scholarly journals Retrieval and Validation of Water Turbidity at Metre-Scale Using Pléiades Satellite Data: A Case Study in the Gironde Estuary

2020 ◽  
Vol 12 (6) ◽  
pp. 946 ◽  
Author(s):  
Yafei Luo ◽  
David Doxaran ◽  
Quinten Vanhellemont
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Near Infrared ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Quality Parameters ◽  
Satellite Constellations ◽  
Water Turbidity ◽  
Gironde Estuary ◽  
The Difference

This study investigated the use of frequent metre-scale resolution Pléiades satellite imagery to monitor water quality parameters in the highly turbid Gironde Estuary (GE, SW France). Pléiades satellite data were processed and analyzed in two representative test sites of the GE: 1) the maximum turbidity zone and 2) the mouth of the estuary. The main objectives of this study were to: (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction developed by Vanhellemont and Ruddick (2018) applied to Pléiades satellite data recorded over the GE; (ii) highlight the benefits of frequent metre-scale Pléiades observations in highly turbid estuaries by comparing them to previously validated satellite observations made at medium (250/300 m for MODIS, MERIS, OLCI data) and high (20/30 m for SPOT, OLI and MSI data) spatial resolutions. The results show that the DSF allows for an accurate retrieval of water turbidity by inversion of the water reflectance in the near-infrared (NIR) and red wavebands. The difference between Pléiades-derived turbidity and field measurements was proven to be in the order of 10%. To evaluate the spatial variability of water turbidity at metre scale, Pléiades data at 2 m resolution were resampled to 20 m and 250 m to simulate typical coarser resolution sensors. On average, the derived spatial variability in the GE is lower than or equal to 10% and 26%, respectively, in 20-m and 250-m aggregated pixels. Pléiades products not only show, in great detail, the turbidity features in the estuary and river plume, they also allow to map the turbidity inside ports and capture the complex spatial variations of turbidity along the shores of the estuary. Furthermore, the daily acquisition capabilities may provide additional advantages over other satellite constellations when monitoring highly dynamic estuarine systems.

scholarly journals Geostatistical integration to improve representativeness of satellite precipitation estimates and field measurements

10.29007/glj1 ◽  
2019 ◽  
Author(s):  
Felipe-Omar Tapia-Silva
Keyword(s):  
Spatial Variability ◽  
Satellite Data ◽  
Field Measurements ◽  
Auxiliary Variable ◽  
Measured Parameter ◽  
Geostatistical Methods ◽  
Variability Pattern ◽  
Distance Weighted ◽  
Precipitation Estimates ◽  
Leave One Out

Since the network of rainfall gauges and ground radars is generally not dense enough, satellite data have been used to estimate Precipitation (P). These data have the ability to capture the spatial variability pattern of the parameter, but are often inaccurate in relation to the value of the field measured parameter. Therefore, geostatistical methods were evaluated to improve the spatial representativeness of field measurements (FM) and satellite estimates. The work has been made for a hydrological sub region in the Mexican tropic. The geostatistical methods used to interpolate P-FM were ordinary kriging (KO), universal kriging (KU) and regression kriging (RK) as well as the Inverse Distance Weighted (IDW) mechanical interpolator for comparison purposes. Furthermore, the values at the pixel centers of the Tropical Rainfall Monitoring Mission (TRMM) images were interpolated using OK and evaluated using leave-one-out cross validation (LOO-CV). The best LOO-CV evaluated method consisted of the RK interpolation of the point FM taking as auxiliary variable the OK interpolation of the TRMM cell centers. It is concluded that the geostatistical integration between rainfall estimates from satellite data and FM data is promising because satellite information has the ability to capture spatial variability and the point FM add accuracy to the results. These characteristics combined can produce a P product useful for modeling activities and environmental management.

scholarly journals Atmospheric Correction for Tower-Based Solar-Induced Chlorophyll Fluorescence Observations at O2-A Band

