Assessment of water quality in Lake Garda (Italy) using Hyperion
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Remote Sensing of Environment 109 (2007) 183 – 195 www.elsevier.com/locate/rse Assessment of water quality in Lake Garda (Italy) using Hyperion Claudia Giardino a,⁎, Vittorio E. Brando b , Arnold G. Dekker b , Niklas Strömbeck c , Gabriele Candiani a a Optical Remote Sensing Group, CNR–IREA, Milano, Italy b Environmental Remote Sensing Group, CSIRO-Land and Water, Canberra, Australia c Department of Limnology, EBC, Uppsala University, Uppsala, Sweden Received 22 August 2006; received in revised form 20 December 2006; accepted 23 December 2006 Abstract For testing the integration of the remote sensing related technologies into the water quality monitoring programs of Lake Garda (the largest Italian lake), the spatial and spectral resolutions of Hyperion and the capability of physics-based approaches were considered highly suitable. Hyperion data were acquired on 22nd July 2003 and water quality was assessed (i) defining a bio-optical model, (ii) converting the Hyperion at- sensor radiances into subsurface irradiance reflectances, and (iii) adopting a bio-optical model inversion technique. The bio-optical model was parameterised using specific inherent optical properties of the lake and light field variables derived from a radiative transfer numerical model. A MODTRAN-based atmospheric correction code, complemented with an air/water interface correction was used to convert Hyperion at-sensor radiances into subsurface irradiance reflectance values. These reflectance values were comparable to in situ reflectance spectra measured during the Hyperion overpass, except at longer wavelengths (beyond 700 nm), where reflectance values were contaminated by severe atmospheric adjacency effects. Chlorophyll-a and tripton concentrations were retrieved by inverting two Hyperion bands selected using a sensitivity analysis applied to the bio-optical model. The sensitivity analysis indicated that the assessment of coloured dissolved organic matter was not achievable in this study due to the limited coloured dissolved organic matter concentration range of the lake, resulting in reflectance differences below the environmental measurement noise of Hyperion. The chlorophyll-a and tripton image-products were compared to in situ data collected during the Hyperion overpass, both by traditional sampling techniques (8 points) and by continuous flow-through systems (32 km). For chlorophyll-a the correlation coefficient between in situ point stations and Hyperion-inferred concentrations was 0.77 (data range from 1.30 to 2.16 mg m− 3). The Hyperion-derived chlorophyll-a concentrations also match most of the flow-through transect data. For tripton, the validation was constrained by variable re-suspension phenomena. The correlation coefficient between in situ point stations and Hyperion-derived concentrations increased from 0.48 to 0.75 (data range from 0.95 to 2.13 g m− 3) if the sampling data from the re-suspension zone was avoided. The comparison of Hyperion- derived tripton concentrations and flow-through transect data exhibited a similar mismatch. The results of this research suggest further studies to address compatibilities of validation methods for water body features with a high rate of change, and to reduce the contamination by atmospheric adjacency effects on Hyperion data at longer wavelengths in Alpine environment. The transferability of the presented method to other sensors and the ability to assess water quality independent from in situ water quality data, suggest that management relevant applications for Lake Garda (and other subalpine lakes) could be supported by remote sensing. © 2007 Elsevier Inc. All rights reserved. Keywords: Hyperspectral satellite data; Lake waters; Bio-optical modelling; In situ data 1. Introduction supply, irrigation, transportation), industrial (processing and cooling, energy production, fishery) and recreational purposes. Lake water is an essential renewable resource for mankind Sustainable use of water resources requires the coupling of and the environment and it is important for civil (drinking water surface waters assessment monitoring programs and decision making and management tools. The Water Framework Di- rective (WFD) of the European Commission (Directive 2000/ ⁎ Corresponding author. Tel.:+39 0223699298; fax: +39 0223699300. 60/EC, 2000) is the major reference in Europe to guide efforts E-mail address: email@example.com (C. Giardino). for attaining a sustainable aquatic environment in the years to 0034-4257/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2006.12.017
184 C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 come. The WFD includes guidelines which define the cat- This study is part of ongoing research efforts aimed at de- egories of quality and the required components and parameters. veloping RS strategies towards the implementation of the WFD, As some of these parameters can be determined by Remote ensuring systematic monitoring of water quality in Lake Garda, Sensing (RS) with a reasonable accuracy, RS-related technol- the largest Italian lake. Empirical and semi-empirical ap- ogies may be integrated in the monitoring programs defined by proaches were previously investigated to retrieve chlorophyll the WFD, provided they can be demonstrated to independently concentrations in the lake (Brivio et al., 2001; Candiani et al., assess Water Quality Parameters (WQPs). 