Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models

Chang, Ni‐Bin; Xuan, Zhemin; Yang, Yue-Tzu

doi:10.1016/j.rse.2013.03.002

Cited by 75 publications

(38 citation statements)

References 26 publications

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“…However, most of these algorithms do not determine phytoplankton species relative composition from remote sensing reflectance (R rs ) spectra. Recently, with the development of learning algorithms and increasing computing power, machine learning has been applied to the field of earth science [40], including related successful applications in ocean color remote sensing [41][42][43]. Most machine learning methods function well under a common assumption-training and validation datasets are in the same feature space and follow the same distribution rule [44].…”

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confidence: 99%

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

et al. 2019

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Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R 2 = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.focused on the identification of specific phytoplankton species or groups under blooming conditions using ocean color satellites with a moderate spectral resolution [14][15][16]. Furthermore, discrimination of phytoplankton phyla in blooming conditions has been achieved based on differences in optical signals among different phytoplankton populations [17]. However, these endeavors are constrained to a limited type of phytoplankton species or groups because of the limitations in band settings for multi-spectral remote sensing. Moreover, some studies have been focused on Case 1 water with relatively simple optical properties where optically active constituents co-vary with chlorophyll concentration [18,19]; however, it is more challenging to invert phytoplankton species composition from current ocean color remote sensing data with moderate spectral resolution in optically complex waters. Comparatively, hyperspectral remote sensing presents a promising tool to resolve the spectral variability of phytoplankton species [20].Several ocean color algorithms have also been proposed for phytoplankton information inversion in natural waters [21][22][23][24]. Among these efforts, a derivative spectroscopy/similarity index (SI) approach is the most common method for identifying dominant phytoplankton species or groups [25][26][27]. However, because SI-based approaches assign unknown spectra to the reference spectra that have the largest SI, only dominant species or groups can be identified, so it is difficult to determine phytoplankton species compo...

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confidence: 99%

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

et al. 2019

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“…Methods to remotely-sense water quality parameters, such as colored dissolved organic material (CDOM) (Le et al, 2013), nutrients (Chang et al, 2012(Chang et al, , 2013, chlorophylla (Le et al, 2013a,b,c) and other parameters (Hu et al, 2014), are now being developed in the region. These products may offer greater spatiotemporal coverage in determining the water quality state of the estuary over traditional in situ collection methods, ) - Table 2 Example of annual compliance assessment steps for a hypothetical bay segment in Tampa Bay.…”

Section: Future Efforts To Monitor the Benefits Of A Recovering Estuarymentioning

confidence: 99%

Tampa Bay estuary: Monitoring long-term recovery through regional partnerships

Sherwood

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Janicki³

et al. 2016

Regional Studies in Marine Science

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“…This satellite is providing us with data to improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere [49]- [51]. In the past decade, studies have shown the capability of MODIS data as a potential use for water quality monitoring [52]- [57]. However, MODIS data are highly vulnerable to cloud contamination as it is mainly retrieved across optical and infrared wavelength ranges.…”

Section: A Study Area and Data Sourcesmentioning

confidence: 99%

Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images

Chang

Bai

Chen

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of 7.4E − 04sr −1 between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications.Index Terms-Artificial neural network, cloud removal, computational intelligence, extreme learning machine, machine learning, satellite images.

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Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models

Cited by 75 publications

References 26 publications

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

Hyperspectral Remote Sensing of Phytoplankton Species Composition Based on Transfer Learning

Tampa Bay estuary: Monitoring long-term recovery through regional partnerships

Smart Information Reconstruction via Time-Space-Spectrum Continuum for Cloud Removal in Satellite Images

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