_rs(λ): R_rs(λ), derived from radiometric measurements; and R_rs(λ), derived from simultaneous IOP measurements. Five subsequent steps have been taken to achieve this goal, including (1) estimation of the R_rs(λ) from radiometric measurements; (2) scattering correction for the non-water spectral absorption coefficient a_pd(λ); (3) estimation of the below-water spectral remote-sensing reflectance r_rs(λ) from IOPs measurements; (4) the estimation of the R_rs(λ) from the r_rs(λ) values; and (5) the comparison between the R_rs(λ) derived from radiometric and IOP measurements. All steps were realized by using both direct measurements and different models based on radiative transfer theory. Results demonstrated that the impact of the errors caused by the scattering correction procedure and conversion of radiometric quantities into R_rs(λ) may be rather significant, especially in the long-wavelength spectrum range. Nevertheless, spectral features were similar between these R_rs(λ) sets for all waters – from relatively clear to very turbid. Exploiting this fact allows use of the spectral reflectance ratios for remote sensing of the estuarine and coastal Chinese waters.     

Estimation of chlorophyll-a concentration in coastal waters with HJ-1A HSI data using a three-band bio-optical model and validation

Accurate assessment of phytoplankton chlorophyll-a (chl-a) concentration in turbid waters by means of remote sensing is challenging because of the optical complexity of case 2 waters. We applied a bio-optical model of the form [R^–1(λ₁) – R^–1(λ₂)](λ₃), where R(λ_i) is the remote-sensing reflectance at wavelength λ_i, to estimate chl-a concentration in coastal waters. The objectives of this article are (1) to validate the three-band bio-optical model using a data set collected in coastal waters, (2) to evaluate the extent to which the three-band bio-optical model could be applied to the spectral radiometer (SR) ISI921VF-512T data and the hyperspectral imager (HSI) data on board the Chinese HJ-1A satellite, (3) to evaluate the application prospects of HJ-1A HSI data in case 2 waters chl-a concentration mapping. The three-band model was calibrated using three SR spectral bands (λ₁ = 664.9 nm, λ₂ = 706.54 nm, and λ₃ = 737.33 nm) and three HJ-1A HSI spectral bands (λ₁ = 637.725 nm, λ₂ = 711.495 nm, and λ₃ = 753.750 nm). We assessed the accuracy of chl-a prediction with 21 in situ sample plots. Chl-a predicted by SR data was strongly correlated with observed chl-a (R² = 0.93, root mean square error (RMSE) = 0.48 mg m^–3, coefficient of variation (CV) (RMSE/mean(chl-a_mea)) = 3.72%). Chl-a predicted by HJ-1A HSI data was also closely correlated with observed chl-a (R² = 0.78, RMSE = 0.45 mg m^–3, CV (RMSE/mean(chl-a_mea)) = 7.51%). These findings demonstrate that the HJ-1A HSI data are promising for quantitative monitoring of chl-a in coastal case-2 waters.     

Retrieving seawater turbidity from Landsat TM data by regressions and an artificial neural network

The radiance reflected at the sea surface (R_W (λ)) of the Ariake Sea, Japan, was first estimated by subtracting Lowtran 7 estimated Rayleigh and aerosol scattered radiances from Landsat Thematic Mapper measured radiance. Then R_W (λ) was averaged from 4×4 pixel windows centred at 33 sampling sites of the Ariake Sea and the data calibrated against the observed Secchi disk depth (SDD) using linear (LR) and nonlinear (NLR) regressions, and an artificial neural network (ANN) algorithm called the Modified Counter Propagation Network (MCPN). We found that at the validation stage, multi-date R_W (λ) data that are mainly based on the visible channels of Landsat Thematic Mapper (TM) predict more accurate and dependable SDDs than single-date R_W (λ) data. Furthermore, the NLR describes the SDD/R_W (λ) relationship more closely than the LR. As an ANN, MCPN possesses non-linearity, inter-connectivity, and an ability to learn and generalize information from complex or poorly understood systems, which enables it to even better represent the SDD/R_W (λ) relationship than the NLR. Our study confirms the feasibility of retrieving SDD (or turbidity) from Landsat TM data, and it seems that the calibrated MCPN and possibly NLR are portable temporally within the Ariake Sea. Lastly, the coefficient of efficiency E_f is a more stringent and probably a more accurate statistical measure than the popular coefficient of determination R ².     

