scholarly journals Measurements and significance of bio-optical parameters for remote sensing in two subalpine lakes of different trophic state

1994 ◽  
Vol 56 (3) ◽  
pp. 263-303 ◽  
Author(s):  
J. M. Jaquet ◽  
F. Schanz ◽  
P. Bossard ◽  
K. Hanselmann ◽  
F. Gendre
Keyword(s):  
Remote Sensing ◽  
Trophic State ◽  
Optical Parameters ◽  
Subalpine Lakes

scholarly journals A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

2021 ◽  
Vol 13 (13) ◽  
pp. 2498
Author(s):  
Shijie Zhu ◽  
Jingqiao Mao
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Water Temperature ◽  
Trophic State ◽  
Trophic Levels ◽  
Water Bodies ◽  
Trophic State Index ◽  
Urban Water ◽  
Estimation Model ◽  
Input Variables

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.

scholarly journals Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes

2019 ◽  
Vol 11 (2) ◽  
pp. 184 ◽  
Author(s):  
Kun Xue ◽  
Ronghua Ma ◽  
Dian Wang ◽  
Ming Shen
Keyword(s):  
Remote Sensing ◽  
Absorption Coefficient ◽  
Lake Taihu ◽  
Optical Parameters ◽  
Water Type ◽  
Water Types ◽  
Remote Sensing Reflectance ◽  
Inorganic Matter ◽  
Suspended Particulate ◽  
Huai River

Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters.

scholarly journals Spectral characterization of dissolved organic matter along trophic gradients: Potential indicators of eutrophication of plateau lakes in Southwest China

2021 ◽  
Author(s):  
Yuying Guan ◽  
Ruiming Han ◽  
Nannan Jia ◽  
Da Huo ◽  
Gongliang Yu
Keyword(s):  
Water Quality ◽  
Organic Matter ◽  
Optical Properties ◽  
Dissolved Organic Matter ◽  
Trophic State ◽  
Intensity Increase ◽  
Optical Parameters ◽  
Trophic State Index ◽  
Main Role ◽  
Neural Network Prediction

Abstract Dissolved organic matter (DOM) acts as a chemical intermediary between terrestrial and lacustrine ecosystems and significantly affects the structure and function of lakes. The optical characteristics of DOM have been widely used to estimate the water quality. However, little is known about its absorption and fluorescence under different trophic states. Especially, comparative research is needed among gradient eutrophic level of plateau lakes when considering their special characteristics. A total of 119 water samples were collected in the Erhai watershed from November 2018 to July 2019 to investigate the optical properties of DOM depending on the trophic state using ultraviolet–visible spectroscopy and parallel factor analysis of the excitation–emission matrix. The water quality conditions in the Erhai watershed were classified using the trophic state index (TSI; 31 < TSI < 67). The DOM is largely autochthonous and includes tyrosine-like protein (C1), tryptophan-like protein (C2), and humic-like compounds (C3). Except for an apparent trend of decreasing slope ratio (SR) (p < 0.01), both absorption coefficient at 254 nm and fluorescence intensity increase with the rising trophic state (p < 0.01). In this study, new models (R2aCDOM(254) = 0.762; R2 Fn(355) = 0.705, p < 0.01) basing on significant correlations between the TSI and aCDOM(254) and Fn(355) were established to predict the trophic state. The results of this study demonstrate that the effects of nutrients and environmental factors (pH and water temperature) on DOM vary depending on the trophic state and that the pH plays the main role in DOM production. Our analyses highlight the importance of DOM in aquatic ecosystems and the correlation between TSI and the optical properties of DOM. Our research unmasks the strong linkage between optical parameters of DOM and freshwater quality by applying neural network prediction.

scholarly journals Red Algae Bloom Detection in Occhito Lake Combining MERIS and MODIS Data

2017 ◽  
Author(s):  
Raffaella Matarrese ◽  
Nicolas Guyennon ◽  
Diego Copetti
Keyword(s):  
Remote Sensing ◽  
Ad Hoc ◽  
High Accuracy ◽  
Optical Parameters ◽  
Surface Reflectance ◽  
Algae Bloom ◽  
Modis Data ◽  
South Italy ◽  
Spectral Behaviour ◽  
Remote Sensing Techniques

