An Iterative Learning Framework for Multimodal Chlorophyll-a Estimation.
IEEE Transactions on Geoscience and Remote Sensing(2016)
摘要
Precise monitoring of the chlorophyll type “a” (chl-a) concentration is critical in determining the level of production of oxygen and, consequently, the health conditions of inland aquatic ecosystems. This paper addresses two important issues in building precise and robust regression models for chl-a concentration from remote sensing data: the presence of multimodality in the sensor data distribut...
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关键词
Lakes,MODIS,Water,Satellites,Indexes,Data models,Analytical models
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