Assessment of underwater light climate for Lake Dahuchi using field spectral data and Landsat TM
A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm
with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available
radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater
PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval.
The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements.
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Document Type: Research Article
School of Resource and Environmental Science, Wuhan University, Wuhan, PR China,Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan, PR China
School of Resource and Environmental Science, Wuhan University, Wuhan, PR China,Key Laboratory of Digital Mapping and Land Information Application of State Bureau of Surveying and Mapping, Wuhan University, Wuhan, PR China
International Institute for Geo-Information Science and Earth Observation, The Netherlands
Publication date: 2010-02-01
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