scholarly journals An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

2020 ◽  
Vol 12 (24) ◽  
pp. 4123
Author(s):  
Michela Sammartino ◽  
Bruno Buongiorno Nardelli ◽  
Salvatore Marullo ◽  
Rosalia Santoleri
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Temperature Fields ◽  
Spatial And Temporal Variability ◽  
Dynamic Topography ◽  
In Situ Data ◽  
Artificial Neural ◽  
The Mediterranean ◽  
3D Fields

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

scholarly journals Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

2020 ◽  
Vol 32 ◽  
pp. 53-63
Author(s):  
Stefan Kazakov ◽  
Valko Biserkov ◽  
Luchezar Pehlivanov ◽  
Stoyan Nedkov
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Suspended Matter ◽  
Trophic Status ◽  
Remote Sensing Data ◽  
Water Quality Monitoring ◽  
Floodplain Lakes ◽  
In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

scholarly journals Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data

2021 ◽  
Vol 13 (1) ◽  
pp. 85-97
Author(s):  
Ioannis Moutzouris-Sidiris ◽  
Konstantinos Topouzelis
Keyword(s):  
Mediterranean Sea ◽  
Chlorophyll A ◽  
Time Window ◽  
Pearson Correlation ◽  
Neural Net ◽  
Data Set ◽  
Chl A ◽  
In Situ Data ◽  
The Mediterranean

Abstract The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated (r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN.

scholarly journals VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK

2010 ◽  
Vol 2 (-) ◽  
Author(s):  
TAKAHIRO OSAWA ◽  
CHAO FANG ZHAO ◽  
I WAYAN Nuarsa ◽  
I KETUT SWARDIKA ◽  
YASUHIRO SUGIMORI
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vertical Distribution ◽  
Primary Production ◽  
Chlorophyll A ◽  
Ocean Color ◽  
Chl A ◽  
In Situ Data ◽  
Artificial Neural

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space. Keywords : Ocean color, Chlorophyll-a (Chl-a), Vertical structure, Artificial Neural Networks (ANN).

scholarly journals Computation of a new Mean Dynamic Topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in-situ data

2014 ◽  
Vol 11 (1) ◽  
pp. 655-692 ◽  
Author(s):  
M.-H. Rio ◽  
A. Pascual ◽  
P.-M. Poulain ◽  
M. Menna ◽  
B. Barceló ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
External Validation ◽  
Dynamic Topography ◽  
Crucial Issue ◽  
Mean Dynamic Topography ◽  
In Situ Data ◽  
Velocity Variance ◽  
Sensitivity Studies ◽  
The Mediterranean

Abstract. The accurate knowledge of the ocean Mean Dynamic Topography (MDT) is a crucial issue for a number of oceanographic applications and in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new Mean Dynamic Topography (MDT) that was computed for the Mediterranean Sea. It takes profit of improvements made possible by the use of extended datasets and refined processing. The updated dataset spans the 1993–2012 period and consists of: drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This has required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous version, SMDT-MED-2014 features shorter scales structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP

Irriga ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 585-598
Author(s):  
Pedro Henrique Jandreice Magnoni ◽  
Cesar De Oliveira Ferreira Silva ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Remote Sensing ◽  
Water Budget ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
In Situ Data ◽  
Joint Application ◽  
E Mail ◽  
Sentinel 2