In winter 2008-2009, Lake Occhito, a strategic multiple-uses reservoir in South Italy, was affected by an extraordinary Planktothrix rubescens bloom. P. rubescens is a filamentous potentially toxic cyanobacterium which has recently colonized many environments in Europe. A number of studies is currently available on the use of remote sensing techniques to monitor different fresh water cyanobacteria species. By contrast no specific applications are available on the remote sensing monitoring of P. rubescens. In this paper we present a specific algorithm, based on Water Leaving Reflectances (WLR) from MERIS data, atmospherically corrected using the Aerosol Optical Thickness (AOT) retrieved by MODIS data, to detect P. rubescens blooms. The high accuracy in AOT data, provided by MOD09 surface reflectance product, at 1km spatial resolution, allowed obtaining a good correlation between the WLR and the P. rubescens chlorophyll-a concentrations measured in the field, through multiple stations fluorometric profiles. A modified Normalized Difference Chlorophyll index (NDCI) algorithm is presented. The performance of the proposed algorithm has been successfully compared with other specific algorithms for turbid productive waters. We demonstrated how important is to verify the spectral behaviour of bio-optical parameters in order to develop an ad hoc algorithm that better performs with respect to standard algorithms.

scholarly journals Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing

2021 ◽  
Vol 21 (23) ◽  
pp. 17969-17994
Author(s):  
Martin Radenz ◽  
Johannes Bühl ◽  
Patric Seifert ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Gravity Wave ◽  
Detection Threshold ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Ice Formation ◽  
Absolute Difference ◽  
Optical Parameters ◽  
Mixed Phase Clouds

Abstract. Multi-year ground-based remote-sensing datasets were acquired with the Leipzig Aerosol and Cloud Remote Observations System (LACROS) at three sites. A highly polluted central European site (Leipzig, Germany), a polluted and strongly dust-influenced eastern Mediterranean site (Limassol, Cyprus), and a clean marine site in the southern midlatitudes (Punta Arenas, Chile) are used to contrast ice formation in shallow stratiform liquid clouds. These unique, long-term datasets in key regions of aerosol–cloud interaction provide a deeper insight into cloud microphysics. The influence of temperature, aerosol load, boundary layer coupling, and gravity wave motion on ice formation is investigated. With respect to previous studies of regional contrasts in the properties of mixed-phase clouds, our study contributes the following new aspects: (1) sampling aerosol optical parameters as a function of temperature, the average backscatter coefficient at supercooled conditions is within a factor of 3 at all three sites. (2) Ice formation was found to be more frequent for cloud layers with cloud top temperatures above -15∘C than indicated by prior lidar-only studies at all sites. A virtual lidar detection threshold of ice water content (IWC) needs to be considered in order to bring radar–lidar-based studies in agreement with lidar-only studies. (3) At similar temperatures, cloud layers which are coupled to the aerosol-laden boundary layer show more intense ice formation than decoupled clouds. (4) Liquid layers formed by gravity waves were found to bias the phase occurrence statistics below -15∘C. By applying a novel gravity wave detection approach using vertical velocity observations within the liquid-dominated cloud top, wave clouds can be classified and excluded from the statistics. After considering boundary layer and gravity wave influences, Punta Arenas shows lower fractions of ice-containing clouds by 0.1 to 0.4 absolute difference at temperatures between −24 and -8∘C. These differences are potentially caused by the contrast in the ice-nucleating particle (INP) reservoir between the different sites.

scholarly journals Proposal for a remote sensing trophic state index based upon Thematic Mapper/Landsat images

2013 ◽  
Vol 8 (3) ◽  
Author(s):  
Luciana De Resende Londe ◽  
Evlyn Márcia Leão de Moraes Novo ◽  
Claudio Clemente Faria Barbosa ◽  
Carlos Alberto Sampaio de Araujo ◽  
Camilo Daleles Rennó
Keyword(s):  
Remote Sensing ◽  
Trophic State ◽  
Thematic Mapper ◽  
Trophic State Index ◽  
Landsat Images ◽  
State Index

Improved estimates of bio-optical parameters in optically complex water using hyperspectral remote sensing data

2021 ◽  
Vol 42 (8) ◽  
pp. 3056-3073
Author(s):  
Syed Moosa Ali ◽  
Anurag Gupta ◽  
Mini Raman ◽  
Arvind Sahay ◽  
Gunjan Motwani ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Optical Parameters ◽  
Sensing Data

scholarly journals Determinação da Concentração de Clorofila-a por Sensoriamento Remoto no Reservatório de Chapéu d’Úvas (Mg), Brasil

2021 ◽  
Vol 14 (6) ◽  
pp. 3561
Author(s):  
Larissa Ferreira Serbeto ◽  
George Mendes ◽  
Celso Bandeira de Melo Ribeiro ◽  
Renata De Oliveira Pereira
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Trophic State ◽  
The Body ◽  
Component Concentration ◽  
Public Water Supply ◽  
Trophic State Index ◽  
Chlorophyll A Concentration ◽  
Sentinel 2

