MODIS remote sensing monitoring method of leucocyan in eutrophic lakes

Abstract

The invention provides an MODIS remote sensing monitoring method of leucocyan in eutrophic lakes. The method comprises the following steps: images of eutrophic lakes are divided into phytoplankton bloom images and non-phytoplankton bloom images on the basis of MODIS RGB image manual interpretation and field measurement data analysis; with the combination of consecutive two years of satellite-ground synchronous data of the eutrophic lakes, an EOF decomposition-based leucocyan estimation algorithm is obtained; EOF algorithm-based leucocyan estimation results are obtained; and on the basis of the algorithm, a long-term sequence spatial and temporal distribution result of leucocyan in the eutrophic lakes is obtained. According to the method, pigment concentration is estimated after image classification. Thus, estimation result is more accurate, and a more comprehensive evaluation of water quality can be made. The method of the invention provides scientific supports for scientific decision-making of water resource management and water environment protection for water conservancy and environmental protection departments, etc.

Description

technical field [0001] The invention relates to a MODIS remote sensing monitoring method for phycocyanin in eutrophic lakes. Background technique [0002] Satellite remote sensing technology has the advantages of quasi-real-time, strong periodicity, wide coverage, and low cost. It can make up for the shortcomings of conventional water quality monitoring methods and has become an important means of real-time monitoring of lake water quality (Ma Ronghua, 2010). In the past, remote sensing research on cyanobacterial blooms mainly focused on chlorophyll a (Chlorophyll a, Chla), but chlorophyll a is a common pigment of all algae and cannot fully represent cyanobacteria. Phycocyanin pigment concentration (PC) is an indicator pigment of cyanobacteria, which can provide cyanobacteria information more effectively for a more comprehensive evaluation of water quality (Sui Zhanying et al., 2003). Therefore, the detection of the main pigment concentration of cyanobacteria is of great si...

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Problems solved by technology

[0011] In summary, although the above algorithms have successfully estimated the concentration of pigments, the estimation of phycocyanin concentration in Chaohu Lake still faces the following problems: most of the algorithms are based on the measured hyperspectral data, due to the band setting of the MODIS band sensor, semi-analytical and bio-optics The algorithm is difficult to apply to MODIS sensors; in addition, the limitation of atmospheric correction makes it impossible to obtain accurate remote sensing albedo data from MODIS satellite sensors, which leads to poor application of empirical algorithms

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