Monitoring cyanobacteria occurrence in freshwater reservoirs using semi-analytical algorithms and orbital remote sensing

2020 ◽  
Vol 71 (5) ◽  
pp. 569 ◽  
Author(s):  
Henrique Dantas Borges ◽  
Rejane Ennes Cicerelli ◽  
Tati de Almeida ◽  
Henrique L. Roig ◽  
Diogo Olivetti
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Cyanobacterial Blooms ◽  
Inland Waters ◽  
Strongly Correlated ◽  
Chl A ◽  
Analytical Algorithm ◽  
Remote Sensing Techniques ◽  
Better Than ◽  
Sentinel 2

Cyanobacterial blooms pose a serious threat to the multiple uses of inland waters because of their adverse effects on the environment and human health. Monitoring cyanobacteria concentrations using traditional methods can be expensive and impractical. Recently, alternative efforts using remote sensing techniques have been successful. In particular, semi-analytical modelling approaches have been used to successfully predict chlorophyll (Chl)-a concentrations from remote sensing reflectance. The aims of this study were to test the performance of different semi-analytical algorithms in the estimation of Chl-a concentrations and the applicability of Sentinel-2 multispectral instrument (MSI) imagery, and its atmospheric correction algorithms, in the estimation of Chl-a concentrations. For our dataset, phycocyanin concentration was strongly correlated with Chl-a concentration and the inversion model of inland waters (IIMIW) semi-analytical algorithm was the best performing model, achieving a root mean square error of 4.6mgm–3 in the prediction of Chl-a. When applying the IIMIW model to MSI data, the use of top-of-atmosphere reflectance performed better than the atmospheric correction algorithm tested. Overall, the results were satisfactory, demonstrating that even without an adequate atmospheric correction pipeline, the monitoring of cyanobacteria can be successfully achieved by applying a semi-analytical bio-optical model to MSI data.

scholarly journals Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

2021 ◽  
Vol 13 (14) ◽  
pp. 2821
Author(s):  
Runfei Zhang ◽  
Zhubin Zheng ◽  
Ge Liu ◽  
Chenggong Du ◽  
Chao Du ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
Atmospheric Correction ◽  
Dianchi Lake ◽  
Inland Waters ◽  
Eutrophic Lakes ◽  
Correction Model ◽  
Chl A

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

scholarly journals The Use of Sentinel-2 for Chlorophyll-a Spatial Dynamics Assessment: A Comparative Study on Different Lakes in Northern Germany

2021 ◽  
Vol 13 (8) ◽  
pp. 1542
Author(s):  
Igor Ogashawara ◽  
Christine Kiel ◽  
Andreas Jechow ◽  
Katrin Kohnert ◽  
Thomas Ruhtz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Spectral Band ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
Spatial Dynamics ◽  
Eutrophic Lakes ◽  
Chl A ◽  
Spatial Coverage ◽  
Sentinel 2

Eutrophication of inland waters is an environmental issue that is becoming more common with climatic variability. Monitoring of this aquatic problem is commonly based on the chlorophyll-a concentration monitored by routine sampling with limited temporal and spatial coverage. Remote sensing data can be used to improve monitoring, especially after the launch of the MultiSpectral Instrument (MSI) on Sentinel-2. In this study, we compared the estimation of chlorophyll-a (chl-a) from different bio-optical algorithms using hyperspectral proximal remote sensing measurements, from simulated MSI responses and from an MSI image. For the satellite image, we also compare different atmospheric corrections routines before the comparison of different bio-optical algorithms. We used in situ data collected in 2019 from 97 sampling points across 19 different lakes. The atmospheric correction assessment showed that the performances of the routines varied for each spectral band. Therefore, we selected C2X, which performed best for bands 4 (root mean square error—RMSE = 0.003), 5 (RMSE = 0.004) and 6 (RMSE = 0.002), which are usually used for the estimation of chl-a. Considering all samples from the 19 lakes, the best performing chl-a algorithm and calibration achieved a RMSE of 16.97 mg/m3. When we consider only one lake chain composed of meso-to-eutrophic lakes, the performance improved (RMSE: 10.97 mg/m3). This shows that for the studied meso-to-eutrophic waters, we can reliably estimate chl-a concentration, whereas for oligotrophic waters, further research is needed. The assessment of chl-a from space allows us to assess spatial dynamics of the environment, which can be important for the management of water resources. However, to have an accurate product, similar optical water types are important for the overall performance of the bio-optical algorithm.

