Timo Pyhälahti

Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons

The suitability of the AISA airborne imaging spectrometer for monitoring lake water quality was tested in four surveys carried out in southern Finland in 1996-1998. Altogether, 11 lakes were surveyed and the total number of stations with concurrent remote sensing and limnological measurements was 127. The ranges of the water quality variables were: the sum of chlorophyll a and phaeophytin a 1-100 microg l(-1), turbidity 0.4-26 FNU, total suspended solids 0.7-32 mg l(-1), absorption coefficient of aquatic humus at 400 nm 1.2-14 m(-1) and secchi disc transparency 0.4-7 m. For the retrieval analyses, 24 AISA channels in the 450-786 nm range with a channel width of 6-14 nm were used. The agreement between estimated and observed water quality variables was generally good and R2 for the best algorithms was in the range of 0.72-0.90 over the whole dataset. The channels used for May were, in most cases, the same as those for August, but the empirical parameters of the algorithms were different. After seasonal grouping, R2 varied from 0.84 to 0.95. The use of apparent reflectance instead of radiance improved the estimation of water quality in the case of total suspended solids and turbidity. In the most humic lake, the empirical algorithms tested were suitable only for the interpretation of total suspended solids and turbidity.

Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland.

The aim of the study was to test the feasibility of the band combination of the TERRA MODIS and ENVISAT MERIS instruments for operational monitoring of lakes and coastal waters in Finland. Also simulated LANDSAT TM data were tested. Satellite bands were simulated using airborne measurements with AISA imaging spectrometer. Semi-empirical algorithms with simulated satellite data were tested against field observations using regression analysis. Interpretation of chlorophyll a, suspended matter, turbidity and secchi-disk depth was included in the analyses. The data for this study were gathered in campaigns carried out in May and August 1997 and August 1998 both for lakes in southern Finland and coastal waters of the Baltic Sea. The data set included 85 in situ observations for lakes and 107 for coastal waters. Our results show that the band combination to be included in the ENVISAT MERIS instrument enables the interpretation of water quality, including chlorophyll a concentration using semi-empirical algorithms both for lakes and coastal waters. MERIS band 9 centred at 705 nm is proven to be of vital importance for the detection of chlorophyll a in local surface waters.

Water quality classification of lakes using 250-m MODIS data

The traditional method used in the water quality classification of Finnish lakes includes the collection of water samples from lakes and their analysis in laboratory conditions. The classification is based on statistical analysis of water quality parameter values and on expert opinion. It is possible to acquire similar information by using radiance values measured with the Earth Observing System Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). In this letter, the classification accuracy with MODIS data is about 80%. Only about 0.2% of the 20 391 pixels were misclassified by two or more classes, as a four-class classification system is used.

A filtering approach for estimating lake water quality from remote sensing data

Abstract In this paper we consider the estimation of lake water quality constituent distributions from hyperspectral remote sensing data. We present a computational approach that can be used to assimilate information from mathematical evolution models into data processing. The method is based on a reduced order iterated extended Kalman filter, and a convection–diffusion model is used to describe the movement of the water quality constituents. The performance of the technique is evaluated in a simulation study. The results show that the filter approach with an appropriate evolution model yields estimates that have better spatial and temporal resolutions than those obtained with conventional methods. Furthermore, the use of a feasible evolution model may make it possible to obtain information also on the concentrations in the lower parts of the lake.

The combined use of optical remote sensing data and unattended flow‐through fluorometer measurements in the Baltic Sea