2019 ◽  
Vol 11 (3) ◽  
pp. 355 ◽  
Author(s):  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
Liangyun Liu
Keyword(s):  
Chlorophyll Fluorescence ◽  
Atmospheric Correction ◽  
Field Measurements ◽  
Correction Method ◽  
Oxygen Absorption ◽  
Atmospheric Transmission ◽  
Absorption Bands ◽  
Transfer Path ◽  
The Difference ◽  
Atmospheric Transmittance

Solar-induced chlorophyll fluorescence (SIF) has been proven to be an efficient indicator of vegetation photosynthesis. To investigate the relationship between SIF and Gross Primary Productivity (GPP), tower-based continuous spectral observations coordinated with eddy covariance (EC) measurements are needed. As the strong absorption effect at the O2-A absorption bands has an obvious influence on SIF retrieval based on the Fraunhofer Line Discrimination (FLD) principle, atmospheric correction is required even for tower-based SIF observations made with a sensor tens of meters above the canopy. In this study, an operational and simple solution for atmospheric correction of tower-based SIF observations at the O2-A band is proposed. The aerosol optical depth (AOD) and radiative transfer path length (RTPL) are found to be the dominant factors influencing the upward and downward transmittances at the oxygen absorption band. Look-up tables (LUTs) are established to estimate the atmosphere transmittance using AOD and RTPL based on the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) model simulations, and the AOD is estimated using the ratio of the downwelling irradiance at 790 nm to that at 660 nm (E790/E660). The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL based on an empirical equation. A series of field measurements were carried out to evaluate the performance of the atmospheric correction method for tower-based SIF observations. The difference between the SIF retrieved from tower-based and from ground-based observations decreased obviously after the atmospheric correction. The results indicate that the atmospheric correction method based on a LUT is efficient and also necessary for more accurate tower-based SIF retrieval, especially at the O2-A band.

scholarly journals Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing

2021 ◽  
Author(s):  
Edward Hamilton Bair ◽  
Jeff Dozier ◽  
Charles Stern ◽  
Adam LeWinter ◽  
Karl Rittger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Topography ◽  
Near Infrared ◽  
Laser Scanner ◽  
Field Measurements ◽  
Spectral Unmixing ◽  
Snow Albedo ◽  
The Difference ◽  
Multispectral Sensors

Abstract. Intrinsic albedo is the bihemispherical reflectance of a substance with a smooth surface. Conversely, the apparent albedo is the bihemispherical reflectance of the same substance with a rough surface. For snow, the surface is often rough, and these two optical quantities have different uses: intrinsic albedo is used in scattering equations whereas apparent albedo should be used in energy balance models. Complementing numerous studies devoted to surface roughness and its effect on snow reflectance, this work analyzes a timeseries of intrinsic and apparent snow albedos over a season at a sub-alpine site using an automated terrestrial laser scanner to map the snow surface topography. An updated albedo model accounts for shade, and in situ albedo measurements from a field spectrometer are compared to those from a spaceborne multispectral sensor. A spectral unmixing approach using a shade endmember (to address the common problem of unknown surface topography) produces grain size and impurity solutions; the modeled shade fraction is compared to the intrinsic and apparent albedo difference. As expected and consistent with other studies, the results show that intrinsic albedo is consistently greater than apparent albedo. Both albedos decrease rapidly as ablation hollows form during melt, combining effects of impurities on the surface and increasing roughness. Intrinsic broadband albedos average 7 % greater than apparent albedos, with the difference being about 6 % in the near-infrared or 3–4 % if the average (planar) topography is known and corrected. Field measurements of spectral surface reflectance confirm that multispectral sensors see the apparent albedo but lack the spectral resolution to distinguish between darkening from ablation hollows versus low concentrations of impurities. In contrast, measurements from the field spectrometer have sufficient resolution to discern darkening from the two sources. Based on these results, conclusions are: 1) impurity estimates from multispectral sensors are only reliable for relatively dirty snow with high snow fraction; 2) a shade endmember must be used in spectral mixture models, even for in situ spectroscopic measurements; and 3) snow albedo models should produce apparent albedos by accounting for the shade fraction. The conclusion re-iterates that albedo is the most practical snow reflectance quantity for remote sensing.