2003; Giardino et al., 2005) but their results were scene Since the 1980s satellite RS represents an opportunity for dependent. The aim of this study is to provide a RS-based synoptic and multitemporal viewing of water quality. To estimate measurement tool, transferable to different RS-instruments. It WQPs from satellite data three different approaches can be used would be useful for water management authorities of Lake (Cracknell et al., 2001; Dekker et al., 1995). The (1) empirical Garda for coarse scale regular monitoring (with high revisiting approach is based on the development of bi-variate or multivariate time spaceborne sensors), for intermediate/fine scale studies regressions between RS data and measured WQPs. Digital (with high spatial resolution satellite and airborne sensors) and numbers or radiance values at the sensor, as well as their band for retrospective analysis with time-series imagery (as in combinations, are correlated with in situ measurements of WQPs, Dekker et al., 2005). As a test, hyperspectral Hyperion data usually collected in coincidence of the sensor overpass. A (with a 30 m pixel size and a choice of more than 200 spectral summary of empirical approaches for lakes can be found in channels), analytical modelling, and in situ measurements Lindell et al. (1999). The (2) semi-empirical approach may be coincident with the satellite overpass for a validation or com- used when spectral characteristics of the parameters of interest parison of the image-derived products, were considered ap- are known. This knowledge is included in the statistical analysis propriate. The approach used in this study builds on the method by focusing on well-chosen spectral areas and appropriate developed for Hyperion imagery of a sub-basin of a subtropical wavebands used as correlates. An example of a semi-empirical bay in Australia (Brando & Dekker, 2003). Based on a bio- approach with different sensors is reported by Härmä et al. (2001) optical model sensitivity analysis, Hyperion bands were over Finnish lakes. In the (3) analytical approach, WQPs are selected and concentrations of chlorophyll-a and tripton (the related to the bulk Inherent Optical Properties (IOPs) via the non-algal particles of the suspended particulate matter) were Specific Inherent Optical Properties (SIOPs). The IOPs of the retrieved. Point in situ data for an initial validation of the water column are then related to the Apparent Optical Properties products, followed by a comparison of concentrations retrieved (AOPs) and hence to the Top of Atmosphere (TOA) radiance, from Hyperion using high spatial resolution flow-through such as described by the radiative transfer theory (Mobley, 1994; estimates of chlorophyll-a and tripton were used. Vermote et al., 1997). The analytical method involves inverting all above relations (WQPs → IOPs → AOPs → TOA radiances) to 2. Materials and methods determine the WQPs from RS data. An example of such approach, using Landsat over lakes, can be found in Dekker et al. (2001) for 2.1. Study area and fieldwork activities the total suspended matter retrieval. Quantitatively, the relationships developed to assess water Approximately 500,000 lakes over 1 ha surface area exist in quality in lakes within empirical and semi-empirical approaches Europe. Most of the largest European lakes are located in the are often scene dependent and only apply to the data from which Nordic countries and in the Alpine regions (EEA, 1999). The they are derived. Well-calibrated and validated physics-based most important Italian lake district is located in the subalpine approaches are instead applicable to every scene acquired over region and represents more than 80% of the total Italian the selected lake (presuming constant SIOPs), giving the lacustrine volume (Premazzi et al., 2003). Lake Garda, located opportunity to assess water quality independently from ground 65 m a.s.l. around 45°40′ N and 10°41′ E at the eastern border measurements of WQPs. The monitoring of spatially heteroge- of the subalpine region, is the largest Italian lake. It has a surface neous parameters, as re-suspension phenomena due to local area of 368 km2, a volume of 49 million m3 and a maximum and variability in wind and circulation, or algal blooms at the surface a mean depth of 350 m and 133 m, respectively. The average also necessitates these (in situ independent) methods. Dekker value of the Secchi disk depth is 4.5 m in summer and 16 m in et al. (2002) investigated the capabilities of Landsat-TM and winter. Chlorophyll-a (CHL-a) and suspended particulate matter SPOT data for retrospective analysis in Dutch lakes. Both (SPM) concentrations range from 0.5 to 12 mg m− 3 and from sensors were capable of describing larger concentration 0.1 to 5.5 g m− 3, respectively. The coloured dissolved organic gradients characterised by temporal changes that were not matter concentration (absorption coefficient at 440 nm, aCDOM represented by point in situ data. Kutser (2004) used Hyperion (440)) ranges from 0.017 to 0.36 m− 1. Average concentrations data to map accumulation of aggregations of cyanobacteria in of CHL-a, SPM and aCDOM(440) are around 2.7 mg m− 3, 2.5 g the Gulf of Finland, an assessment unachievable by traditional m− 3 and 0.09 m− 1, respectively (Premazzi et al., 2003; Zilioli, in situ sampling due to spatial and temporal issues. Kutser 2002). According to the OECD guidelines (Vollenweider & (2004) also showed that flow-through systems were only Kerekes, 1982), Lake Garda can be classified as an oligo- suitable to map chlorophyll from a fixed depth, and therefore mesotrophic basin. inappropriate for assessing cyanobacteria blooms closer to or at In collaboration with the local agencies in charge of the water surface. limnological monitoring of Lake Garda, intensive fieldwork
C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 185 activities were run in the whole basin, within several national particulate matter can be divided into phytoplankton and into and international projects (Lindell et al., 1999; Zilioli, 2002, the non-algal component (i.e., tripton), TR was indirectly 2004). More than 30 days over 5 years of in situ measurements estimated from SPM concentrations. They were measured were performed to achieve a comprehensive dataset of on pre-combusted and pre-weighted Watman GF/F filters, concentrations of WQPs, of IOPs and AOPs, leading to the dried at 95 °C overnight. The biomass of phytoplankton parameterisation of a three-component bio-optical model was considered correlated to CHL-a and the formula according to Strömbeck et al. (2003). In this study in situ data TR = SPM − 0.07CHL-a (with TR and SPM in g m− 3 and collected on 22nd July 2003 to validate the atmospheric CHL-a in mg m− 3) was used to separate TR from SPM. Gons correction of Hyperion data, to validate the image-derived et al. (1992) observed that for fresh water algae