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.     

Mapping aerosol optical thickness and water-leaving signals using an improved NIR/SWIR switching atmospheric correction model: A case study in the Bohai Sea

In this study, the performance of the near-infrared & short wave infrared switching atmospheric correction (NSSAC) model in estimating remote sensing reflectance (R_rs(λ)) and aerosol optical thickness at 869 nm (τ_a(869)) were assessed by field measurements taken in the Bohai Sea. It was found that the NSSAC model had approximately 30% uncertainty for retrievals of R_rs(λ) in the green regions but provided approximately 50% uncertainty for estimations of τ_a(869) and R_rs(λ) at all other moderate resolution imaging spectroradiometer (MODIS) visible wavelengths. Therefore, an optimised method is proposed for optimizing the retrieval results of the NSSAC model; it was validated using the field measurements collected from the Oujiang River estuary. The results show that the performance of the NSSAC model for τ_a(869) and R_rs(λ) at the blue, red, and near-infrared bands was greatly improved by using the optimised NSSAC model. Moreover, the study also finds that the τ_a(869) shows a large variation in the Bohai Sea, decreasing from coastal to offshore regions. The monthly average τ_a(869) has a maximum at February and August. Due to the imperfect atmospheric correction procedure, the NSSAC model-derived R_rs(λ) is always larger than those of the field measurements. Future work is needed to minimise the detected water-leaving signals in the short wave infrared (SWIR) images.     

The effect of phytoplankton pigment composition and packaging on the retrieval of chlorophyll-a concentration from satellite observations in the Southern Ocean

The Antarctic waters are known to be optically unique and the standard empirical ocean colour algorithms applied to these waters may not address the regional bio-optical characteristics. This article sheds light on the performance of current empirical algorithms and a regionally optimized algorithm (ROA) for the retrieval of chlorophyll-a (chl-a) concentration from Aqua-Moderate Resolution Imaging Spectroradiometer (Aqua-MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) in the Indian Ocean Sector of Southern Ocean (IOSO). Analysis indicated that empirical algorithms used for the retrieval of chl-a concentration from Aqua-MODIS and SeaWiFS underestimate by a factor varying from 2 to 2.9, resulting in underestimation when in situ chl-a exceeds about 0.3 mg m^?3. To explain these uncertainties, a study was carried out to understand the effect of phytoplankton pigment composition and pigment packaging on remote-sensing reflectance (R_rs,λ), from the analysis of phytoplankton-specific absorption coefficient (a_ph,*_λ). The spatial variation of phytoplankton groups analysed using diagnostics pigments (DP) indicated shifting of the phytoplankton community structure from offshore to coastal Antarctic, with a significant increasing trend for diatoms and a decreasing trend for haptophytes population. The diatom-dominated population exhibits lower a_ph,*_λ in the 405–510 nm region (with relative flattening in 443–489 nm) compared with the a_ph,*_λ spectra of the haptophytes-dominated population that peaks near 443 nm. The flattening of a_ph,*_λ spectra for the diatom-dominated population was attributed to its larger cell size, which leads to pigment packaging (intracellular shading) and in turn results in higher R_rs,λ. The relationship between pigment composition (normalized by chl-a) and blue:green absorption band ratios (a_ph,*₄₄₃:a_ph,*₅₅₅ and a_ph,*₄₈₉:a_ph,*₅₅₅) corresponding to the Aqua-MODIS and SeaWiFS bands showed in-phase associations with most of the pigments such as 19?-hexanoyloxyfucoxanthin, 19?-butanoyloxyfucoxanthin, peridinin, and zeaxanthin. In contrast, the out-of-phase association observed between the blue:green absorption ratios and fucoxanthin indicated apparent deviations from the general pigment retrieval algorithms, which assumes that blue:green ratios vary in a systematic form with chl-a. The out-of-phase correspondence suggests that the increasing trend of fucoxanthin pigments towards the Antarctic coast was associated with the decreasing trend of blue:green absorption ratios and in turn results in higher R_rs,λ. Therefore, an increase in R_rs,λ leads to underestimation of chl-a from Aqua-MODIS and SeaWiFS in the IOSO region.     

