IOPs and AOPs commonly used in optical hydrology
In this chapter, we attempted to present an overview of the use of remote sensing to monitor water quality parameters, mainly chlorophyll-a (chl-a) and turbidity. We summarized the main concepts of bio-optical modeling and presented a case study of the application of the Hyperspectral Imager for the Coastal Ocean (HICO) for the monitoring of water quality in a tropical hypersaline aquatic environment. Using HICO, we evaluated a set of different semi-empirical bio-optical algorithms for chl-a and turbidity estimation developed for inland and oceanic waters in the Araruama Lagoon, RJ, Brazil, which is an extreme environment due to its high salinity values. We also developed an empirical algorithm for both water quality parameters and compared the performances. Results showed that for chl-a estimation all models have a low performance with a normalized root mean square error (NRMSE) varying from 24.13 to 30.46. For turbidity, the bio-optical algorithms showed a better performance with the NRMSE between 15.49 and 28.04. Overall, these results highlight the importance of including extreme environments, such as the Araruama Lagoon, on the validation of bio-optical algorithms as well as the need for new orbital hyperspectral sensors which will improve the development of the field.
- Water quality
- bio-optical modeling
Earth Observations from space began in August, 1972, with the launch by National Aeronautics and Space Administration (NASA) of the Earth Resources Technology Satellite (ERTS-1) . However, the use of remote sensing techniques to monitor inland water quality parameters such as chlorophyll-a (chl-
Water column optical properties are grouped into inherent optical properties (IOPs) and apparent optical properties (AOPs). IOPs are related to those properties that depend only upon the environment, thus, they are independent of the environment light field. The two most essential IOPs are the total absorption coefficient (
|Volume scattering function||m-1 sr-1||β|
|Scattering phase function||m-1||β∼|
|Beam attenuation coefficient||m-1||
|Irradiance reflectance (ratio)||-||
|Remote sensing reflectance||sr-1||
|Remote sensing reflectance (sub)||sr-1||
|of radiance L(z, θ, φ)||m-1||
|of downwelling irradiance Ed(z)||m-1||
|of upwelling irradiance Eu(z)||m-1||
|of Photosynthetic Active Radiation (PAR)||m-1||
Based on the interaction among AOPs and IOPs, absorption, scattering and attenuation properties of the water column are retrieved from proximal, aerial or orbital measurements of the solar spectrum mainly in the visible and near-infrared (NIR) spectral range. These optical properties allow the estimation of different water quality parameters such as: primary production, turbidity, eutrophication, particulate and dissolved carbon contents or the assessment of currents and algal blooms . The relation among all these optical properties as well as the equipment to measure them were developed by oceanographers based on the modeling of downwelling solar and sky radiation spectra with the air–water interface and the subsurface aquatic absorption and scattering centers. Studies such as [4–8], among numerous others, established the main theory of the field before or around the launch of ERTS-1.
The first application of the theories of hydrologic optics was described by , which used a Monte Carlo simulation of the radiative transfer equation to relate the AOPs to the IOPs in oceanic waters containing optically active constituents, molecular water and chl-
The development of bio-optical algorithms usually starts by collecting in situ limnological data as well as hyperspectral
To overpass the problem of the spatial resolution and to keep a good spectral resolution, hyperspectral airborne sensors have been used to monitor the quality of inland waters. One of the most common airborne hyperspectral sensor used to monitor water quality parameters is the airborne imaging spectrometer for application (AISA), which is a push-broom system that collect spectral-radiance data (upwelling radiance and downwelling solar irradiance) in the visible and NIR range of the electromagnetic spectrum (approximately from 392 to 982 nm with a bandwidth of 7–8 nm). From an altitude of 1,000 m, this sensor has a spatial resolution of 1 m, surpassing the problems caused by medium to low spatial resolutions found in orbital sensors. In , AISA imagery was used to estimate chl-
An orbital hyperspectral sensor could be the solution for the high costs of flying an airborne sensor, and this was accomplished by the launch of Hyperion, in 2000. However, this sensor was not used in a water quality research because of its signal-to-noise ratio which was very low , and also because of its unreliability caused by problems such as radiometric instability. An alternative for the acquisition of hyperspectral images with a medium spatial resolution was the hyperspectral imager for the coastal ocean (HICO), a hyperspectral sensor with 87 spectral bands covering the visible and NIR range (400–900 nm) on-board of the International Space Station (ISS). HICO acquired programmed images from September 2009 to September 2014 with a spatial resolution of 90 m, higher than MERIS (300 m) and MODIS (250, 500 and 1000 m). Since HICO was a sensor developed for the monitoring of aquatic environments, several researches used it to monitor several parameters such as: seagrass and algae mapping , cloud removal , red tide detection , improved chl-
Bio-optical algorithms developed for inland or deep ocean waters are unable to uptake empirical algorithms developed especially for extreme environments.
