Quantitative retrieval of chlorophyll-a by remote sensing based on TM data in Lake Taihu

Heng Lu; Jiazhu Huang; Yunmei Li

doi:10.1117/12.780907

25 April 2008 Quantitative retrieval of chlorophyll-a by remote sensing based on TM data in Lake Taihu

Heng Lu, Jiazhu Huang, Yunmei Li

Proceedings Volume 7000, Optical and Digital Image Processing; 700025 (2008) https://doi.org/10.1117/12.780907
Event: SPIE Photonics Europe, 2008, Strasbourg, France

Abstract

Based on TM (ETM) data and in-situ measurements of chlorophyll-a concentration (Chl-a) in Lake Taihu, analysis was conducted to decide the correlation between Chl-a and the ratios of different reflectance corrected by the 6S model. The results show Chl-a is closely related to TM3/(TM1+TM4) and the inverse model to infer Chl-a in Lake Taihu can be written as Ln(Chl-a)=-9.247*(TM1+TM4)/(TM2+TM3) -27.903*TM3/(TM1+TM4) +24.518. However, the accuracy of this model can not be assured due to the complexity of spectral reflectance strongly dependant on water quality in Lake Taihu. Thus we developed a further 2-layer BP neural net model based on 4 input nodes, 7 hide nodes and 1 output node to for calculating Chl-a in the lake. The derived results reveal that the BP model has much higher accuracy than the linear model. A test was made based on 16 samples and suggests that the maximum relative error (RE) of BP model was only 35.43%. Of all the samples, 15 ones had a RE of less than 30% from the BP model.. However, there were only 3 samples with RE less than 30% from the results derived from the linear model. The comparison shows that the BP model has high availability for inferring Chl-a of surface water having complex spectral reflectance.

Citation Download Citation

Heng Lu, Jiazhu Huang, and Yunmei Li "Quantitative retrieval of chlorophyll-a by remote sensing based on TM data in Lake Taihu", Proc. SPIE 7000, Optical and Digital Image Processing, 700025 (25 April 2008); https://doi.org/10.1117/12.780907

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KEYWORDS

Neural networks

Data modeling

Reflectivity

Atmospheric modeling

Remote sensing

Atmospheric corrections

Satellites

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