A Penman-Monteith evapotranspiration model with bulk surface conductance derived from remotely sensed spatial contextual information

ABSTRACT

A novel approach involving the use of the contextual information in a scatter plot of Moderate Resolution Imaging Spectrometer (MODIS) derived Land Surface Temperature versus Fraction of Vegetation (LST vs. F_v) has been proposed in this study to obtain pixel-wise values of bulk surface conductance (G_s) for use in the Penman-Monteith (PM) model for latent heat flux (λ_ET) estimation. Using a general expression for G_s derived by assuming a two-source total λ_ET (canopy transpiration plus soil evaporation) approach proposed by previous researchers, minimum and maximum values of G_s for a given region can be inferred from a trapezoidal scatter plot of pixel-wise values of LST and corresponding F_v. Using these as limiting values, G_s values for each pixel can be derived through interpolation and subsequently used with the PM model to estimate λ_ET for each pixel. The proposed methodology was implemented in 5 km × 5 km areas surrounding each of four flux towers located in tropical south-east Asia. Using climate data from the tower and derived G_s values the PM model was used to obtain pixel-wise instantaneous λ_ET values on six selected dates/times at each tower. Excellent comparisons were obtained between tower measured λ_ET and those estimated by the proposed approach for all four flux tower locations (R² = 0.85–0.96; RMSE = 18.27–33.79 W m^–2). Since the LST- F_v trapezoidal method is simple, calibration-free and easy to implement, the proposed methodology has the potential to provide accurate estimates of regional evapotranspiration with minimal data inputs.     

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.     

Northern Gulf of Mexico estuarine coloured dissolved organic matter derived from MODIS data

Coloured dissolved organic matter (CDOM) is relevant for water quality management and may become an important measure to complement future water quality assessment programmes. An approach to derive CDOM using the Moderate Resolution Imaging Spectroradiometer (MODIS) was developed that may be more accessible to water quality managers by selecting an off-the-shelf software and algorithm with standard atmospheric correction. This study focused on demonstrating the transferability of a remote-sensing reflectance (R_rs) band ratio algorithm, R_rs(667)/R_rs(488), previously developed to derive CDOM absorption (a_g(λ)) at multiple MODIS wavebands in open ocean and coastal waters to the United States northern Gulf of Mexico estuaries. In situ calibration regressions at 412, 443, 469, and 488 nm had coefficients of determination (R²) of 0.76, 0.71, 0.69, and 0.57, respectively. Waveband calibrations at 531, 547, and 555 nm were below R² = 0.50, and therefore were not considered further. MODIS R_rs, from the standard atmospheric correction, followed nearly identical spectral shapes to the in situ HyperSAS R_rs, but were on average 0.002 ± 0.0004 sr^?1 less. A satellite to in situ validation match-up window of ≤1 hour was selected with an R² = 0.82 and root mean square error (RMSE = 1.79) at 412 nm. An in situ water quality mooring demonstrated that the overall response and range of MODIS a_g(412) were similar, with relative mean error from –32% to 42%. The advantage to managers was synoptic coverage across multiple estuaries and the ability to provide estimates of derived water quality parameters between the water quality assessment programme sample collection periods, which could offer more holistic assessment.     

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.     

