A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Chlorophyll <i>a</i>algorithms for oligotrophic oceans: A novel approach based on three‐band reflectance difference

Chuanmin Hu, Zhongping Lee, Bryan A. Franz · Journal of Geophysical Research Atmospheres · 2011 · 941 citations · Full text

Natural Sciences, Marine Sciences, Oceanography +16

Review of constituent retrieval in optically deep and complex waters from satellite imagery

Daniel Odermatt, Anatoly A. Gitelson, Vittorio Brando et al. · Remote Sensing of Environment · 2011 · 509 citations

Natural Sciences, Earth Sciences, Oceanography +14

The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

Yile Ao, Hongqi Li, Liping Zhu et al. · Journal of Petroleum Science and Engineering · 2018 · 326 citations

Computing, Computer Science, Forestry +13

Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands

Giorgio Dall’Olmo, Anatoly A. Gitelson, Donald C. Rundquist et al. · Remote Sensing of Environment · 2005 · 302 citations

Natural Sciences, Marine Sciences, Oceanography +12

Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China)

Boqiang Qin, Wei Li, Guangwei Zhu et al. · Journal of Hazardous Materials · 2015 · 230 citations

Life Sciences, Ecology, Microbiology +14