the part of WQPs products, as well as to improve the bio-optical SPM determined by the biomass of phytoplankton can vary parameterisation are presented. between 0.02 and 0.1. The average value of 0.07 had been successfully adopted in Dutch lakes (Hoogenboom et al., • Water samples of the first integrated meter of water column in 1998), Finnish lakes (Kutser et al., 2001) as well as in coastal 9 point stations (Fig. 1) were analysed for CHL-a and tripton waters (Brando & Dekker, 2003). Thus, it was supposed to be (TR) concentrations. The in situ samples were collected valid for Lake Garda too. within a 3-hour interval around the image acquisition. Water • Five water samples collected during the day were used to was filtered through Watman GF/F glass fiber filters and the measure the absorption spectra of phytoplankton aph(λ) material retained was analysed for CHL-a concentrations and tripton aTR(λ), according to the method showed in according to the analytical method ISO 10260-E (1992). Strömbeck and Pierson (2001). The absorption spectra of Phytoplankton was composed by 70% of Chlorophyta particles ap(λ) retained onto the GF/F filters, were measured species and by 30% of almost equal parts of Cryptophyceae, using a laboratory spectrophotometer and the filter-pad Diatomeae and Cyanophyta species. Because the suspended technique (Tassan & Ferrari, 1995). The filters were then treated with cold Methanol to extract pigments and the absorption spectra of tripton aTR(λ) of these bleached filters were measured. The absorption spectrum of phytoplankton aph(λ) was derived by subtracting aTR(λ) from ap(λ) spectra. • A 32-km-long transect (Fig. 1) of fluorescence and turbidity data were collected using a flow-through system. The system is composed of a hydraulic device, (essentially an intake pipe) continuously pumping water from 0.5 m depth into a Turner Design Scufa-II fluorometer/turbidimeter, and a GPS, both logged by a Campbell data-logger. Logged values of fluorescence (in mV) and turbidity, in Nephelometric Turbidity Units (NTU), were corrected for delays caused by the flow-through system. Flow-through data were con- verted into chlorophyll-a and tripton using the concentrations derived from laboratory analysis on water samples. Eight laboratory-concentrations were regressed against the average of logged values over the nearest 100 m to the GPS location where the water samples were collected. By means of linear regression analysis, the measured in vivo fluorescence was transformed into chlorophyll-a concentrations (R2 = 0.55), and turbidity into tripton concentrations (R2 = 0.68) (Fig. 2). It was hence assumed that flow-through data were able to describe both CHL-a and TR concentrations along the 32- km-long transect although turbidity, because it includes phytoplankton scattering, is more closely a measure of SPM. • Spectroradiometric measurements of water radiance using the PR-650 spectroradiometer were performed to calculate the subsurface irradiance reflectance R(0−, λ) in three pelagic stations (4, 6 and 7 in Fig. 1). R(0−, λ) values were computed from remote sensing reflectances Rrs(0+, λ), Fig. 1. Study area and location of fieldwork activities performed within a 3-hour measured above-water according to the SeaWifs protocol interval around the image acquisition. 1 to 9 are the stations where water was (Fargion & Mueller, 2000). Effects of the lake surface sampled for laboratory analysis for chlorophyll-a and tripton concentrations (stations 4, 6 and 7 have also PR-650 radiometric measurements). The flow- roughness on above-water Rrs(0+, λ) determinations were through system was cruised throughout all the stations, for a length of about corrected by a sky-radiance reflectance factor and by an 32 km. The 7.5-km-wide portion of lake imaged by Hyperion is outlined. offset term, that does not impose a constrained normalisation
186 C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 Fig. 2. Scatter plots of fluorescence vs. chlorophyll-a (using stations 1 to 8) and of turbidity vs. tripton (using stations 2 to 9) with the calibration lines (turbidity in Station 1 and fluorescence in Station 9 were no available). at 750 nm (Toole et al., 2000). Literature data (Toole et al., tion spectra of coloured dissolved organic matter. In this study 2000) for clear sky and high wind speeds were used (on 22nd SCDOM and STR were equal to 0.021 and 0.012, respectively. July, 2003 the average wind speed on the lake was 6 m s− 1). The spectral total backscattering coefficient bb(λ) was Assuming an air/water interface parameter of 0.533 (Lee computed as: et al., 1994) and a Q-factor of 4.2 sr− 1 the roughness-cor- ⁎ rected Rrs(0+, λ) were then transformed into R(0−, λ) bb ðkÞ ¼ bbw ðkÞ þ ½CHL abbph ðkÞ values. As reported in Strömbeck et al. (2003) the Q-factor ⁎ k −ni þ ½TRbbTR ð550Þ ð3Þ was the average, between 400 and 750 nm, of a spectral Q- 550 factor computed using the HYDROLIGHT 4.2 model (Mobley, 1994; Mobley & Sundman, 2001). where, bbw(λ) is the backscattering coefficient of pure water (Morel, 1974; Dall'Olmo & Gitelson, 2006), b⁎bph (λ) is the specific backscattering caused by phytoplankton, b⁎bTR(λ) is the 2.2. The bio-optical model specific backscattering coefficient at 550 nm for 1 g m− 3 of tripton, ni is an exponent describing the spectral dependency of The bio-optical model used in this study was similar to tripton backscattering (mainly due to its inorganic components). previously published three-components (i.e., chlorophyll-a, The specific backscattering by phytoplankton was computed tripton and coloured dissolved organic matter) Case-2 or lake using an expression based on Gordon et al. (1988), Morel water models, e.g., Pierson and Strömbeck (2001). The sub- (1988), Ammenberg et al. (2002), and Roesler and Boss (2003): surface irradiance reflectance R(0−, λ) was calculated as a function of absorption and backscattering coefficients according ⁎ k −nph bbph to Walker (1994): b⁎bph ðkÞ ¼ ⁎ bphð555Þ þ aphð555Þ ⁎ −kaphðkÞ 555 bph 1 bb ð kÞ ð4Þ Rð0−;kÞ ¼ d ð1Þ l d ðkÞ að kÞ þ bb ð kÞ P 1þ l u ðkÞ P where, b⁎ph (555) is the chlorophyll-a specific scattering, nph is where, a(λ) is the spectral total absorption