Parameterization of the chlorophyll a-specific in vivo light absorption coefficient covering estuarine,coastal and oceanic waters

We evaluated models predicting the spectral chlorophyll-a (Chl a)-specific absorption coefficient (a* _ph (λ)) from Chl a concentration [Chl a] on the basis of 465 phytoplankton absorption spectra collected in estuarine, coastal and oceanic waters. A power model on ln-transformed data provided the best model fit compared to a power model on non-transformed data previously applied to parameterize the relationship between a* _ph (λ) and [Chl a]. The variation in a* _ph (λ) was parameterized over four orders of magnitude in [Chl a] (0.01-100 mg Chl a m^?3) producing a 13-fold range in a* _ph (0.19 to 0.015 m² mg^?1 Chl a) at 440 nm, the peak absorption of Chl a in the blue part of the spectrum. The variations in the modelled a* _ph spectra were within realistic predictions of a* _ph (λ) and the model satisfactorily reproduced the spectral flattening with increasing [Chl a]. The parameterization of a* _ph (λ) confirmed the indirect dependency of a* _ph (λ) on [Chl a] through co-variations between [Chl a] with pigment packaging and pigment composition. Although pigment packaging determined the spectral flattening, analysis of absorption ratios revealed a systematic change in pigment composition with profound influence on the variability of a* _ph in the 440 to 495 nm region. Modelled spectra deviated by approximately 20% from the measured spectra on average and model accuracy was independent of [Chl a]. Although the model cannot fully replace spectral measurements of phytoplankton absorption, it does permit realistic reconstructions of a* _ph (λ) from simple measurements of [Chl a] sampled in estuarine, coastal and oceanic waters.     

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.     

Relationship between maximum acceptable work time and physical workload

For safe job design, it is necessary to know the maximum acceptable work time (MAWT) for a given workload. The aim was to establish the relationship between MAWT and physical workload. Cycling tests at six different work rates relative to personal maximum working capacity were performed by 12 young adults in the laboratory. The oxygen uptake (VO₂) in the per cent maximum aerobic capacity (%VO_2max), relative heart rate (RHR) and relative oxygen uptake (RVO₂) were collected throughout the test. MAWT was determined by observing the heart rate data during the test. The results showed that MAWT was negatively correlated with %VO_2max, RHR and RVO₂(p<0.01). Three exponential regression models were obtained and all their R² >0.80. These models suggest that long-hour shifts (>10 h) should assign a lower work intensity than for an 8-h workday. It is also logical that the workload limit for a 4-h work shift could be set at about 10%VO_2max higher than the suggested limit for an 8-h workday.     

Remote sensing reflectance and its relationship to optical properties of natural waters

Monte Carlo simulations of photon propagation through natural water have been utilized to determine the sub-surface remote sensing reflectance, R _RSW (the sub-surface value of the ratio of upwelling radiance from the nadir to the downwelling irradiance) as a function of water type (defined by the ratio of the backscattering coefficient to the absorption coefficient Bb/a), solar zenith angle, and incident radiation distribution (direct or diffuse). R _RSW, as opposed to volume reflectance, R _V (the sub-surface value of the ratio of upwelling to downwelling vector irradiance), is directly applicable to remotely sensed data collected over natural waters. It is shown that, for a nadir viewing direction, (a) R _RSW is essentially independent of solar zenith angle and incident radiation distribution and (b) the dominant factor in determining R _RSW is the optical nature of the water body itself (expressed as Bb/a). A relationship between the sub-surface remote sensing reflectance averaged over solar zenith angle between 15° and 89°, R _RSW and water type is found to predict R _RSW with an r.m.s. error of 9 per cent. Also addressed is the determination of the aquatic optical property, Bb/a, from the sub-surface remote sensing reflectance, R _RSW This capability along with the specific absorption and scattering coefficients of aquatic constituents can, through bio-optical models, be used to estimate the concentrations of these aquatic constituents in non-Case I waters. The empirical relationship obtained to estimate Bb/a (with a r.m.s. error of 9·3 per cent) from the nadir value of the sub-surface remote sensing reflectance is Bb/a = 0·0027 + 987R _RSW ? 34·5( R _RSW)² + 1534( R _RSW)³.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

Effect of ‘Pt’ loading in ZnO-CuO hetero-junction material sensing carbon monoxide at room temperature