Drone image processing for water quality in the cloud

2021 ◽  
Author(s):  
Els Knaeps ◽  
Robrecht Moelans ◽  
Liesbeth De Keukelaere
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quantitative Information ◽  
Short Wave ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Radiometric Calibration ◽  
Water Turbidity ◽  
Marine Litter ◽  
Infrared Wavelength

<p>The use of drones to monitor water quality is relatively new. Although drones and lightweight cameras are readily available, deriving water quality parameters is not so straightforward.  It requires knowledge of the water optical properties, the atmospheric contribution and special approaches for georeferencing of the drone images.  We present a cloud-based environment, MAPEO-water, to deal with the complexity of water surfaces and retrieve quantitative information on the water turbidity, the chlorophyll content and the presence of marine litter/marine plastics. </p><p>MAPEO-water supports already a number of camera types and allows the drone operator to upload the images in the cloud. MAPEO-water also offers a protocol to perform the drone flights and allow efficient processing of the images. Processing of the drone images includes direct georeferencing, radiometric calibration and removal of the atmospheric contribution. Final water quality parameters can be downloaded through the same cloud platform. Water turbidity and chlorophyll retrieval are based on spectral approaches utilizing information in the visible and Near Infrared wavelength ranges. Marine litter detection combines spectral approaches and Artificial Intelligence. Visible, Near Infrared and Short Wave Infrared wavelengths are used to detect marine litter but also discriminate marine litter from turbid water plumes and surface features such as glint and white caps. First tests have also been performed to apply a Convolutional Neural Network (CNN) for the automatic recognition of the marine plastic litter.</p>

scholarly journals VISIBLE, VERY NEAR IR AND SHORT WAVE IR HYPERSPECTRAL DRONE IMAGING SYSTEM FOR AGRICULTURE AND NATURAL WATER APPLICATIONS

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 165-170 ◽  
Author(s):  
H. Saari ◽  
A. Akujärvi ◽  
C. Holmlund ◽  
H. Ojanen ◽  
J. Kaivosoja ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Accurate Determination ◽  
Field Measurements ◽  
Short Wave ◽  
Quality Parameters ◽  
Grass Species ◽  
Spectral Bands ◽  
Sensing Technology

The accurate determination of the quality parameters of crops requires a spectral range from 400&amp;thinsp;nm to 2500&amp;thinsp;nm (Kawamura et al., 2010, Thenkabail et al., 2002). Presently the hyperspectral imaging systems that cover this wavelength range consist of several separate hyperspectral imagers and the system weight is from 5 to 15&amp;thinsp;kg. In addition the cost of the Short Wave Infrared (SWIR) cameras is high (~&amp;thinsp;50&amp;thinsp;k€). VTT has previously developed compact hyperspectral imagers for drones and Cubesats for Visible and Very near Infrared (VNIR) spectral ranges (Saari et al., 2013, Mannila et al., 2013, Näsilä et al., 2016). Recently VTT has started to develop a hyperspectral imaging system that will enable imaging simultaneously in the Visible, VNIR, and SWIR spectral bands. The system can be operated from a drone, on a camera stand, or attached to a tractor. The targeted main applications of the DroneKnowledge hyperspectral system are grass, peas, and cereals. In this paper the characteristics of the built system are shortly described. The system was used for spectral measurements of wheat, several grass species and pea plants fixed to the camera mount in the test fields in Southern Finland and in the green house. The wheat, grass and pea field measurements were also carried out using the system mounted on the tractor. The work is part of the Finnish nationally funded <q>DroneKnowledge – Towards knowledge based export of small UAS remote sensing technology</q> project.