coefficient, bb(λ) is an exponent describing the spectral dependency of the phy- the spectral total backscattering coefficient, and μ̄d(λ)/μ̄ u(λ) is toplankton beam attenuation, k is an empirical coefficient reg- the ratio of the average cosine of the downwelling light to that of ulating the effect by phytoplankton absorption and bbph / bph is the upwelling light (Mobley, 1994). the average spectral backscattering efficiency of phytoplankton. The spectral total absorption coefficient a(λ) was computed The parameterisation of the bio-optical model used in this as: study is largely based on the data presented in Strömbeck et al. aðkÞ ¼ aw ðkÞ þ ½CHL aa⁎ph ðkÞ þ aCDOM ð440Þe−SCDOM ðk−440Þ (2003), which have been acquired in Lake Garda on 10th and 11th October 2002. The dataset contains discrete measurements þ ½TRa⁎ ð440Þe−STR ðk−440Þ TR of WQPs, total absorption a and total scattering b coefficients ð2Þ at 9 wavelengths obtained with a WET Labs ac-9, and total backscattering bb coefficients at 6 wavelengths obtained by a where, aw(λ) is the pure water absorption (Pope & Fry, 1997; HOBILabs HydroScat-6. In particular, scattering b and back- Smith & Baker, 1981), a⁎ph(λ) is the chlorophyll-specific scattering bb data of the lake, were used to parameterise Eq. (4) phytoplankton absorption, SCDOM is the slope factor of the originally adopted for oceanic phytoplankton. Because the absorption spectra of coloured dissolved organic matter, average specific absorption coefficients of phytoplankton, a⁎TR(440) is the absorption coefficient at 440 nm specific ⁎ (λ) derived from data collected on 22nd ⁎ (λ) and tripton, aTR aph for 1 g m− 3 of tripton, STR is the slope factor of the absorp- July 2003 were comparable to data collected on 10th and 11th
C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 187 Table 1 where, N is the number of bands, and X̄ i and X̂i are the List of the bio-optical parameters and in situ water quality data presented in this subsurface reflectance values from in situ data and forward study with information about the day of acquisition and data source (APPA is the Environmental Protection Agency of Trento, ARPAV is the Environmental modelling, respectively. The number of bands N was 28; 22 of Protection Agency of Veneto) these in the visible (VIS) range, from 480 to 690 nm, and the Parameter Day of acquisition Source remaining in the near-infrared (NIR) range, from 700 to 750 nm. The optical closure between in situ determinations of R(0−, aw(λ) – Smith and Baker (1981), λ) and the simulated values from forward modelling was Pope and Fry (1997) bbw(λ) – Morel (1974), Dall'Olmo considered satisfactory in all stations (Fig. 4, Table 2). In and Gitelson (2006) particular, the convergence was good in the VIS range (average a⁎ph (λ) 10th–11th October 2002 HelsinkiUniversity and RMSE of the three stations 0.006, relative RMSE 12%) while Luode Consulting Oy beyond 700 nm a larger divergence was observed (average 22nd July 2003 APPA RMSE 0.012, relative RMSE 55%). b⁎bph (λ) 10th–11th October 2002 HelsinkiUniversity and Luode Consulting Oy a⁎TR(440), STR 10th–11th October 2002 HelsinkiUniversity and 2.3. Hyperion data and pre-processing analyses Luode Consulting Oy 22nd July 2003 APPA On 22nd June 2003, image data from an area of 7.5 by 42 km b⁎bTR (λ) 10th–11th October 2002 HelsinkiUniversity and was acquired by Hyperion with a near-nadir viewing. At the Luode Consulting Oy SCDOM 10th–11th October 2002 APPA time of the overpass Sun zenith and azimuth angles were 32° μ̄u(λ), μ̄d(λ) – HYDROLIGHT 4.2 and 136°, respectively. For this study 28 Hyperion spectral CHL-a, TR 22nd July 2003 ARPAV bands ranging from 480 nm to 750 nm were selected to be Fluorescence, turbidity 22nd July 2003 CNR–IREA relevant for WQPs estimation and reliable for the sensor calibration (Green et al., 2003). Following the approach by October 2002, they were averaged and integrated in the existing Brando and Dekker (2003), the image was convolved using a dataset. Apparently, the natural modifications of algal and tripton compositions occurred between the two periods had a negligible effect on their absorption spectral properties. Together with the μ̄ d(λ)/μ̄ u(λ) ratio (average value from 450 nm to 750 nm equal to 0.327), that was derived running HYDROLIGHT 4.2 with inputs typical of Lake Garda (e.g., IOPs, averages values of wind speed and visibility ranges, summertime Sun zenith angles at 11 ± 1 h UTC) (Strömbeck et al., 2003), these SIOPs uniquely characterise the parameter- isation of Eq. (1) for Lake Garda waters. Table 1 summarises the bio-optical model parameters with the day of acquisition and the data provider, Fig. 3 shows the SIOPs used in this study. The performance of the above parameterisation was evaluated using the 22nd July 2003 dataset. It consisted of the PR-650-derived measurements of R(0−, λ), collected in the stations 4, 6 and 7 (Fig. 1), in which concentrations of CHL-a and TR were also known. These concentrations, together with the long-term (i.e., 0.09 m− 1) average value of aCDOM(440) of Lake Garda (as aCDOM(440) concentrations were not measured in this campaign), were given as input to the bio-optical model to simulate R(0−, λ) spectra, assuming that SIOPs were the same of October 2002. The optical closure between in situ determinations of R(0−, λ) and the simulated values from forward modelling was quantified with the Root Mean Square Error (RMSE) and the relative RMSE (in %): vﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ uN 2 uP P u X − X̂ ti¼1 i i Fig. 3. SIOPs of Lake Garda: spectra of absorption (upper graph) and RMSE ¼ ð5Þ backscattering (lower graph) coefficients used in the bio-optical model. aw is the N −1 absorption coefficient of pure water, a⁎ph is the chlorophyll-specific absorption coefficient of phytoplankton, aTR⁎ is the specific absorption coefficient of tripton, RMSE ⁎ and aCDOM is the specific absorption coefficient of coloured dissolved organic Relative RMSE ¼ d 100 ð6Þ matter. bbw is the backscattering coefficient of pure water, bb⁎ph is the P N P 1 N Xi chlorophyll-specific backscattering coefficient of phytoplankton, and bb⁎TR is i¼1 the specific backscattering coefficient of tripton.