scholarly journals Deriving inherent optical properties and associated inversion-uncertainties in the Dutch Lakes

2009 ◽  
Vol 13 (7) ◽  
pp. 1113-1121 ◽  
Author(s):  
M. S. Salama ◽  
A. Dekker ◽  
Z. Su ◽  
C. M. Mannaerts ◽  
W. Verhoef
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Atmospheric Correction ◽  
Inland Waters ◽  
Data Sets ◽  
Modified Model ◽  
Chl A ◽  
Inherent Optical Properties ◽  
Data Inversion ◽  
Suspended Particulate

Abstract. Remote sensing of water quality in inland waters requires reliable retrieval algorithms, accurate atmospheric correction and consistent method for uncertainty estimation. In this paper, the GSM semi-analytical inversion model is modified for inland waters to derive inherent optical properties (IOPs) and their spectral dependencies from air and space borne data. The modified model was validated using two data sets from the Veluwe and the Vecht Dutch lakes. For the Veluwe lakes, the model was able to derive a linear relationship between measured concentrations and estimated IOPs with R2 values above 0.7 for chlorophyll-a (Chl-a) and up to 0.9 for suspended particulate matters (SPM). In the Vecht lakes, the modified model derived accurate values of IOPs. The R2 values were 0.89 for Chl-a and up to 0.95 for SPM. The RMSE values were 0.93 mg m−3 and 0.56 g m−3 for Chl-a and SPM respectively. Finally, the IOPs of the Veluwe lakes are derived from multi-spectral, ocean color and hyperspectral airborne data. Inversion-uncertainties of the derived IOPs were also estimated using a standard nonlinear regression technique. The study shows that inversion-uncertainties of remote sensing derived IOPs are proportional to water turbidity.

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Retrieving Total and Inorganic Suspended Sediments in Amazon Floodplain Lakes: A Multisensor Approach

2019 ◽  
Vol 11 (15) ◽  
pp. 1744 ◽  
Author(s):  
Daniel Maciel ◽  
Evlyn Novo ◽  
Lino Sander de Carvalho ◽  
Cláudio Barbosa ◽  
Rogério Flores Júnior ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Floodplain Lakes ◽  
Empirical Modeling ◽  
Landsat 8 ◽  
Amazon Floodplain ◽  
The Impact ◽  
Sentinel 2

