GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring

Spatiotemporal Variation of Turbidity Based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam

In recent years, seagrass beds in Cam Ranh Bay and Thuy Trieu Lagoon have declined from 800 to 550 hectares, resulting insignificantly reducing the number of fish catch. This phenomenon is due to the effect of the degradation of water environment. Turbidity is one of the most important water quality parameters directly related to underwater light penetration which affects the primary productivity. This study aims to investigate spatiotemporal variation of turbidity in the waters with major factors affecting its patterns using remote sensing data. An algorithm for turbidity retrieval was developed based on the correlation between in situ measurements and a red band of Landsat 8 OLI with R2 = 0.84 (p < 0.05). Simulating WAves Nearshore (SWAN) model was used to compute bed shear stress, a major factor affecting turbidity in shallow waters. In addition, the relationships between turbidity and rainfall, and bed shear stress induced by wind were analyzed. It was found that: (1) In the dry season, turbidity was low at the middle of the bay while it was high in shallow waters nearby coastlines. Resuspension of bed sediment was a major factor controlling turbidity during time with no rainfall. (2) In the rainy season or for a short time after rainfall in the dry season, turbidity was high due to a large amount of runoff entering into the study area

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3)

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

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 (R2 = 0.692, root mean square error (RMSE) = 21.4 mg m−3), Lake Kasumigaura (R2 = 0.866, RMSE = 11.3 mg m−3), and MERIS data over Lake Kasumigaura (R2 = 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

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

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 (R2) 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 (R2 = 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

Observations of water optical properties during red tide outbreaks off southeast Hokkaido by GCOM-C/SGLI: implications for the development of red tide algorithms

A severe red tide event, caused primarily by dinoflagellate Karenia selliformis, occurred during the autumn of 2021 in the waters off southeast Hokkaido and resulted in damage of $70 million to fishery industries. There is a high demand for early warning methods based on ocean colour observations to respond to future occurrences of red tides. We therefore used a quasi-analytical algorithm to compute the total absorption coefficient (a) from the Second Generation Global Imager (SGLI) remote sensing reflectance and assessed its potential to detect blooms of K. selliformis. We discovered that, within the same range of SGLI-retrieved chlorophyll-a concentrations, the a at a wavelength of 530 nm a(530) tended to be lower during K. selliformis blooms than during diatom blooms. The a(530) observed by the SGLI therefore showed promise as an optical property for detecting K. selliformis blooms. We discuss the reasons for selection of a(530) and the limitations of the current study’s bloom detection method. Red tide caused by K.selliformis in the waters off southeast Hokkaido resulted in damage of$70 million to fishery industriesRemote sensing-based observation that can serve as an early warning for red tide occurrence is thus required by fishery-related stakeholdersWe therefore investigated a remote sensing-based algorithm for detecting K.selliformis blooms in the waters off southeast Hokkaido Red tide caused by K.selliformis in the waters off southeast Hokkaido resulted in damage of $70 million to fishery industries Remote sensing-based observation that can serve as an early warning for red tide occurrence is thus required by fishery-related stakeholders We therefore investigated a remote sensing-based algorithm for detecting K.selliformis blooms in the waters off southeast Hokkaido</p