An assessment of MERIS algal products during an intense bloom in Lake of the Woods

Binding, Caren; Greenberg, Tracie A.; Jerome, John H.; Bukata, R. P.; Létourneau, G.

doi:10.1093/plankt/fbq133

Cited by 76 publications

(48 citation statements)

References 21 publications

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“…The algorithms that were compared within the two studies were both based on the neural network and their results revealed that the EUL processor was the most accurate algorithm. Inter-comparison between the band height and neural network algorithms showed that the band height algorithms outperformed the neural network as reported by Binding et al [36] and Lankester et al [37].…”

Section: Introductionsupporting

confidence: 57%

“…In addition, the upper limit of Chla concentrations during the above-mentioned studies were less than 70 mg·m −3 [35][36][37], which does not represent highly turbid water bodies. The aim of the current study was to investigate the performance of the seven algorithms over a complex water body (i.e., Lake Kasumigaura), which is characterized by a trophic environment covering broad statuses (i.e., mesotrophic to hypertrophic).…”

Section: Introductionmentioning

confidence: 88%

“…The variation in the central wavelength per pixel can be in the range of 0.1 nm for one camera and within 1.7 nm between the five cameras [53]. The land detection expression for EUL, BOL, and C2R processors was modified to 'toa_reflec_10 > toa_reflec_6 AND toa_reflec_13 > 0.01' for a better representation of the coastline between the lake and land, as indicated by Binding et al [36]. As many of the lakes' pixels were inaccurately masked as land for the FLH, MCI, and MPH processors, the 'l1_flags.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

et al. 2017

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Abstract:The chlorophyll-a (Chla) products of seven processors developed for the Medium Resolution Imaging Spectrometer (MERIS) sensor were evaluated. The seven processors, based on a neural network and band height, were assessed over an optically complex water body with Chla concentrations of 8.10-187.40 mg·m −3 using 10-year MERIS archival data. These processors were adopted for the Ocean and Land Color Instrument (OLCI) sensor. Results indicated that the four processors of band height (i.e., the Maximum Chlorophyll Index (MCI_L1); and Fluorescence Line Height (FLH_L1)); neural network (i.e., Eutrophic Lake (EUL); and Case 2 Regional (C2R)) possessed reasonable retrieval accuracy with root mean square error (R 2 ) in the range of 0.42-0.65. However, these processors underestimated the retrieved Chla > 100 mg·m −3 , reflecting the limitation of the band height processors to eliminate the influence of non-phytoplankton matter and highlighting the need to train the neural network for highly turbid waters. MCI_L1 outperformed other processors during the calibration and validation stages (R 2 = 0.65, Root mean square error (RMSE) = 22.18 mg·m −3 , the mean absolute relative error (MARE) = 36.88%). In contrast, the results from the Boreal Lake (BOL) and Free University of Berlin (FUB) processors demonstrated their inadequacy to accurately retrieve Chla concentration > 50 mg·m −3 , mainly due to the limitation of the training datasets that resulted in a high MARE for BOL (56.20%) and FUB (57.00%). Mapping the spatial distribution of Chla concentrations across Lake Kasumigaura using the seven processors showed that all processors-except for the BOL and FUB-were able to accurately capture the Chla distribution for moderate and high Chla concentrations. In addition, MCI_L1 and C2R processors were evaluated over 10-years of monthly measured Chla as they demonstrated the best retrieval accuracy from both groups (i.e., band height and neural network, respectively). The retrieved Chla of MCI_L1 was more accurate at tracking seasonal and annual variation in Chla than C2R, with only slight overestimation occurring during the springtime.

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Section: Introductionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 88%

Section: Accuracy Assessmentmentioning

confidence: 99%

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Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

et al. 2017

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“…The matchup process resulted in a total of 13 images (i.e., five images at the same day and eight images within a one-day difference). All images were pre-processed using a Smile Correction processor to adjust the variations in the spectral wavelengths among MERIS's five cameras and within the same camera [76]. In order to convert MERIS L1b top-of-atmosphere radiances to water-leaving reflectance, the Case-2 Regional (C2R), the Eutrophic Lake (EUL) and Boreal Lake (BL) processors are neural network modules for performing atmospheric correction in Case 2 waters [35,77].…”

Section: Meris Images Processingmentioning

confidence: 99%

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

et al. 2017

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Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms' indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R 2 = 0.692, root mean square error (RMSE) = 21.4 mg m −3 ), Lake Kasumigaura (R 2 = 0.866, RMSE = 11.3 mg m −3 ), and MERIS data over Lake Kasumigaura (R 2 = 0.57, RMSE = 36.5 mg m −3 ). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

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“…The MODIS sensor has been shown to be effective in estimating water clarity, chlorophyll concentrations, and suspended sediments in a variety of water types: oceans, coastal areas, the Great Lakes, and large inland lakes [27][28][29][30][31][32][33][34][35][36][37][38]. However, few studies have considered its use for monitoring water clarity on a regional or statewide basis, including potentially hundreds of large inland lakes [39].…”

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confidence: 99%

Application of MODIS Imagery for Intra-Annual Water Clarity Assessment of Minnesota Lakes

Knight

Voth

2012

Remote Sensing

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Abstract:Monitoring of water clarity trends is necessary for water resource managers. Remote sensing based methods are well suited for monitoring clarity in water bodies such as the inland lakes in Minnesota, United States. This study evaluated the potential of using imagery from NASA's MODIS sensor to study intra-annual variations in lake clarity. MODIS reflectance images from six dates throughout the 2006 growing season were used with field collected Secchi disk transparency data to estimate water clarity in large lakes throughout Minnesota. The results of this research indicate the following: water clarity estimates derived from MODIS imagery are largely similar to those derived from lower temporal resolution sensors such as Landsat, robust water clarity estimates can be derived using MODIS for many dates throughout a growing season (R 2 values between 0.32 and 0.71), and the relatively low spatial resolution of MODIS restricts its applicability to a subset of the largest inland lakes (>160 ha, or 400 acres). This study suggests that water clarity maps developed with MODIS imagery and bathymetry data may be useful tools for resource managers concerned with intra-and inter-annual variations in large inland lakes.

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An assessment of MERIS algal products during an intense bloom in Lake of the Woods

Cited by 76 publications

References 21 publications

Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

Evaluation of MERIS Chlorophyll-a Retrieval Processors in a Complex Turbid Lake Kasumigaura over a 10-Year Mission

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

Application of MODIS Imagery for Intra-Annual Water Clarity Assessment of Minnesota Lakes

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