Remote sensing imagery are fundamental to increasing the knowledge about sediment dynamics in the middle-lower Amazon floodplains. Moreover, they can help to understand both how climate change and how land use and land cover changes impact the sediment exchange between the Amazon River and floodplain lakes in this important and complex ecosystem. This study investigates the suitability of Landsat-8 and Sentinel-2 spectral characteristics in retrieving total (TSS) and inorganic (TSI) suspended sediments on a set of Amazon floodplain lakes in the middle-lower Amazon basin using in situ Remote Sensing Reflectance (Rrs) measurements to simulate Landsat 8/OLI (Operational Land Imager) and Sentinel 2/MSI (Multispectral Instrument) bands and to calibrate/validate several TSS and TSI empirical algorithms. The calibration was based on the Monte Carlo Simulation carried out for the following datasets: (1) All-Dataset, consisting of all the data acquired during four field campaigns at five lakes spread over the lower Amazon floodplain (n = 94); (2) Campaign-Dataset including samples acquired in a specific hydrograph phase (season) in all lakes. As sample size varied from one season to the other, n varied from 18 to 31; (3) Lake-Dataset including samples acquired in all seasons at a given lake with n also varying from 17 to 67 for each lake. The calibrated models were, then, applied to OLI and MSI scenes acquired in August 2017. The performance of three atmospheric correction algorithms was also assessed for both OLI (6S, ACOLITE, and L8SR) and MSI (6S, ACOLITE, and Sen2Cor) images. The impact of glint correction on atmosphere-corrected image performance was assessed against in situ glint-corrected Rrs measurements. After glint correction, the L8SR and 6S atmospheric correction performed better with the OLI and MSI sensors, respectively (Mean Absolute Percentage Error (MAPE) = 16.68% and 14.38%) considering the entire set of bands. However, for a given single band, different methods have different performances. The validated TSI and TSS satellite estimates showed that both in situ TSI and TSS algorithms provided reliable estimates, having the best results for the green OLI band (561 nm) and MSI red-edge band (705 nm) (MAPE < 21%). Moreover, the findings indicate that the OLI and MSI models provided similar errors, which support the use of both sensors as a virtual constellation for the TSS and TSI estimate over an Amazon floodplain. These results demonstrate the applicability of the calibration/validation techniques developed for the empirical modeling of suspended sediments in lower Amazon floodplain lakes using medium-resolution sensors.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

2019 ◽  
Vol 64 (20) ◽  
pp. 1540-1556 ◽  
Author(s):  
Kun Shi ◽  
Yunlin Zhang ◽  
Boqiang Qin ◽  
Botian Zhou
Keyword(s):  
Remote Sensing ◽  
Present Knowledge ◽  
Cyanobacterial Blooms ◽  
Inland Waters ◽  
Future Challenges

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

scholarly journals Comparison of Lake Optical Water Types Derived from Sentinel-2 and Sentinel-3

2019 ◽  
Vol 11 (23) ◽  
pp. 2883 ◽  
Author(s):  
Soomets ◽  
Uudeberg ◽  
Jakovels ◽  
Zagars ◽  
Reinart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lake Water ◽  
Natural Waters ◽  
Critical Role ◽  
Inland Waters ◽  
Water Types ◽  
Lake Võrtsjärv ◽  
Level 2 ◽  
Sentinel 2

Inland waters play a critical role in our drinking water supply. Additionally, they areimportant providers of food and recreation possibilities. Inland waters are known to be opticallycomplex and more diverse than marine or ocean waters. The optical properties of natural waters areinfluenced by three different and independent sources: phytoplankton, suspended matter, andcolored dissolved organic matter. Thus, the remote sensing of these waters is more challenging.Different types of waters need different approaches to obtain correct water quality products;therefore, the first step in remote sensing of lakes should be the classification of the water types. Theclassification of optical water types (OWTs) is based on the differences in the reflectance spectra ofthe lake water. This classification groups lake and coastal waters into five optical classes: Clear,Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes:Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of thisstudy was a comparison of two different Copernicus optical instrument data for opticalclassification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and MultispectralInstrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes werecomparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs ofthe study lakes during 2017. The comparison between two satellites was carried out to understandif the classification of the OWTs with both satellites is compatible. Our results could give us not onlya better overview of the changes in the lake water by studying the temporal and spatial variabilityof the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guidedapproach.

scholarly journals Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1469 ◽  
Author(s):  
Marcela Pereira-Sandoval ◽  
Ana Ruescas ◽  
Patricia Urrego ◽  
Antonio Ruiz-Verdú ◽  
Jesús Delegido ◽  
...  
Keyword(s):  
Near Infrared ◽  
Atmospheric Correction ◽  
Inland Waters ◽  
Atmospheric Effects ◽  
Image Correction ◽  
Lakes And Reservoirs ◽  
Dark Pixel ◽  
Organic Particles ◽  
Sentinel 2

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

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