有色溶解有机物
高光谱成像
遥感
环境科学
溶解有机碳
波长
吸收(声学)
地理
地质学
光学
海洋学
浮游植物
物理
化学
营养物
有机化学
作者
Jinuk Kim,Wonjin Jang,Jin Hwi Kim,Jiwan Lee,Kyung Hwa Cho,Yong-Gu Lee,Kangmin Chon,Sanghyun Park,JongCheol Pyo,Yongeun Park,Seong‐Joon Kim
出处
期刊:International journal of applied earth observation and geoinformation
日期:2022-10-17
卷期号:114: 103053-103053
被引量:13
标识
DOI:10.1016/j.jag.2022.103053
摘要
Colored dissolved organic matter (CDOM) in inland waters is used as a proxy to estimate dissolved organic carbon (DOC) and may be a key indicator of water quality and nutrient enrichment. CDOM is optically active fraction of DOC so that remote sensing techniques can remotely monitor CDOM with wide spatial coverage. However, to effectively retrieve CDOM using optical algorithms, it may be critical to select the absorption coefficient at an appropriate wavelength as an output variable and to optimize input reflectance wavelengths. In this study, we constructed a CDOM retrieval model using airborne hyperspectral reflectance data and a machine learning model such as random forest. We evaluated the best combination of input wavelength bands and the CDOM absorption coefficient at various wavelengths. Seven sampling events for airborne hyperspectral imagery and CDOM absorption coefficient data from 350 nm to 440 nm over two years (2016–2017) were used, and the collected data helped train and validate the random forest model in a freshwater reservoir. An absorption coefficient of 355 nm was selected to best represent the CDOM concentration. The random forest exhibited the best performance for CDOM estimation with an R2 of 0.85, Nash-Sutcliffe efficiency of 0.77, and percent bias of 3.88, by using a combination of three reflectance bands: 475, 497, and 660 nm. The results show that our model can be utilized to construct a CDOM retrieving algorithm and evaluate its spatiotemporal variation across a reservoir.
科研通智能强力驱动
Strongly Powered by AbleSci AI