188 C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 of NEΔR(0−, λ)E is about 0.001 for Hyperion data acquired over water. The filtered image was atmospherically corrected to R(0−, λ) using the MODTRAN-based c-WOMBAT-c procedure (Brando & Dekker, 2003). The procedure consists of: (i) a three step atmospheric inversion from at-sensor-radiance to apparent reflectance and (ii) a two step inversion of the air–water interface from apparent reflectance to subsurface irradiance reflectance. c-WOMBAT-c was run with actual measurements of visibility range (15 km) derived from sun-photometer ob- servations performed synchronously to the sensor overpass (Vermote et al., 1997). A Q-factor of 4.2 sr− 1, an air/water interface parameter of 0.533, a nadir-viewing geometry and a maritime extinction for aerosols were also given as inputs to the atmospheric correction code. The atmospherically corrected image was geo-located and image-derived R(0−, λ) values were compared to in situ data measured during the Hyperion overpass in the pelagic stations 4, 6 and 7 (Fig. 1). The optical closure (Fig. 4, Table 2) between in situ and Hyperion spectra was on average good in the VIS range (from 480 to 690 nm, the average reflectance RMSE of the three stations was 0.007 and the relative RMSE 14%) and inferior in the NIR bands (beyond 700 nm, the average reflectance RMSE was 0.011 and the relative RMSE 77%). More in detail, in Station 7 the near-infrared wavelength re- flectance values from image data were over-estimated compared to in situ values (beyond 700 nm the RMSE was 0.015 and the relative RMSE 159%). The most likely cause of such over- estimation in the atmospherically corrected image data was the contamination of Hyperion radiances by adjacency effects, due to multiple reflections of radiation coming from the neighbour- ing environment, specifically in the northern part of Lake Fig. 4. In situ, forward-modelled and Hyperion-derived subsurface irradiance Garda. Here adjacency effects were probably caused by the reflectance R(0−, λ) spectra in stations 4, 6 and 7 (Fig. 1): “In situ” spectra are vegetation growing over the steep sides, laterally delimiting the derived from above-water measurements of Rrs(0+, λ); “Model” spectra are northern narrow part of the lake, where Station 7 is located calculated from forward bio-optical modelling using CHL-a (in mg m− 3) and TR (in g m− 3) concentrations measured in situ (shown in each graph) and assuming (Fig. 1). The contribution of reflections from the vegetated aCDOM(440) = 0.09 m− 1 in all stations; “Hyp” spectra are computed from environment on the water surface increases the signal measured Hyperion imagery using the c-WOMBAT-c atmospheric correction code. All the by the sensor at longer wavelengths (Floricioiu & Rott, 2005) data were collected on 22nd July 2003. Average reflectance RMSEs (see also and c-WOMBAT-c can fail in removing these quantities. Table 2) are 0.006 (relative RMSE 13%) between 480 and 690 nm, and 0.011 (relative RMSE 66%) beyond 700 nm. 2.4. Band selection and model inversion 5 × 5 low pass filter to reduce the environmental noise- A direct inversion of the bio-optical model was applied to equivalent reflectance differences NEΔR(0−, λ)E. According the Hyperion image using a linear Matrix Inversion Method to Brando and Dekker (2003) and Wettle et al. (2004), the value (MIM), as in Brando and Dekker (2003). They ran MIM on a Table 2 List of RMSE and relative RMSE (in %) measuring the optical closure of in situ determinations of R(0−, λ) vs. forward-modelled and Hyperion-derived R(0−, λ) values In situ vs. forward-modelled In situ vs. Hyperion-derived St. 4 St. 6 St. 7 AvMod St. 4 St. 6 St. 7 AvHyp AvAll VIS 0.006 0.007 0.005 0.006 0.005 0.006 0.010 0.007 0.006 12% 13% 10% 12% 10% 10% 21% 14% 13% NIR 0.015 0.020 0.002 0.012 0.009 0.009 0.015 0.011 0.011 72% 73% 22% 55% 42% 31% 159% 77% 66% RMSE and relative RMSE for each station are reported together with their average value (in bold); AvMod is the average for stations 4, 6 and 7 of RMSEs computed from in situ and forward-modelled R(0−, λ) values, AvHyp is the average for stations 4, 6 and 7 of RMSEs computed from in situ and Hyperion-derived R(0−, λ) values. AvAll is the average of AvMod and AvHyp RMSEs and measures how R(0−, λ) values from Hyperion, forward bio-optical modelling and in situ optical data converge. RMSEs and relative RMSEs are separately computed for VIS (22 bands from 480 to 690 nm) and NIR (6 bands from 700 to 750 nm) ranges.
C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 189 Hyperion image of a coastal site to retrieve chlorophyll, tripton model changing aCDOM(440) from 0.03 to 0.30 m− 1 and fixing and aCDOM(440) by inverting three bands (490 nm, 670 nm and CHL-a and TR to 1 mg m− 3 and to 1 g m− 3, respectively. The 700–740 nm). The bands were chosen through some iterations first derivative was rescaled by a 0.01 factor to appreciate the starting from wavelengths closest to spectral feature typical for sensitivity of the model in discriminating aCDOM(440) at con- each of the WQPs and avoiding the shortest wavelengths (blue) centration ranges of the lake of around 0.09 m− 1. The first where Hyperion was noisy. In this study, the large number derivative spectra relative to variations of CHL-a were computed of Hyperion bands was exploited by performing a sensitivity by 10 forward runs of the bio-optical model incrementing CHL-a analysis of the bio-optical model on aCDOM(440), CHL-a by 1 mg m− 3 within a range between 1 and 1 mg m− 3, and fixing and TR independently, based on the first derivative approach TR to 1 g m− 3 and aCDOM(440) to 0.09 m− 1. Similarly, the first (Hoogenboom et al., 1998). derivative spectra relative to variations of TR were computed by The first derivative spectra relative to variations of aCDOM 10 forward runs of the bio-optical model incrementing TR by 1 g (440) were obtained from 10 forward runs of the bio-optical m− 3 within a range between 1 and 10 g m− 3, and fixing CHL-a to 1 mg m− 3 and aCDOM(440) to 0.09 m− 1. Fig. 5 presents the first derivative spectra of R(0−, λ) vs. each of the WQPs. The maximum variation of the first derivative of R(0−, λ) for aCDOM(440) occurred at shortest wavelengths (Fig. 5a), in a region where Hyperion data are too noisy and ill calibrated (Green et al., 2003). Moving towards the region where Hyperion provides calibrated