Sensing of carbon monoxide (CO) was carried out with ZnO-CuO and Pt/ZnO-CuO. Synthesized materials were characterized by temperature-programmed reduction (TPR), high-resolution transmission electron microscopy (HR-TEM) and X-ray diffraction (XRD). The composite materials of ZnO-CuO were found to have an active sensing center, The sensor response obtained for an optimized ratio of ZnO and CuO (1:1 weight ratio) yielded the highest response of 1.28 (R_co/R_air) for 1000 ppm of CO at room temperature; the response and recovery times were found to be 41 and 86 s, respectively. However, loading 1:1 ZnO-CuO with 0.4% Pt boosted the sensor response to 2.64 (R_co/R_air) for the same CO concentration. The sensor (0.4% Pt/ZnO-CuO) response was linear towards CO concentrations between 100 and 1000 ppm. In the Pt loaded case both the response and recovery times were found to be 81 s. A mechanism for CO sensor response was put forward with reference to CO adsorption and desorption.     

Detection of mosaic virus disease in cassava plants by sunlight-induced fluorescence imaging: a pilot study for proximal sensing

Cassava mosaic disease (CMD) is a prominent virus infection that causes considerable crop damage and yield reduction. Early detection of crop damage by remote sensing could be a useful tool for initiating remedial measures to reduce further crop damage. This article presents a non-destructive method for detection and classification of CMD infection, based on the red:far-red chlorophyll (chl) fluorescence image ratio. This pilot study was carried out in 14 varieties of potted cassava plants (Manihot esculenta Crantz) with a multispectral imaging system (MSIS) consisting of an electron multiplying charge coupled device (EMCCD) camera. Sunlight-induced chl fluorescence (SICF) images of plant leaves were recorded using the MSIS at the Fraunhofer lines of O₂-B at 687 nm and O₂-A at 759.5 nm and their off-lines at 684 and 757.5 nm. The recorded images were analysed using the Fraunhofer line discrimination (FLD) technique to extract the SICF from the solar reflectance in the recorded images. The chl fluorescence image ratio (red:far-red, F₆₈₇:F₇₆₀) was computed and correlated with the laser-induced chl fluorescence (LICF) ratio (F₆₈₅:F₇₃₅) determined by point monitoring, chl content variation, and the net photosynthetic rate (P_n). The scatter plot of the F₆₈₇:F₇₆₀ image ratio showed good discrimination between different levels of CMD infection as evidenced by the high sensitivity and specificity values. It is observed that the fluorescence image ratio (F₆₈₇:F₇₆₀) has a good correlation with P_n (coefficient of determination (R²) = 0.85), chl content (R² = 0.82), and the LICF ratio (F₆₈₅:F₇₃₅) (R² = 0.80), thereby highlighting the potential of the SICF image ratio in the discrimination of CMD infection. The results clearly indicate that changes in the red:far-red fluorescence image ratio due to CMD stress can easily be detected at an early stage and the technique has great potential for monitoring the health of crops and vegetation from proximal sensing platforms.     

Single-wavelength algorithms for in situ or remote sensing estimation of mean pigment concentration

The bio-optical relationships between inherent and apparent optical properties, and between optical properties and phytoplankton pigment concentration (C) averaged in a layer (ΔZ), were derived from analysis of data collected during the period 1996–1998 in the Gulf of Aqaba (Eilat). Parametrization of these relationships was based on radiative transfer theory, Gershun's equation, minimization of model errors by least-square fitting, and on known optical models relating underwater remote sensed reflectance (R _rsw) with the ratio of backscattering (b _b) to vertical attenuation coefficient (K _d) [or to absorption coefficient (a)]. These relationships explain a frequently used form of remote sensing algorithms for C estimation using ratio of water-leaving radiances measured at two or more wavelengths (λ). In this study, the possibility of using for this purpose a single wavelength in the blue range (λ=443?nm) within the framework of in situ and remote sensing algorithms for Case 1 waters was assessed.