摘要
针对水体叶绿素a浓度监测时大气介质影响光谱真实性的问题,文章从影像外部大气产品校正、内部大气补偿参数校正和光谱均值校正等方面分别对资源1号02D(ZY-1 02D)高光谱卫星影像进行大气校正以突出水体信号,同时借助多维光谱指数和CatBoost机器学习算法进一步提高水体叶绿素a浓度的反演精度。结果表明:大气校正算法在独山湖的应用中,6S优于QUAC,而FLAASH最差;CatBoost模型能够更好地拟合预测误差,提高反演精度;6S算法-四波段参数-CatBoost模型的反演组合效果最好(R^(2)=0.80)。
In the process of monitoring the concentration of chlorophyll-a in water,the destruction of the spectral authenticity by the atmospheric medium is a difficult problem to be solved.In response to this problem,this study focuses on atmospheric correction of ZY-102D hyperspectral satellite imagery from three perspectives:external atmospheric products,internal atmospheric compensation parameters and spectral mean of the image,so as to enhance water signal accuracy;At the same time,with the help of multi-dimensional spectral index,combined with the CatBoost machine learning algorithm,the inversion accuracy of water chlorophyll-a concentration is further improved.The results show that in the application of atmospheric correction algorithm in Dushan Lake,6S is better than QUAC,and FLAASH is the worst.CatBoost model can better fit the prediction error and improve the inversion accuracy.6S algorithm-Four-band parameter-CatBoost model inversion combination works best(R^(2) reaches 0.80).
作者
孟祥亮
冯建飞
付萍杰
张家威
张雨煊
孟飞
MENG Xiangliang;FENG Jianfei;FU Pingjie;ZHANG Jiawei;ZHANG Yuxuan;MENG Fei(School of Surveying,Mapping and Geographic Information,Shandong Jianzhu University,Jinan 250101,China;Shandong Ecological Environment Monitoring Center,Jinan 250101,China;School of Surveying and Spatial Information,Shandong University of Science and Technology,Qingdao 266000,China)
出处
《山东建筑大学学报》
2024年第1期98-107,共10页
Journal of Shandong Jianzhu University
基金
国家自然科学基金项目(42101388)。