data (i.e., N 480 nm), the variation of the first derivative dR(0−, λ) / daCDOM(440) falls within the Hyperion NEΔR(0−, λ)E of 0.001. This implies that estimates of aCDOM(440) at the concentration range of the lake was not achievable and therefore fixed to the long-term average value for Lake Garda (i.e., 0.09 m− 1). This, a priori determination of an unmeasurable signal due to small aCDOM (440) variations illustrates the usefulness of the first derivative approach. Based on sensitivity analysis for CHL-a and TR, the Hyperion bands at 490 nm and 550 nm were selected for the inversion. The 490 nm band was chosen because of the location of the maximum variation of the first derivative of R(0−, λ) for CHL-a concentration (Fig. 5b). The 550 nm band was chosen because it is the hinge point of the first derivative of R(0−, λ) for CHL-a concentration (Fig. 5b), as well as being in the region where the maximum variation of the first derivative of R(0−, λ) for TR concentration occurred (Fig. 5c). However, since Hy- perion spectra show band to band spikes or dips (Cairns et al., 2003), a selection based on single bands could match some spikes (Fig. 4). The binning of bands 480–500 nm and of bands 550–560 nm were thus used instead of the two single channels, centred at 490 nm and 550 nm, respectively. To implement the MIM algorithm to retrieve CHL-a and TR concentrations, Eq. (1) (in which Eqs. (2) and (3) were substituted) was rewritten to a set of 2 equations (for 2 wave- lengths), where each equation has the form: ⁎ ⁎ 1þP l d ðki Þ=P l u ð ki Þ ½CHL a aph ðki Þ þ bbph ðki Þ 1− Rð0−; ki Þ Fig. 5. Sensitivity analysis for the band selection of the MIM image inversion. (a): first derivative spectra of R(0−, λ) vs. aCDOM(440), rescaled by a 0.01 factor (aCDOM(440) concentration was changed from 0.03 to 0.30 m− 1, the other two ⁎ ðk Þ þ b⁎ ðk Þ 1− 1 þ l d ðki Þ= l u ðki Þ P P WQPs were kept constant to CHL-a = 1 mg m− 3 and TR = 1 g m− 3); (b): first þ ½TR aTR i bTR i Rð0−; ki Þ derivative spectra of R(0−, λ) vs. CHL-a (CHL-a concentration was changed from 1 to 10 mg m− 3, the other two WQPs were kept constant to aCDOM(440) = 0.09 m− 1 and TR = 1 g m− 3); (c): first derivate spectra of R(0−, λ) vs. TR (TR 1þP l d ðki Þ=P l u ð ki Þ concentration was changed from 1 to 10 g m− 3, the other two WQPs were kept ¼ aw ðki Þ−bbw ðki Þ 1− constant to aCDOM(440) = 0.09 m− 1 and CHL-a = 1 mg m− 3). The NEΔR(0−, Rð0−; ki Þ λ)E of Hyperion is overlaid on each of the graphs as dot lines. The box indicates the wavelength range of Hyperion bands that may be used. Note the different y axis ranges of each plot. −aCDOM ð440Þe−SCDOM ðki −440Þ ð7Þ
190 C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 where, R(0−, λi) is the Hyperion-derived subsurface irradiance scattering waters (i.e., light-blue colours in the pseudo true reflectance, i is the band used in the inversion and aCDOM(440) colour Hyperion image). The two MIM-retrieved Hyperion is fixed to 0.09 m− 1. WQP maps describe the chlorophyll-a and tripton concentra- tions. In these product maps, pixels where bottom depth was 3. Results and discussion less than 10 m, were masked because the bio-optical model we used (Eq. (1)) is applicable in optically deep waters only. Both Fig. 4 and Table 2 show how subsurface irradiance re- maps show ranges of CHL-a and TR between 0 and 5 mg m− 3 flectance values from atmospherically corrected image data, in and 0 and 5 g m− 3, respectively. The patterns in TR map seem situ optical data and bio-optical modelling converge. A correlated to the patterns in pseudo true colour Hyperion image: reasonable optical closure was achieved in the range from 480 TR concentrations are higher in the light-blue waters, whilst to 690 nm (average reflectance RMSE 0.006 and relative RMSE they are lower in the dark-blue (less-scattering) waters. The fact 13%, Table 2). Below 480 nm Hyperion data were not reliable that the CHL-a map is uncorrelated to the TR map indicates for calibration (Green et al., 2003) while beyond 700 nm successful decomposing of the R(0−, λ) signal and independent Hyperion reflectances did not seem very well corrected for assessment of CHL-a and TR concentrations following this adjacency effects and the optical closure was inferior (average method. reflectance RMSE 0.011 and relative RMSE 66%, Table 2). At Validation of CHL-a and TR Hyperion-derived maps was the two wavelengths (490 and 550 nm) where bands used for performed using in situ point stations. Fig. 7 shows the two MIM were located and a good convergence to the same scatter plots depicting the Hyperion-derived CHL-a and TR subsurface irradiance reflectance was obtained (the reflectance estimations vs. in situ concentrations measured in 8 pelagic differences were on average less than 0.005, or 7% as relative stations (all the stations in Fig. 1, except Station 1 which is value). located in shallow waters where Hyperion data were masked to Fig. 6 presents the pseudo true colour Hyperion image and avoid bathymetric effects). Hyperion data was averaged on a 3 the two WQP maps retrieved applying the MIM to the image. by 3 pixel region of interest centred on the location of in situ The pseudo true colour Hyperion image qualitatively describes sampling stations. The Hyperion-derived CHL-a was in good the diversity of waters within the lake. The turquoise–cyan agreement with in situ point data, showing a correlation colours in the southern part of the lake are due to bottom effects coefficient (r) of 0.77, a determination coefficient (R2) of of bright substrates. Most of the deep waters are dark-blue but 0.59, a RMSE of 0.36 mg m− 3 (relative RMSE 20%), a bias of lighter blue waters come up about the middle of the scene, as 0.12 mg m− 3 (relative bias 6%), and being close to the 1:1 line well as in the south, on the western side of the peninsula. On (Fig. 7). The RMSE and the relative RMSE were computed with 22nd July 2003, re-suspension of sediments was caused by the Eqs. (5) and (6), where N is now equal to 8 (i.e., the number of strong wind action resulting in variable patterns of more highly stations), and X̄ i and X̂ i are the in situ observed and the Fig. 6. The pseudo true colour Hyperion image (with locations of point in situ stations) and the two MIM-retrieved products obtained from Hyperion data acquired on 22nd July 2003. In the product maps shallow waters are masked.
