COLLABORATING REMOTE SENSING WITH HISTORICAL LIMNOLOGICAL DATA TO MAP PRIMARY PRODUCTIVITY AT A EUTROPHIC LAKE

摘要

Primary productivity is a complex process, especially in shallow eutrophic inland waters, where there is considerable upwelling and mixing of bottom sediments during winds and considerable anthropogenic effects from the land-mass surrounding it. For mapping primary productivity by remote sensing in eutrophic lake therefore often involves costly and laborious sampling of the same simultaneous to the satellite overpass. This follows relating the lake-surface productivity to satellite retrieved radiances to develop some empirical algorithm for subsequent mapping. In this paper, a novel approach is presented where a satisfactory model based on historic limnological data of four parameters is utilized to map primary productivity at the lake Kasumigaura, Japan. The selected key water quality parameters, namely chlorophyll-a, suspended sediment, secchi disk depth and water temperature, can essentially represent the primary productivity and can be estimated from remote sensing imagery. The developed models can be used for any date of the year to generate satisfactory primary productivity maps at the lake by feeding the water quality maps of selected parameters as inputs. As the input variables are fewer, separate models for each month of a year is necessary for better approximation of the complex process of primary productivity. For the month of January, a neural network is successfully used to develop the productivity model with a coefficient of correlation (R2) >0.7 in both training and validation. Finally, a productivity map of Kasumigaura for 19th January, 2001 is generated for demonstration.