In this chapter, we attempted to present an overview of the application of bio-optical algorithms to monitor water quality parameters as well as to assess chl-
2. Study site
The Araruama Lagoon is a hypersaline coastal lagoon located in the central coast of Rio de Janeiro State, Southeastern Brazil, between latitudes 22°50’S and 22°57’ S and the longitudes 42°00’ W and 42°44’ W. It is situated in a micro-region called “Região dos Lagos”, around 120 km from Rio de Janeiro City (Figure 1a,b). This region is densely populated showing a population density around 268 habitants per square kilometer . The lagoon area encompasses five municipalities: Araruama, Arraial do Cabo, Cabo Frio, Iguaba Grande, São Pedro da Aldeia and Cabo Frio (see Figure 1c).
From the morphological point view, the Araruama Lagoon consists of a series of elongated spits and shallow embayment presenting a longitudinal elongated shape with around 35 km in length and a mean width of 8 km; the maximum width is around 13 km. The surface area is around 220 km2 and the depth ranges from 1 to 17 m; the mean depth is around 3 m . The only connection between the Araruama Lagoon and sea, the Itajuru Channel, is located in the Cabo Frio City, Northeastern portion of the lagoon (see Figure 1c). The drainage basin covers around 320 km2, and permanent sources of freshwater come from Moças River and Mataruna River, in the Western portion of the Lagoon (see Figure 1c); the two rivers present a combined discharge of 1 m3/s .
The salinity of the Araruama Lagoon ranges from 35 to 43 practical salinity unit (psu) in the Itajuru Channel and from 46 to 56 psu in the main body of the Lagoon, being the salinity mainly balanced by the climatology of the area . According to the Köppen-Ginger classification scheme , the climate in the region can be classified as Tropical Monsoon (Am) with rainfall ranging between 36 (August) and 101 mm per month (December) and the air temperature ranging from 21 (August) and 25.4°C (February–March) along the year (, see Figure 2); the mean annual precipitation is 771 mm per year and the mean air temperature is around 23°C.
The water quality in the Araruama Lagoon has changed over the time, showing an increasing eutrophication along the past few years as a result of the increasing urban growth in the Região dos Lagos . According to the Trophic State Index (TSI) classification scheme proposed by , the Araruama Lagoon can be classified as eutrophic environment, with an average total phosphorous concentration around 0.09 mg/L and the average chl-
3. Materials and methods
3.1. Remote sensing data
HICO imageries of Araruama Lagoon were acquired from HICO's website database at Oregon State University (OSU) . The acquisition of the images over Araruama Lagoon occurred from 2011 to 2013, where only images without cloud cover over the lagoon were selected. HICO images are available with a Level 1B of processing, which corresponds to the radiance in the top of the atmosphere (LTOA) given in Wm-2μm-1sr-1 after the application of a division factor of 50. Table 2 lists the HICO imagery with clear sky over the Araruama Lagoon.