C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 191 Fig. 7. Scatter plots of Hyperion-derived products and in situ concentrations measured in point stations: on left for chlorophyll-a, on right for tripton. Dot lines indicate the 1:1 relation. Both graphs do not include data from Stations 1 because it is located in the shallow waters. The statistic in the tripton graph does not include the black symbol (i.e., Station 5). Hyperion-derived concentrations of WQPs, respectively. The (a). Overall, Hyperion-based estimations are in agreement with Hyperion-derived TR did not match in situ point data (r = 0.48, transect measurements. The first part of the transect (0–4 km of R2 = 0.23 and RMSE 1.1 g m− 3), due to divergence between the length) is not shown since it is not included in the image Hyperion and in situ point data observed in Station 5. Removing footprint. In Section I of the transect (4–6 km of the length), this measurement from the dataset the regression analysis only calibrated transect data are plotted because Hyperion data performed better (r = 0.75, R2 = 0.57, RMSE 0.55 g m− 3, were masked to avoid bathymetric effects. Section II (6–14 km relative RMSE 31%, bias 0.27 g m− 3, and relative bias 15%) of the length) shows a good agreement in range and spatial and data become closer to the 1:1 line (Fig. 7). Station 5 is behaviour between the Hyperion-derived CHL-a and the located in the region of the light-blue, more scattering waters. transect fluorescence-derived CHL-a data, even if the peak Due to the strong wind blowing on the day of the image occurrences in concentration are sometimes shifted in phase. In acquisition the light-blue water pattern was characterised by a Section III (14–20 km of the length), when the transect data high rate of change (both in time and space). The water at were acquired almost at the same time of Hyperion data, Station 5 was probably sampled when these more scattering imagery-derived CHL-a match flow-through-derived CHL-a waters (imaged by Hyperion at 9:50 UTC) had already moved data. Unfortunately this is the section where many flow-through elsewhere (assuming that the 6 m s− 1 north–south direction data were missed through filtering of wave-related anomalies. wind resulted in a 0.06 m s− 1 water current, within 10 min a one In Section IV of the transect (20–22 km of the length), Hyperion pixel displacement may occur). Hyperion-derived CHL-a values were lower than flow-through- The ability to monitor water quality in highly dynamic derived concentrations but presented a similar ascending systems could be hindered by the spatial or temporal density of gradient. Section V (22–32 km of the length), shows a rea- point sampling offered by traditional sampling techniques sonable agreement in range and spatial behaviour between rendering them inappropriate to validate RS-derived products. imagery-derived CHL-a and the flow-through-derived CHL-a Lindfors et al. (2005) suggested that validation of remotely transect data with a descending gradient towards the transect sensed data products and locations of point measurements end. The last kilometre depicts Hyperion data only because the needed for monitoring work should be based on continuously fluorescence measurements were not available. measured flow-through values. They discussed IOPs, salinity Fig. 8(b) describes the spatial variation for tripton. As for the and temperature in Lake Vänaren (Sweden) and in the Gulf of chlorophyll-a, the first 4 km of the transect were outside the Finland. In this study, to qualitatively evaluate the spatial image footprint. In Section I of the transect, only calibrated variation of the WQP retrievals, the flow-through calibrated transect data are plotted because Hyperion data were masked to transects of CHL-a and TR were resampled according to the avoid bathymetric effects. In Section II, estimations derived 30 m size of Hyperion pixels. Flow-through data were first from Hyperion and flow-through transect data are comparable cleaned of anomalies, e.g., spikes due to bubbles, saturation and in values, except for the 3 g m− 3 peak assessed by Hyperion at lack of data (caused by wave-related difficulties in pumping the 7th kilometre. This peak also exists in the transect flow- water from subsurface into the onboard instrumentation). Fig. 8 through data but it is located at the 5th kilometre, in Section I. illustrates the comparison between two indirect estimates of Hyperion-derived concentrations appeared shifted in phase with WQPs: the Hyperion-derived concentrations and the flow- respect to flow-through data. As for Fig. 8(a), Section III is the through-derived transect in situ data. In Fig. 8, the location of region where collections of flow-through data were closer to the transect in situ data with respect to the time of the Hyperion Hyperion overpass. Within this section, both Hyperion-derived overpass is indicated to show the temporal mismatch between TR concentrations and flow-through-derived data show a close the acquisition of transect in situ data and image data. agreement exhibiting a steep ascending gradient and compara- The spatial trend of CHL-a concentrations, derived from ble concentration ranges. Unfortunately between the 18th and Hyperion data and from flow-through data is plotted in Fig. 8 the 20th kilometres many flow-through data were missed due to
192 C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 Fig. 8. Comparison of Hyperion-derived products and in situ concentrations estimated from the flow-through data along the horizontal transect: (a) chlorophyll-a, (b) tripton. Hyperion and flow-through transect data are extracted from a 30 m per 30 m pixel grid (see text for labels I to IV). The approximate location of transect in situ data with respect to the time of Hyperion overpass is also indicated. wave-related anomalies. In Section IV of the transect, flow- first part of Section V, lower concentrations in the last part of through data exhibit a flat trending, contrary to the image data Section V. The flow-through system gave the TR concentrations that presents a peak with two times higher concentrations. This (actually geo-coded to Hyperion pixels) 1–1.5 h later with is the region where the transect track crosses the light-blue respect to Hyperion, when the front had probably moved waters. As observed before, when point in situ data at Station 5 elsewhere and the distribution of tripton was changed. In were compared to image-derived TR estimations (Fig. 7), it general, Fig. 8(b) shows that Hyperion-derived tripton con- seems that the light-blue water front, quickly changing its centrations were not comparable to flow-through-derived position in time and space, was not described by in situ values, expect for few kilometres in Section III (from 15th to observations. A re-suspension of tripton due to the strong wind 19th kilometre) where Hyperion and transect acquisitions match action, which was synoptically imaged at 9:50 UTC by in time. These results suggest that Hyperion-derived tripton Hyperion, had been easily missed by in situ observations concentrations, in occasion of events subjected to local vari- collected at about 0.3–0.5 h later. In Section V, both the flow- ability in wind, re-suspension and circulation, are difficult to through data and the Hyperion retrieved TR concentrations compare to in situ data due to the incompatibilities of methods present a descending gradient but with different concentration used for tripton assessments. Even fast monitoring methods like ranges and slopes. Moreover, the peak of 4 g m− 3 of TR flow-through measurements are time consuming (∼ 3 h for a 32- observed by Hyperion (at the 25th kilometre), did not occur in km-long transect) and they could become inappropriate to the transect in situ data. Hyperion synoptically assessed the describe natural events with a high rate of change as may occur tripton distribution at 9.50 UTC: higher concentrations in the in wind driven currents in lakes.
