In- Situ Optical measurements of sediment type and phytobenthos of tidal flats: A basis for imaging remote sensing spectroscopy

1997 ◽  
Vol 49 (2-3) ◽  
pp. 367-373 ◽  
Author(s):  
Hans Hakvoort ◽  
Kerstin Heymann ◽  
Christian Stein ◽  
Desmond Murphy
Keyword(s):  
Remote Sensing ◽  
Tidal Flats ◽  
Optical Measurements ◽  
Sediment Type

A basis for mapping the erodibility of tidal flats by optical remote sensing

1998 ◽  
Vol 49 (8) ◽  
pp. 867 ◽  
Author(s):  
J. H. M. Hakvoort ◽  
M. Heineke ◽  
K. Heymann ◽  
H. Kühl ◽  
R. Riethmüller ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shear Stress ◽  
Chlorophyll A ◽  
Fine Particles ◽  
Tidal Flats ◽  
Benthic Diatom ◽  
Reflectance Spectra ◽  
Sediment Type ◽  
Optical Remote Sensing ◽  
Chlorophyll A Concentration

A means of monitoring surface sediment stability of tidal flats with optical remote sensing has been developed. Erosion shear stress and corresponding bio-geo-chemical parameters of tidal flats were measured over five years in the Sylt/Rømø Bight, Germany. Ground-based optical reflectance spectra were measured during one year. A significant dependence of erosion shear stress on the benthic diatom chlorophyll a concentration in the uppermost 1-mm layer was found for muddy areas but decreased with decreasing proportion of fine particles (< 63 µm). With a low phytobenthic coverage there was a weak dependence of erosion shear stress on the proportion of fine particles. There were two main classes of the reflectance spectra: containing information on sediment type i.e. proportion of fine particles, and containing information on benthic diatoms and other phytobenthic species. There was a significant correlation between the reflectance spectra and proportion of fine particles and also between reflectance spectra and benthic diatom chlorophyll α concentration. Hence, the erodibility of tidal flats can be mapped by optical remote sensing when benthic chlorophyll a concentration and proportion of fine particles are used for estimation of the erosion shear stress.

scholarly journals HSRL-2 aerosol optical measurements and microphysical retrievals vs. airborne in situ measurements during DISCOVER-AQ 2013: an intercomparison study

2016 ◽  
Author(s):  
Patricia Sawamura ◽  
Richard H. Moore ◽  
Sharon P. Burton ◽  
Eduard Chemyakin ◽  
Detlef Müller ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Mie Theory ◽  
In Situ Measurements ◽  
Optical Measurements ◽  
High Spectral Resolution ◽  
Size Distributions ◽  
In Situ Data ◽  
Intercomparison Study

Abstract. Over 700 vertically-resolved retrievals of effective radii, number, volume, and surface-area concentrations of aerosols obtained from inversion of airborne multiwavelength High Spectral Resolution Lidar (HSRL-2) measurements are compared to vertically resolved airborne in situ measurements obtained during DISCOVER-AQ campaign from 2013 in California and Texas. In situ measurements of dry and humidified scattering, dry absorption, and dry size distributions are used to estimate hygroscopic adjustments which, in turn, are applied to the dry in situ measurements before comparison to HSRL-2 measurements and retrievals. The HSRL-2 retrievals of size parameters agree well with the in situ measurements once the hygroscopic adjustments are applied to the latter, with biases smaller than 25 % for surface-area concentrations, and smaller than 10 % for volume concentration. A closure study is performed by comparing the extinction and backscatter measured with the HSRL-2 with those calculated from the in situ size distributions and Mie theory, once refractive indices (at ambient RH) and hygroscopic adjustments are calculated and applied. The results of this closure study revealed discrepancies between the HSRL-2 optical measurements and those calculated from in situ measurements, in both California and Texas datasets, with the aerosol extinction and backscatter coefficients measured with the HSRL-2 being larger than those calculated from the adjusted in situ measurements and Mie theory. These discrepancies are further investigated and discussed in light of the many challenges often present in closure studies between in situ and remote sensing systems, such as: limitations in covering the same size range of particles with in situ and remote sensing instruments, as well as simplified parameterizations and assumptions used when dry in situ data are adjusted to account for aerosol hygroscopicity.

scholarly journals Optical measurements of atmospheric particles from airborne platforms: in situ and remote sensing instruments for balloons and aircrafts

2009 ◽  
Vol 49 (1) ◽  
Author(s):  
C. Buontempo ◽  
F. Cairo ◽  
G. Di Donfrancesco ◽  
R. Morbidini ◽  
M. Viterbini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Particles ◽  
Optical Measurements

scholarly journals Exploring the in situ high temperature emissivity of 2014–2015 Bardarbunga magmas

2021 ◽  
Author(s):  
Jonas Biren ◽  
Lionel Cosson ◽  
Leire del Campo ◽  
Cécile Genevois ◽  
Emmanuel Veron ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Risk Mitigation ◽  
Basaltic Magma ◽  
Spectral Emissivity ◽  
The Body ◽  
Lava Flows ◽  
Optical Measurements ◽  
Critical Parameters ◽  
Rock Composition

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Temperature is a key parameter controlling the rheology of lava flows. Unfortunately, the hazardous behavior of eruptions prevents direct measurements of hot magmatic bodies. Hence, the temperature of magma is mostly retrieved by using remote sensing methods (ground-based or satellite-based detectors) build on measuring the infrared (IR) radiance of the body [1]. These well-established techniques are however subjected to important errors related to, among others, the poor knowledge of the spectral emissivity (&amp;#949;), which is one of the most critical parameters in IR radiance measurement [2, 3]. In this study, we performed in situ optical measurements at relevant magmatic temperatures of basaltic magma from the 2014&amp;#8211;2015 Holuhraun eruption (Bardarbunga volcano, Iceland). Spectral emissivity has been systematically determined over a wide spectral range (400&amp;#8211;15000 cm&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;) covering TIR, MIR and SWIR regions, from room temperature up to 1473 K using a non-contact in situ IR emissivity apparatus [4]. SEM, EMPA, Raman spectroscopy, DSC, XRD and TEM techniques helped characterize and understand the complex radiative behavior of this natural magmatic composition. The results show not only that spectral emissivity varies accordingly with temperature and wavenumber but also that small changes in bulk rock composition or texture produce drastic changes in emissivity at given temperature, with iron content and its oxidation state being the main agents controlling this parameter. Appropriate emissivity values can then be used to refine current radiative data from IR remote sensing and to implement the thermo-rheological models of lava flows [5] as to support hazard assessment and risk mitigation.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;strong&gt;References:&lt;/strong&gt; [1] Kolzenburg et al. 2017. Bull. Volc. 79:45. [2] Harris, A. 2013: Cambridge University press. 728. [3] Rogic et al. 2019 Remote Sens., 11, 662 [4] De Sousa Meneses et al. 2015. Infrared Physics &amp; Technology 69. [5] Ramsey et al. 2019. Annals of Geophysics, 62, 2.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Spectral emissivity, temperature, IR spectroscopy, remote sensing, basalt&lt;/p&gt;

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data

2021 ◽  
pp. 105623
Author(s):  
Stefan Becker ◽  
Ramesh Prasad Sapkota ◽  
Binod Pokharel ◽  
Loknath Adhikari ◽  
Rudra Prasad Pokhrel ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Reanalysis Data ◽  
In Situ Measurements
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