scholarly journals Underwater Holographic Sensor for Plankton Studies In Situ Including Accompanying Measurements

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4863
Author(s):  
Victor Dyomin ◽  
Alexandra Davydova ◽  
Igor Polovtsev ◽  
Alexey Olshukov ◽  
Nikolay Kirillov ◽  
...  
Keyword(s):  
World Ocean ◽  
Field Tests ◽  
Field Measurements ◽  
Measuring Systems ◽  
Water Turbidity ◽  
Background Data ◽  
Average Size ◽  
Size Dispersion

The paper presents an underwater holographic sensor to study marine particles—a miniDHC digital holographic camera, which may be used as part of a hydrobiological probe for accompanying (background) measurements. The results of field measurements of plankton are given and interpreted, their verification is performed. Errors of measurements and classification of plankton particles are estimated. MiniDHC allows measurement of the following set of background data, which is confirmed by field tests: plankton concentration, average size and size dispersion of individuals, particle size distribution, including on major taxa, as well as water turbidity and suspension statistics. Version of constructing measuring systems based on modern carriers of operational oceanography for the purpose of ecological diagnostics of the world ocean using autochthonous plankton are discussed. The results of field measurements of plankton using miniDHC as part of a hydrobiological probe are presented and interpreted, and their verification is carried out. The results of comparing the data on the concentration of individual taxa obtained using miniDHC with the data obtained by the traditional method using plankton catching with a net showed a difference of no more than 23%. The article also contains recommendations for expanding the potential of miniDHC, its purpose indicators, and improving metrological characteristics.

scholarly journals Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

2020 ◽  
Vol 12 (12) ◽  
pp. 5050
Author(s):  
Katarzyna Szwedziak ◽  
Ewa Polańczyk ◽  
Żaneta Grzywacz ◽  
Gniewko Niedbała ◽  
Wiktoria Wojtkiewicz
Keyword(s):  
Total Protein ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Quality Parameters ◽  
Neural Modeling ◽  
Infrared Spectrometry ◽  
Stored Grain ◽  
Stored Wheat

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 ± 0.15), water (10.63 ± 0.16) or gluten (30.56 ± 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain.

Simultaneous Determination of the Main Constituents of Viscose Spinning Solutions by Visible near Infrared Spectroscopy

1997 ◽  
Vol 5 (3) ◽  
pp. 135-148 ◽  
Author(s):  
S. Sollinger ◽  
M. Voges
Keyword(s):  
Near Infrared ◽  
Quality Parameters ◽  
Spectroscopic Technique ◽  
Cellulose Content ◽  
Careful Monitoring ◽  
Cellulose Fibres ◽  
Chemical Determination ◽  
Analytical Procedures ◽  
Product Control

The production of cellulose fibres by wet fibres spinning requires a careful monitoring of the spinning bath and especially the spinning solution in terms of product control and for quality assurance purposes. The chemical composition as well as the ripening of the spinning solution are of major importance for maintaining a constant product quality. The conventional determination of the quality parameters of the viscose spinning solution are rather time- and labour-intensive due to the fact that several independent analytical procedures are involved which score low in time efficiency themselves. Briefly, the complete characterisation of the spinning solution requires an iodometric titration of the cellulose xanthogenate (γ-number), an acidimetric titration of the sodium hydroxide (NaOH) content, a UV-VIS spectroscopic determination of the trithiocarbonate (TTC) content and a time-consuming gravimetric cellulose content determination. Sometimes, also, a colloid chemical determination of the degree of ripening (Hottenroth number) is performed in the plant control laboratory. With this work, an approach will be demonstrated, which enables the substitution of these numerous analytical procedures by a single and time-efficient method—a VIS-NIR spectroscopic technique. Therefore, it is possible to determine the parameters: NaOH, TTC, cellulose xanthogenate content and the cellulose content of the viscose spinning solution simultaneously with a reasonable precision within a few minutes.

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