C. Giardino et al. / Remote Sensing of Environment 109 (2007) 183–195 193 4. Conclusions may produce large errors in obtaining R(0−, λ) and conse- quently in retrieving concentrations of water quality parameters. This work presents a procedure to map CHL-a and TR This study aimed to use Hyperion imagery as a bench-mark concentrations in Lake Garda from hyperspectral satellite data for moving towards operational use of RS-related technologies based on forward and inverse bio-optical modelling. The per- that, integrated with traditional survey programmes, could formance of the analytical inversion approach was measured by provide useful information to implement the European WFD. the optical closure between forward-modelled reflectance, in Within the WFD it is possible, for each water body, to monitor situ reflectance and atmospherically corrected Hyperion only the water quality elements most sensitive to a certain risk or reflectance. The closure was sufficient for the purposes of this pressure. For Lake Garda this could be the deviation from a study but further investigations on atmospheric adjacency ef- trophic level assessed with two causal elements (i.e., phospho- fects, focused on surface reflected vegetation spectra from steep rous and nitrogen) and with one response parameter, the slopes, are recommended to obtain a better closure at longer chlorophyll-a concentration. The Hyperion data processing wavelengths. The bio-optical model sensitivity analysis indi- presented in this study will be transferred to the assessment of cated the optimal bands to run the inversion as well as the lake water quality (mainly chlorophyll-a) using more operational inability to detect aCDOM(440) in this study. The matrix in- instruments (being a part of a technology validation/demonstra- version method was applied to run the inversion on the spatially tion mission, Hyperion cannot be considered suitable for a long- and spectrally convolved Hyperion image. The MIM algorithm term monitoring). Large swath MODIS and MERIS sensors provided ranges of CHL-a concentrations comparable to in situ (both having the spectral bands used by MIM) offer almost-daily data collected the day of the satellite overpass. Results for imagery of northern Italy and the method presented could be tripton were less satisfactory but an improvement was found if extended to the other large (relative to the spatial resolution of data from a re-suspension zone were avoided. A further eval- the remote scanner that is) lakes of the subalpine region, where uation of image-products was based on high spatial resolution visibilities’ ranges are provided by airports or Aeronet stations. transect in situ data: about 32 km (some transect in situ data An onerous activity needs however to be completed mainly to were missed because of the wave-related anomalies) of flow- asses the lakes SIOPs or to evaluate how they differ from the through-derived measurements of CHL-a and TR were Lake Garda ones. To start, Premazzi et al. (2003) discussed that qualitatively compared to concentrations retrieved from Hyper- the in the subalpine region composition of the phytoplankton ion. For chlorophyll-a the Hyperion-derived concentrations communities would register marked similarities from one lake to were on average comparable to transect in situ data. The com- another, as regards density, biomass and species. parison was more difficult for tripton since some incompatibil- ities of methods happened. On the day of the Hyperion overpass Acknowledgements a strong wind occurred over the lake resulting in re-suspension of sediment (tripton). Further investigations are therefore nec- This work is in memory of Eugenio Zilioli who passed away essary, mainly addressing the compatibilities of methods for in 2004 and who made a considerable effort in Europe to monitoring water body features with high rate of wind or wave establish remote sensing of lakes as a tool for environmental driven change. Matthews et al. (2001) already observed how monitoring. Hyperion data were acquired by the Helge Axson continuous fluorometers towed behind boats may offer an Johnsons Foundation, Sweden. IOP data were collected by A. increased capability to monitor chlorophyll-a with respect to Lindfors and K. Rasmus at the Dep. of Geophysics, Helsinki traditional sampling technique in highly dynamic coastal zone University and Luode Consulting Oy, Helsinki. This study was but, the linear track estimates, may be themselves inadequate to funded by the Italian Space Agency (Ninfa Project), and by describe the wide-scale heterogeneous phenomenon as synop- ESA and Regione Lombardia with financial support grants to N. tically retrieved by RS. The results also indicate that fast Strömbeck and to G. Candiani, respectively. The CNR/CSIRO processing of hyperspectral images is feasible: once the pre- Agreement (2004–06 Program) and the Scientific Office at the processing was done the Hyperion image processing took only Embassy of Italy in Canberra supported the collaboration 180 s on a standard desktop PC. Another advantage of the among our institutes. This work would not be possible without method is that each good set of in situ AOPs, IOPs and SIOPs the assistance and contributions provided during these years by measurements added to the spectral library of the lake will L. Alberotanza from CNR-ISMAR, G. Zibordi from JRC, and improve the algorithm performance (thus at a certain moment by C. Defrancesco and G. Franzini from the Environmental no further in situ measurements will be required as all source Protection Agencies of Trento and Veneto, respectively. We are materials, e.g., inflowing waters, re-suspended material and grateful to T. Kutser from Estonian Marine Institute for his algal populations are characterised properly). In such a context, valuable suggestions. Constructive comments from the anon- next studies could also benefit from more information about ymous reviewers, including ones on an earlier version of the variability in the SIOPs over time and space. 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