Jenni Vepsäläinen

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.

Improved linear interpolation method for the estimation of snow-covered area from optical data

Spatially well-distributed information on the regional fraction of snow-covered area (SCA) is important to snow hydrology during the melting season. One approach for regional SCA estimation using visible and near-infrared reflectances is based on linear interpolation between reference reflectances for full snow cover and snow-free conditions. We present an improved method for National Oceanographic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) imagery with (1) an automated determination of reference reflectances by distinguishing wet and dry snow conditions and, on the other hand, near melt-off and totally melt-off conditions and (2) an employment of Normalized Difference Vegetation Index (NDVI) to avoid overestimations due to vegetation cover at the end of the melting season. The study site covers the area of Finland, which serves as an example of the Eurasian boreal coniferous forest zone. Finnish drainage basins are used as areal calculation units in order to produce feasible information for hydrological models. Since the frequent cloudiness in the northern latitudes reduces the availability of optical data, we developed a technique to generate reference reflectances for basins that were obscured at the actual moment of data retrieval. For a basin without a reference value, the proper values were derived from a basin of the same characteristics; the similarity was described with a special Forest Sparseness Index generated from AVHRR data. The linear interpolation method with the additional features was tested for AVHRR imagery during melting period 2000. Validation against a comprehensive network of ground observations at snow courses and weather stations indicated good performance.

Analysis on the feasibility of multi-source remote sensing observations for chl-a monitoring in Finnish lakes.

Chlorophyll-a (chl-a) concentration of lake water can be measured with airborne (or spaceborne) optical remote sensing instruments. The rmse obtained here with empirical algorithms and 122 measurement points was 8.9 microg/l (all points used for training and testing). Airborne Imaging Spectrometer for Applications (AISA) was used in four lake water quality measurement campaigns (8 measurement days) in southern Finland during 1996-1998 with other airborne instruments and extensive in situ data collection. As empirical algorithms are employed for chl-a retrieval from remote sensing data, temporally varying factors such as surface reflection and atmospheric effects degrade the estimation accuracy. This paper analyzes the quantitative accuracy of empirical chl-a retrieval algorithms available as methods to correct temporal disturbances are either included or excluded. The aim is to evaluate the usability of empirical chl-a retrieval algorithms in cases when no concurrent reference in situ data are available. Four methods to reduce the effects of temporal variations are investigated. The methods are: (1) atmospheric correction; (2) synchronous radiometer data; (3) wind speed data; and (4) bidirectional scattering model based on wind speed and sun angle data. The effects of different correction methods are analyzed by using single-date test data and multi-date training data sets. The results show that the use of a bidirectional scattering model and atmospheric correction reduces the bias component of the measurement error. Radiometer data also appear to improve the accuracy. However, if concurrent in situ reference data are not available, the retrieval algorithms and correction methods should be improved for reducing the bias error.

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.

Use of MODIS data for monitoring turbidity in Finnish lakes

The state of surface waters is affected by the quantity and quality of various suspended and dissolved substances. Loading from sources such as agriculture, fish farming, industry and municipalities can cause eutrophication and have other adverse effects on water quality. In Finland the quality of lake water is traditionally determined by collecting water samples and analyzing the samples in a laboratory and by making on-site measurements (e.g. Secchi depth, temperature). However, sampling is slow and expensive and does not include all lakes. Remote sensing instruments can cover large areas quickly and repeatedly and they are also relatively cheap to use once the retrieval methods have been developed into operational level. However, the revisit time for Landsat is 16 days so it is not suitable for operational monitoring especially in areas where cloud cover is frequent. Another interesting instrument is SeaWiFS. It produces daily data but only with 1000-m pixels. One of the latest spaceborne remote sensing instruments, the MODIS spectrometer, produces daily data with two 250-m channels. The objective of this paper is to assess the capabilities of MODIS for monitoring turbidity in Finnish lakes.