Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery

Xu, Min; Liu, Hongxing; Beck, Richard; Lekki, John; Yang, Bo; Shu, Song; Liu, Yang; Benko, Teresa; Anderson, Robert C.; Tokars, Roger; Johansen, Richard; Emery, Erich; Reif, Molly

doi:10.1109/tgrs.2019.2892899

Cited by 39 publications

(25 citation statements)

References 47 publications

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“…The Sentinel-2 series (European Space Agency) is a relatively new satellite remote sensing system comprised of two identical satellites with high spatial resolution (NIR and RGB bands in 10-20 m). The Multi-Spectral Instrument (MSI) onboard these satellites acquires images in 13 spectral bands spanning from 400 nm to 2400 nm [25]. Because it is a relatively new satellite platform with high spatial, temporal, radiometric resolution, high signal-to-noise ratio (SNR), and wide field of view, Sentinel-2 is a significantly better platform than the more commonly used Landsat series for coastal monitoring and hydrological modelling [26,27].…”

Section: Satellite and Aerial Remote Sensing Datamentioning

confidence: 99%

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Yang

Hawthorne

Torres

et al. 2019

Drones

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High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.

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Section: Satellite and Aerial Remote Sensing Datamentioning

confidence: 99%

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Yang

Hawthorne

Torres

et al. 2019

Drones

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“…Atmospheric correction of satellite images using the FLAASH software package in ENVI 5.3 was performed. This method has been widely applied to remove the atmospheric effect from remotely sensed imagery for estimating in-water constituents (Beck et al 2016;Kutser et al 2005;Watanabe et al 2017;Xu et al 2019). After atmospheric correction, some pseudo invariant features (e.g., large buildings and airport tarmacs) on the clearest image for each path or tile were used as the reference for normalizing the other images on the same path or tile.…”

Section: Preprocessing Of Multispectral Satellite Imagesmentioning

confidence: 99%

Regional analysis of lake and reservoir water quality with multispectral satellite remote sensing images

Xu¹,

Liu²,

Beck³

et al. 2019

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The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engineering and environmental challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the Department of Defense, civilian agencies, and our nation's public good. Find out more at www.erdc.usace.army.mil. To search for other technical reports published by ERDC, visit the ERDC online library at http://acwc.sdp.sirsi.net/client/default.

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“…A support vector machine (SVM) machine learning model was then calibrated and validated with the match-up points that were automatically identified within the GEE image data repository before its prediction power was evaluated by comparison with in situ samples analyzed in the laboratory. Figure 1 shows the extent of the study area and the locations of water quality samples obtained by the U.S. Army Corps of Engineers (USACE) Louisville District Water Quality Team [48]. In situ water quality data have been consistently collected for 12 USACE lakes in the tri-state region, including 5 in Kentucky (Barren River Lake, Green River Lake, Nolin River Lake, Rough River Lake, and Taylorsville Lake), 4 in Indiana (Brookville Lake, Cagles Mill Lake, Monroe Lake, and Patoka Lake), and 3 in Ohio (Caesar Creek Lake, West Fork Lake, and Harsha Lake) (https://www.lrl.usace.army.mil/ Missions/CivilWorks/Water-Information/Water-Quality/).…”

Section: Introductionmentioning

confidence: 99%

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Wang

Liu

et al. 2020

Remote Sensing

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Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

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Regionally and Locally Adaptive Models for Retrieving Chlorophyll-a Concentration in Inland Waters From Remotely Sensed Multispectral and Hyperspectral Imagery

Cited by 39 publications

References 47 publications

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

Regional analysis of lake and reservoir water quality with multispectral satellite remote sensing images

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

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