All these images were atmospherically corrected by the Second Signal in the Solar Spectrum (6S) implementation of Tafkaa algorithm . Tafkaa is a radiative transfer algorithm developed mainly for applications in the field of oceanic hyperspectral remote sensing, and it is based on an earlier code named ATmospheric REMoval (ATREM) . Tafkaa is available for processing HICO images online via a web tool , with prior registration. For the atmospheric correction over the Araruama Lagoon, the aerosol model was set to "maritime" and the atmospheric model was set to "tropical", since these characteristics seem to be the more appropriate for the study site. The final products of this process are delivered in units of
3.2. Limnological data
3.3. Bio-optical algorithms
Several empirical and semi-empirical bio-optical algorithms for chl-
Turbidity is usually identified by the high reflectance in the red and NIR spectral bands and is usually correlated to the total suspended solids concentration. Therefore, bio-optical algorithms for TSS can be used to estimate turbidity. The simplest algorithm uses the
3.4. Bio-optical algorithm development
Since the Araruama Lagoon is a hypersaline aquatic system, and the bio-optical algorithms listed in the previous section were develop for fresh or oceanic waters, we developed two empirical algorithms for the estimation of chl-
3.5. Bio-optical algorithms comparison
As described in Section 3.2, the data were divided in calibration (2011–12, 53 sampling points) and validation (2013, 34 sampling points) datasets. For the calibration dataset, a linear regression analysis was computed by the values of slope and intercept for each of the algorithms listed on Table 3 plus the two empirical algorithms developed by the use of ICE. The determination coefficient (
The validation process was computed by analysing a scatter plot between the measured and the estimated values of chl-
4. Results and discussions
4.1. ICE’s results
To compute the two-dimensional color correlation plot, the
The use of ICE generates two different two-dimensional color correlation plots, one for chl-
4.2. Algorithms performances
Calibration was conducted using the semi-empirical (Table 3) and empirical (Table 4) bio-optical algorithms. Linear regressions were computed between bio-optical algorithms and chl-
The poor performance of all algorithms could be associated to the fact that none of these algorithms were developed for hypersaline aquatic systems, which make their calibration difficult in this type of environment. Another source of error could be associated to the temporal window between the image acquisition and field sampling. Since we are using ground truth data that are collected as part of a routine monthly monitoring, we could not find an exact match with temporal windows ranging from 2 to more than 10 days. This can lead to erroneous interpretations since the dynamics of parameters, mainly the biotic ones such as phytoplankton, in the water column can change within days according to the environment dynamics. Adopting a 3-days window, the calibrations showed in Table 5 improved mainly for the chl-
If compared to the performance of 2BDA and 3BDA for the estimation of chl-
The validation of the bio-optical algorithms with the best
Based on these results for the chl
The validation for the turbidity bio-optical algorithms showed a better agreement between the validation plots and the error estimators. Figure 7 showed the validation plots for the four bio-optical algorithms analysed in this chapter: 1BDA (Figure 7A), 2BDA (Figure 7B), LSBA (Figure 7C) and EMPT (Figure 7D). The lowest NRMSE was 15.49% and was achieved by applying the 1BDA to the
4.3. Spatial distribution
Applications of bio-optical modeling to monitor water quality in inland waters have been increasing in the past decade, and this increase is also noticed in the public and private sector investments on remote sensing technologies to monitor water quality and quantity. The advantages of using remote sensing technologies over traditional methods to monitor water quality parameters were already discussed in the introduction of this chapter; however, another advantage of using remote sensing is in the spatial distribution of the data. While using traditional methods of water quality monitoring computes the spatial assessment of the water quality by performing spatial interpolations or by geostatistical methods of few sampling points, remote sensing images can provide different values for each pixel within the aquatic system. The difference is that the few sampling points used to interpolate the data for the aquatic system area is now replaced for several pixels values in the image, where the interpolation is not needed; therefore, it does not have the error caused by data interpolation methods. Figure 8 shows the spatial distribution of chl-
5. Final considerations
Based on the case study of Araruama Lagoon, we observe the need for calibration and validation of bio-optical algorithms in different inland waters since the variability of water column constituents from region to region is big. We also observe that the use of orbital hyperspectral sensors is important for the development of bio-optical modeling due to the number of spectral bands which allow us to study small features, such as the absorption peak of PC around 620 nm. Thus, narrow spectral bands can highlight specific absorption features which can be used in the development and improvement of bio-optical algorithms, mainly the semi- and quasi-analytical algorithms which are based on the radiative transfer theory. Therefore, future hyperspectral missions such as the Hyperspectral Imager SUIte (HISUI), the PRecursore IperSpettrale della Missione Applicativa (PRISMA), and the Environmental Mapping and Analysis Program (EnMAP) are important for the development of bio-optical modeling.
Moreover, these new hyperspectral missions will support a global mapping of inland water quality which is only possible through multispectral sensors such as Landsat, MODIS and MERIS. However, not all water quality parameters are possible to be measured only using multispectral sensors, for example, the Landsat series which have a poor spectral resolution that does not detect the spectral features such as peaks and trough of chl-
Finally, our case study showed that even by developing an empirical algorithm, the semi-empirical algorithms outperform them. The best performance for chl-
We thank the HICO team at Oregon State University (OSU), especially Jasmine Nahorniak for providing access to the database and to all her attention to us. C. A. S. Araújo thanks the Brazilian National Counsel of Technological and Scientific Development (CNPq) for the PCI fellowship (under the grant 300177/2015-1). M. P. Curtarelli also thanks the CNPq for the graduate scholarship (under the grant 161233/2013-9).
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