Na atualidade, um grande impacto nos reservatórios de água doce é a eutrofização, que afeta diretamente o tratamento e uso da água para abastecimento público, navegação, fauna e flora aquática e impacto visual. A clorofila-a é um dos indicadores de estado trófico da água e pode ser determinada utilizando sensoriamento remoto. Desta forma, este estudo objetivou determinar a concentração de clorofila-a na barragem de Chapéu d’Uvas em Juiz de Fora (Brasil), um dos principais mananciais de abastecimento público da cidade. Através de um modelo utilizando imagens do satélite Sentinel-2 foi avaliado o comportamento espaço-temporal da concentração do componente, foi correlacionado com dados de pluviosidade, temperatura, evaporação e uso e ocupação do solo em torno da barragem. Também foi aplicado um modelo para determinar o índice de estado trófico, que apresenta o grau de trofia que o corpo hídrico se encontra, classificando Chapéu d’Uvas como estado mesotrófico. Os resultados se mostraram satisfatórios para a espacialização e análise temporal da concentração de clorofila-a, a correlação com os dados de evaporação nos permitiu observar uma compatibilidade direta com a concentração do componente e verificou-se valores de clorofila-a acima da média do reservatório nas regiões de entradas de água pelos riachos e próximos as margens com menos cobertura de vegetação.Determination of Chlorophyll-a Concentration from Remote Sensing in Chapéu d’Úvas Reservoir (State of Minas Gerais, Brazil) A B S T R A C TCurrently, one of the great impacts on the freshwater reservoirs is eutrophication, which directly affects the treatment and use of water for public water supply, navigation, aquatic fauna and flora and visual impact. Chlorophyll-a is one of the water trophic state indicators and it can be determined using remote sensing. Thus, this study aimed to determine chlorophyll-a concentration in Chapéu d’Uvas dam, in Juiz de Fora (Brazil), one of the main water sources of public water supply for the city. From a model that uses Sentinel-2 satellite images, the spatial-temporal behavior of that component concentration was evaluated and correlated with data regarding rainfall, temperature, evaporation, and soil use and occupation around the dam. A model was also applied to determine trophic state index, which presents the body of water trophic state, classifying Chapéu d’Uvas as mesotrophic state. The results were satisfactory regarding spatialization and temporal analysis of chlorophyll-a concentration. The correlation with evaporation data permitted us to observe a direct correspondence with the component concentration. Chlorophyll-a values higher than the reservoir average were found in creek inlets and near the shore with lower vegetal cover.Keywords: eutrophication, Sentinel-2, quality of water, inland waters, trophic state

scholarly journals Satellite retrieval of aerosol microphysical and optical parameters using neural networks: a new methodology applied to the Sahara desert dust peak

2013 ◽  
Vol 6 (6) ◽  
pp. 10955-11010
Author(s):  
M. Taylor ◽  
S. Kazadzis ◽  
A. Tsekeri ◽  
A. Gkikas ◽  
V. Amiridis
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Refractive Index ◽  
Size Distribution ◽  
Aerosol Optical Depth ◽  
Principal Components ◽  
Complex Refractive Index ◽  
Remote Sensing Data ◽  
Optical Parameters ◽  
Desert Dust

Abstract. In order to exploit the full-Earth viewing potential of satellite instruments to globally characterise aerosols, new algorithms are required to deduce key microphysical parameters like the particle size distribution and optical parameters associated with scattering and absorption from space remote sensing data. Here, a methodology based on neural networks is developed to retrieve such parameters from satellite inputs and to validate them with ground-based remote sensing data. For key combinations of input variables available from MODIS and OMI Level 3 datasets, a grid of 100 feed-forward neural network architectures is produced, each having a different number of neurons and training proportion. The networks are trained with principal components accounting for 98% of the variance of the inputs together with principal components formed from 38 AERONET Level 2.0 (Version 2) retrieved parameters as outputs. Daily-averaged, co-located and synchronous data drawn from a cluster of AERONET sites centred on the peak of dust extinction in Northern Africa is used for network training and validation, and the optimal network architecture for each input parameter combination is identified with reference to the lowest mean squared error. The trained networks are then fed with unseen data at the coastal dust site Dakar to test their simulation performance. A NN, trained with co-located and synchronous satellite inputs comprising three aerosol optical depth measurements at 470, 500 and 660 nm, plus the columnar water vapour (from MODIS) and the modelled absorption aerosol optical depth at 500 nm (from OMI), was able to simultaneously retrieve the daily-averaged size distribution, the coarse mode volume, the imaginary part of the complex refractive index, and the spectral single scattering albedo – with moderate precision: correlation coefficients in the range 0.368 ≤ R ≤ 0.514. The network failed to recover the spectral behaviour of the real part of the complex refractive index with only 39–45% of the data falling within the acceptable level of uncertainty relative to ground-truth data at the daily timescale. In the context of Saharan desert dust, this new methodological approach appears to offer some potential for moderately accurate daily retrieval of previously inaccessible aerosol parameters from space.

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