Global algorithms for chlorophyll‐a (chl‐a) concentration and primary productivity retrieval from satellite observations are often not applicable to areas that are turbid and rich in coloured dissolved organic matter (CDOM), such as the Baltic Sea. Such a turbid (Case 2) water area, with its unique optical properties, often requires a parametrization to local conditions. Water quality algorithms are also sensitive to other naturally occurring phenomena, such as temporal variations in the atmospheric properties. Therefore, adequate reference data are essential for the estimation of water quality algorithm accuracy. In this study, a comparison with Sea‐viewing Wide Field of view Sensor (SeaWiFS) data and high‐frequency flow‐through fluorometer measurements is made. These so‐called ‘ship of opportunity’ data are measured with an automated continuous sampling system onboard several merchant and passenger ships in the Baltic Sea (SOOP, the Alg@line‐system). The study is concentrated on the monitoring of spring blooms in the years 1999 and 2000. By combining Alg@line observations with satellite instrument observations, the strengths of both data sources are obtained. The Alg@line‐fluorometer data ensure a sufficient amount of field samples to parametrize remote sensing chl‐a algorithms. Information with a full spatial coverage on chl‐a fluctuations can be obtained through the use of remote sensing data.

Analysis of Turbid Water Quality Using Airborne Spectrometer Data with a Numerical Weather Prediction Model-aided Atmospheric Correction

The effects of an atmospheric correction method for water quality estimation have been studied and validated for Airborne Imaging Spectrometer for Applications (AISA) data. This novel approach uses atmospheric input parameters from a numerical weather prediction model: HIRLAM (High Resolution Limited Area Model). The atmospheric correction method developed by de Haan and Kokke (1996) corrects the spectrometer data according to the coefficients calculated using Moderate Resolution Transmittance Code (MODTRAN) radiative transfer code simulations. The airborne campaigns were carried out at lake and coastal Case 2 type water areas between 1996 and 1998. The water quality interpretation was made using the MERIS satellite instrument wavelengths. The correction improved most of the water quality (turbidity, total suspended solids, and Secchi disk depth) estimates when data from several flight campaigns were used jointly. The atmospheric correction reduced the standard deviation of the measurements conducted on different days. The highest improvement was obtained in the estimation of turbidity.

Detection of oil pollution on sea ice with airborne and spaceborne spectrometer

In this work we demonstrate the feasibility of imaging spectrometer for the detection of oil spills on sea ice. We show that optical spectrometer images can be used as an alternative for oil spill mapping in winter when SAR-based detection algorithms fail due to ice. By comparing high-resolution airborne spectrometer image to satellite images, we evaluate the usability of MODIS and Landsat images for oil pollution detection on ice and discuss the limitations, set by image resolution and spectral band availability. We evaluate here several spectral indices and discuss the results. We propose simple algorithms for oil detection on ice. Our study strongly suggests that an imaging spectrometer suits very well to oil detection on sea ice. However usability of satellite instruments like MODIS have serious limitations set by the image resolution and band selection. Landsat ETM has significantly better resolution and it is therefore more suitable for most typical, small-scale pollution detection, but its imaging frequency does not meet the monitoring demands

Monitoring of turbid coastal and inland waters by airborne imaging spectrometer AISA

Reliable monitoring of the pelagic ecosystem has proved to be problematic because of its high temporal and spatial heterogeneity. Processes like algal blooms or pollution discharges are patchy, both temporally and spatially. Consequently, they often remain unobserved using the traditional sampling methods based on temporally sparse sampling at a few fixed stations. Furthermore, the traditional programs are usually unable to rapidly report of exceptional events. Monitoring of water quality could be more effective if satellite or airborne remote sensing is used. New optical satellite sensors with high spectral resolution have recently been launched and more sensors will be available in the near future. Empirical algorithms, like spectral ratios, are widely used in the interpretation of remote sensing data. However, these algorithms seem to have local and seasonal variability and different algorithms are needed for coastal and inland waters.

Advances in combining optical citizen observations on water quality with satellite observations as part of an environmental monitoring system

Citizen observations, environmental data gathered by volunteers without professional observation capabilities, have been extensively used for Finnish water quality monitoring tasks. Recently, mobile smartphones and their digital cameras have enabled more direct measurements of transparency related water quality variables with inexpensive technology suitable for volunteers. These “Secchi3000” ideas of measurement technology by viewing known targets through multiple viewing path lengths within measured water were used to develop an iQwtr measurement device for water transparency related citizen observations. Past experiences with crowdsourcing and use of in situ water transparency data with satellite observations are reviewed and future challenges outlined.