Inversing chlorophyll-a concentration by multi-temporal models using TM images

Yunmei Li; Wanning Lu; Haijun Wang

doi:10.1117/12.725430

29 January 2007 Inversing chlorophyll-a concentration by multi-temporal models using TM images

Yunmei Li, Wanning Lu, Haijun Wang

Proceedings Volume 6279, 27th International Congress on High-Speed Photography and Photonics; 62795H (2007) https://doi.org/10.1117/12.725430
Event: 27th International congress on High-Speed Photography and Photonics, 2006, Xi'an, China

Abstract

Chlorophyll is a very important parameter for lake water quality evaluation. Its concentration varies seriously with different season. The chlorophyll-a concentration inversing models in different season were studied using different temporal TM images. The models were built in 3 steps: Firstly, 10 images were selected according to the principle of almost synchronously with in situ measurement; secondly, remote sensing images were preprocessed. Atmospheric corrections were carried out use 6S model, and then, the images were geometric corrected; lastly, the optimum models for chlorophyll-a concentration inversing were discussed for multi-temporal TM images. The water quality parameters were measured on 21 sample points in Tai Lake, China monthly as the monitoring network. The chlorophyll-a concentration inversing models were built using semi-empirical approach by the integrated use of multi-temporal remote sensing data and in situ data. The spectrum character of chlorophyll was analyzed following other's studying. Then the different composed bands and component modes such as TM4/TM3, (TM4-TM3)/(TM4+TM3), TM3*TM4/ln(TM1), etc. were discussed for building the regression models. The inversing accuracy was evaluated by relatively error. The optimum models were selected for each month by comparing the different models. It could be concluded that: The mode of multi-temporal equations might be the same or similar for different month. But the coefficients were quite different; the reflectance of TM3 and TM2 band were the most often used parameter for model building; the estimated accuracy increased with raising chlorophyll-a concentration. For example, when the chlorophyll-a concentration was lower than 0.009mg/l, the estimated value was not so accuracy. But when the chlorophyll-a concentration raised to 0.05mg/l the relatively errors for all samples were less than 30%.

Citation Download Citation

Yunmei Li, Wanning Lu, and Haijun Wang "Inversing chlorophyll-a concentration by multi-temporal models using TM images", Proc. SPIE 6279, 27th International Congress on High-Speed Photography and Photonics, 62795H (29 January 2007); https://doi.org/10.1117/12.725430

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