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A semi-operative approach to retrieve chlorophyll-a concentration from airborne/spaceborne spectrometer observations has been developed and tested using the airborne imaging spectrometer (AISA) data from 11 lakes located in southern Finland. The retrieval approach is empirical and requires nearly simultaneous in situ training data on water quality for the determination of regression coefficients. However, the training data does not have to be collected from every lake under investigation. Instead, the results obtained indicate that reliable estimates on the level of chlorophyll-a (chl-a) for an individual lake can be achieved without employing in situ data representing this specific lake. This enables the estimation of water quality from remotely sensed data for numerous lakes with the aid of reference data only for a few selected lakes representing the region under investigation. In addition, it is shown that the remotely sensed spectrum shape characteristics are highly affected by the trophic and humic state of the lake water.

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.