SENSORIAMENTO REMOTO APLICADO AO MANEJO DA IRRIGAÇÃO EM ÁREAS COM ESCASSEZ DE DADOS: ESTUDO DE CASO EM PIVÔ CENTRAL EM ITATINGA-SP*     PEDRO HENRIQUE JANDREICE MAGNONI1; CÉSAR DE OLIVEIRA FERREIRA SILVA1 E RODRIGO LILLA MANZIONE2   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista", Avenida Universitária, n° 3780, Altos do Paraíso, 18610-034, Botucatu, São Paulo, Brasil,  [email protected]; [email protected]. 2 Departamento de Engenharia de Biossistemas, Faculdade de Ciências e Engenharia, Universidade Estadual Paulista “Júlio de Mesquita Filho”, Rua Domingos da Costa Lopes, 780, CEP 17602496, Tupã – SP, Brasil. E-mail: [email protected]. *Este artigo é proveniente das dissertações de mestrado dos dois primeiros autores.     1 RESUMO   Ferramentas baseadas em sensoriamento remoto possibilitam o monitoramento do balanço hídrico da água em diferentes resoluções espaciais e temporais. Ainda assim, modelos que exigem dados in-situ impossibilitam sua aplicação em áreas com escassez de dados. No sentido de lidar com esse desafio, o presente trabalho apresenta uma abordagem de escolha do momento de irrigar, pelo balanço hídrico da água no solo, baseada em estimativa da evapotranspiração real (ETA) obtida com o uso conjunto de imagens multiespectrais do sensor MSI/SENTINEL-2 e dados de uma estação meteorológica pública. A área de estudo foi um pivô central localizado no munícipio de Itatinga-SP. Para a tomada de decisão do momento de irrigar, com base em um manejo por lâmina de irrigação fixa, foi feita a interpolação da fração evapotranspirativa entre os dias com imagens disponíveis para obter a ETA nos dias sem imagens por meio do seu produto com a evapotranspiração de referência. Essa abordagem captou variações climáticas essenciais para a estimativa do balanço hídrico em dias sem imagem. Destaca-se nessa aplicação conjunta sua capacidade de ser realizada sem necessitar de parâmetros específicos da cultura, do microclima ou do relevo, tornando-se interessante para regiões com escassez de dados.   Palavras-chave:  evapotranspiração, momento de irrigar, agriwater.     MAGNONI, P. H. J.; SILVA, C. O. F.; MANZIONE, R. L. REMOTE SENSING APPLIED TO IRRIGATION MANAGEMENT IN AREAS WITH LACK OF DATA: A CASE STUDY IN A CENTRAL PIVOT IN ITATINGA-SP     2 ABSTRACT   Remote sensing-based tools allow the monitoring of water budgets over different spatial and temporal resolutions. Nevertheless, some models require in situ data, preventing their application in areas with a lack of data. To address this challenge, this work presents an approach for irrigation scheduling, based on soil water budget estimation using actual evapotranspiration (ETA) obtained using MSI/SENTINEL-2 multispectral images and data from a public meteorological station. The study area consisted of a central pivot located in the municipality of Itatinga-SP, Brazil. For decision-making of irrigation scheduling, considering a fixed irrigation rate, the evapotranspiration fraction was interpolated between the days with available images to obtain the ETA on the days without images using its product with the reference evapotranspiration. This approach captured essential climate variations for estimating the water budget on non-image days. Noteworthy in this joint application is its suitability to be performed not requiring crop-, microclimate- or relief-specific parameters, making it useful for regions with a lack of data.   Keywords: evapotranspiration, irrigation scheduling, agriwater.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

scholarly journals Validation of aerosol backscatter profiles from Raman lidar and ceilometer using balloon-borne measurements

2021 ◽  
Vol 21 (3) ◽  
pp. 2267-2285
Author(s):  
Simone Brunamonti ◽  
Giovanni Martucci ◽  
Gonzague Romanens ◽  
Yann Poltera ◽  
Frank G. Wienhold ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Measurements ◽  
Aerosol Size Distribution ◽  
Spatial And Temporal Variability ◽  
Raman Lidar ◽  
Backscatter Coefficient ◽  
Custom Made ◽  
Aerosol Backscatter ◽  
Good Agreement

Abstract. Remote-sensing measurements by light detection and ranging (lidar) instruments are fundamental for the monitoring of altitude-resolved aerosol optical properties. Here we validate vertical profiles of aerosol backscatter coefficient (βaer) measured by two independent lidar systems using co-located balloon-borne measurements performed by Compact Optical Backscatter Aerosol Detector (COBALD) sondes. COBALD provides high-precision in situ measurements of βaer at two wavelengths (455 and 940 nm). The two analyzed lidar systems are the research Raman Lidar for Meteorological Observations (RALMO) and the commercial CHM15K ceilometer (Lufft, Germany). We consider in total 17 RALMO and 31 CHM15K profiles, co-located with simultaneous COBALD soundings performed throughout the years 2014–2019 at the MeteoSwiss observatory of Payerne (Switzerland). The RALMO (355 nm) and CHM15K (1064 nm) measurements are converted to 455 and 940 nm, respectively, using the Ångström exponent profiles retrieved from COBALD data. To account for the different receiver field-of-view (FOV) angles between the two lidars (0.01–0.02∘) and COBALD (6∘), we derive a custom-made correction using Mie-theory scattering simulations. Our analysis shows that both lidar instruments achieve on average a good agreement with COBALD measurements in the boundary layer and free troposphere, up to 6 km altitude. For medium-high-aerosol-content measurements at altitudes below 3 km, the mean ± standard deviation difference in βaer calculated from all considered soundings is −2 % ± 37 % (−0.018 ± 0.237 Mm−1 sr−1 at 455 nm) for RALMO−COBALD and +5 % ± 43 % (+0.009 ± 0.185 Mm−1 sr−1 at 940 mm) for CHM15K−COBALD. Above 3 km altitude, absolute deviations generally decrease, while relative deviations increase due to the prevalence of air masses with low aerosol content. Uncertainties related to the FOV correction and spatial- and temporal-variability effects (associated with the balloon's drift with altitude and different integration times) contribute to the large standard deviations observed at low altitudes. The lack of information on the aerosol size distribution and the high atmospheric variability prevent an accurate quantification of these effects. Nevertheless, the excellent agreement observed in individual profiles, including fine and complex structures in the βaer vertical distribution, shows that under optimal conditions, the discrepancies with the in situ measurements are typically comparable to the estimated statistical uncertainties in the remote-sensing measurements. Therefore, we conclude that βaer profiles measured by the RALMO and CHM15K lidar systems are in good agreement with in situ measurements by COBALD sondes up to 6 km altitude.

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