Journal ArticleDOI
A machine learning-based strategy for estimating non-optically active water quality parameters using Sentinel-2 imagery
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Water quality monitoring for small urban waterbodies by remote sensing gets to be difficult due to the coarse spatial resolution of remote-sensing imagery as discussed by the authors, and the recently launched Sentinel-2 produces...Abstract:
Water-quality monitoring for small urban waterbodies by remote sensing gets to be difficult due to the coarse spatial resolution of remote-sensing imagery. The recently launched Sentinel-2 produces...read more
Citations
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Journal ArticleDOI
Monitoring water quality using proximal remote sensing technology.
TL;DR: In this article, the authors proposed the concept of proximal remote sensing for monitoring water quality in inland waters by using the proximal hyperspectral imager, developed by Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (CAS) and Hikvision Digital Technology, Ltd.
Journal ArticleDOI
Monitoring water quality using proximal remote sensing technology
TL;DR: In this paper , the authors proposed the concept of proximal remote sensing for monitoring water quality in inland waters by using a high spatial, temporal and spectral resolution (1 nm) sensor for continuous observation.
Journal ArticleDOI
A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing.
TL;DR: In this paper, support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements and water temperature of Lake Huron and three other inland waterbodies.
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Evaluating traditional empirical models and BPNN models in monitoring the concentrations of chlorophyll-a and total suspended particulate of eutrophic and turbid waters
Bo Jiang,Liu Hailong,Xing Qianguo,Jiannan Cai,Xiangyang Zheng,Lin Li,Sisi Liu,Zhiming Zheng,Huiyan Xu,Ling Meng +9 more
TL;DR: In this article, the authors used in situ sensed reflectance to monitor the concentrations of chlorophyll-a (Chl-a) and total suspended particulate (TSP) of waters in the Pearl River Delta, which is featured by the highly developed network of rivers, channels and ponds, 135 sets of simultaneously collected water samples and reflectance were used to test the performance of the traditional empirical models (band ratio, three bands) and the machine learning models of a back-propagation neural network (BPNN).
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Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive
TL;DR: In this paper , a multimodal deep learning (MDL) model was developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a ), total phosphorus (TP), and total nitrogen (TN) in Lake Simcoe, Canada.
References
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Journal ArticleDOI
The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features
TL;DR: The Normalized Difference Water Index (NDWI) as mentioned in this paper is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery.
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A trophic state index for lakes1
TL;DR: A numerical trophic state index for lakes has been developed that incorporates most lakes in a scale of 0 to 100, which represents a doubling in algal biomass as well as various measures of biomass or production.
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Support vector machines in remote sensing: A review
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
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Ocean Color Chlorophyll Algorithms for SEAWIFS
John E. O'Reilly,Stéphane Maritorena,B. Greg Mitchell,David A. Siegel,Kendall L. Carder,Sara A. Garver,Mati Kahru,Charles R. McClain +7 more
TL;DR: In this article, a large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor).
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Implementation of machine-learning classification in remote sensing: an applied review
TL;DR: An overview of machine learning from an applied perspective focuses on the relatively mature methods of support vector machines, single decision trees (DTs), Random Forests, boosted DTs, artificial neural networks, and k-nearest neighbours (k-NN).