Lake eutrophication and water bloom outbreak is one of the most important environmental problems faced by lakes in the world currently, especially the cyanobacterial bloom has become the focus for lake eutrophication control concerned by the world. Blue-green algae can lead the deterioration of water quality and use up the oxygen in the water and cause the death of fish if severe. Using remote sensing technology to monitor cyanobacterial bloom has incomparable advantages other methods don’t have. It can rapidly and timely provide the whole lake or whole area’s water bloom distribution status. In recent years, remote sensing monitoring on cyanobacterial bloom in lake water body has made great progress which mainly concentrates on remote sensing estimation of concentration of chlorophyll and phycocyanin. However the above-mentioned two parameters can not reflect the information of blue-green algae abundance in water body alone. Under the funding from National Natural Science Foundation of China and key programs of institute’s “135” plan, Zhang Yunlin research team of Nanjing Institute of Geography & Limnology made great progress on remote sensing quantitative estimation on blue-green algae abundance in water body like inland lakes.

This research uses the specific value of chlorophyll concentration (Chla) and phycocyanin concentration (PC) to represent blue-green algae abundance. It first analyzes the correlativity between PC/Chla and remote sensing reflectance（Rrs） in actual measurement and finds that there is high correlation between Rrs(550)/Rrs(620)and PC/Chla, and then builds estimation model for blue-green algae abundance and achieves estimation on lake water body’s blue-green algae abundance based on remote sensing technology. This algorithm successfully uses the aerial image data (AISA) obtained by airborne hyperspectral instrument to obtain the spatial distribution characteristics of water body blue-green algae abundance in research area. At last, it discusses this algorithm’s application potential that is using the blue-green algae abundance data extracted by remote sensing to evaluate the risk degree of blue-green algae outbreak in research area. This algorithm has high precision and strong applicability. It can recognize blue-green algae’s abundance characteristics only by selecting limited Rrs with specific wave length. It richens the content of lake remote sensing monitoring technology, and provide more scientific basis for using remote sensing to monitor water bloom outbreak and blue-green algae control. The above-mentioned research results are published on the latest Water Research (2015, 68: 217-226）.