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Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis 期刊论文
GLOBAL CHANGE BIOLOGY, 2019
作者:  Kim, Yeonuk;  Johnson, Mark S.;  Knox, Sara H.;  Black, T. Andrew;  Dalmagro, Higo J.;  Kang, Minseok;  Kim, Joon;  Baldocchi, Dennis
收藏  |  浏览/下载：16/0  |  提交时间：2019/11/27
artificial neural network  comparison of gap-filling techniques  eddy covariance  machine learning  marginal distribution sampling  methane flux  random forest  support vector machine  
Gap Filling of High-Resolution Soil Moisture for SMAP/Sentinel-1: A Two-Layer Machine Learning-Based Framework 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (8) : 6986-7009
作者:  Mao, Hanzi;  Kathuria, Dhruva;  Duffield, Nick;  Mohanty, Binayak P.
收藏  |  浏览/下载：5/0  |  提交时间：2019/11/27
Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (7) : 5715-5737
作者:  Araya, Samuel N.;  Ghezzehei, Teamrat A.
收藏  |  浏览/下载：1/0  |  提交时间：2019/11/27
Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA 期刊论文
WATER RESOURCES RESEARCH, 2019, 55 (3) : 2142-2169
作者:  Legleiter, Carl J.;  Harrison, Lee R.
收藏  |  浏览/下载：7/0  |  提交时间：2019/11/26
remote sensing of rivers  bathymetric mapping  hyperspectral  unmanned aircraft system (UAS)  bathymetric LiDAR  satellite image data