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Keywords = LIF LiDAR

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12 pages, 4264 KiB  
Article
Postfilament-Induced Two-Photon Fluorescence of Dyed Liquid Aerosol Enhanced by Structured Femtosecond Laser Pulse
by Dmitry V. Apeksimov, Pavel A. Babushkin, Yury E. Geints, Andrey M. Kabanov, Elena E. Khoroshaeva, Victor K. Oshlakov, Alexey V. Petrov and Alexander A. Zemlyanov
Atmosphere 2024, 15(7), 813; https://doi.org/10.3390/atmos15070813 - 6 Jul 2024
Cited by 1 | Viewed by 1497
Abstract
Laser-induced fluorescence spectroscopy (LIFS) is actively used for remote sensing of atmospheric aerosols and is currently one of the most sensitive and selective techniques for determining small concentrations of substances inside particles. The use of high-power femtosecond laser sources for LIFS-based remote sensing [...] Read more.
Laser-induced fluorescence spectroscopy (LIFS) is actively used for remote sensing of atmospheric aerosols and is currently one of the most sensitive and selective techniques for determining small concentrations of substances inside particles. The use of high-power femtosecond laser sources for LIFS-based remote sensing of aerosols contributes to the development of new-generation fluorescence atmospheric lidars since it makes it possible to overcome the energy threshold for the nonlinear-optical effects of multiphoton absorption in particles and receive the emission signal at long distances in the atmosphere. Our study is aimed at the development and experimental demonstration of the technique of nonlinear laser-induced fluorescence spectroscopy (NLIFS) based on the remote excitation of aerosol fluorescent emission stimulated by a spatially structured high-power femtosecond laser pulse. Importantly, for the first time to our knowledge, we demonstrate the advances in using stochastically structured plasma-free intense light channels (postfilaments) specially formed by the propagation of femtosecond laser radiation through a turbulent air layer to improve NLIFS efficiency. A multiple increase in the received signal of two-photon-excited fluorescence of polydisperse-dyed aqueous aerosols by the structured postfilaments is reported. Full article
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22 pages, 9627 KiB  
Article
Regional Models for Sentinel-2/MSI Imagery of Chlorophyll a and TSS, Obtained for Oligotrophic Issyk-Kul Lake Using High-Resolution LIF LiDAR Data
by Vadim Pelevin, Ekaterina Koltsova, Aleksandr Molkov, Sergei Fedorov, Salmor Alymkulov, Boris Konovalov, Mairam Alymkulova and Kubanychbek Jumaliev
Remote Sens. 2023, 15(18), 4443; https://doi.org/10.3390/rs15184443 - 9 Sep 2023
Cited by 3 | Viewed by 1881
Abstract
The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must [...] Read more.
The development of regional satellite bio-optical models for natural waters with high temporal and spatial variability, such as inland seas, reservoirs, and coastal ocean waters, requires the implementation of an intermediate measuring link in the chain, “water sampling—bio-optical models”, and this link must have certain intermediate characteristics. The most crucial of them are the high-precision measurements of the main water quality parameters, such as the concentration of chlorophyll a (Chl a), colored dissolved organic matter (CDOM), and total suspended sediments (TSS) in the upper water layer, together with a high operational rate and the ability to cover a large water area in a short time, which corresponds to a satellite overpass. A possible solution is to utilize laser-induced fluorescence (LIF) of water constituents measured by a marine LiDAR in situ with a high sampling rate from a high-speed vessel. This allows obtaining a large ground-truth dataset of the main water quality parameters simultaneously with the satellite overpass within the time interval determined by NASA protocols. This method was successfully applied to the oligotrophic Issyk-Kul Lake in Kyrgyzstan, where we obtained more than 4000 and 1000 matchups for the Chl a and TSS, respectively. New preliminary regional bio-optical models were developed on the basis of a one-day survey and tested for archive Sentinel-2A data for 2022. This approach can be applied for regular monitoring and further correction in accordance with seasonal variability. The obtained results, together with previously published similar studies for eutrophic coastal and productive inland waters, emphasize the applicability of the presented method for the development or adjustment of regional bio-optical models for water bodies of a wide trophic range. Full article
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26 pages, 7672 KiB  
Article
Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters
by Aleksandr Molkov, Sergei Fedorov and Vadim Pelevin
Remote Sens. 2022, 14(15), 3663; https://doi.org/10.3390/rs14153663 - 30 Jul 2022
Cited by 10 | Viewed by 2548
Abstract
Atmospheric correction of remote sensing imagery over optically complex waters is still a challenging task. Even algorithms showing a good accuracy for moderate and extremely turbid waters need to be tested when being used for eutrophic inland basins. Such a test was carried [...] Read more.
Atmospheric correction of remote sensing imagery over optically complex waters is still a challenging task. Even algorithms showing a good accuracy for moderate and extremely turbid waters need to be tested when being used for eutrophic inland basins. Such a test was carried out in this study on the example of a Sentinel-3/OLCI image of the productive waters of the Gorky Reservoir during the period of intense blue-green algal bloom using data on the concentration of chlorophyll a and remote sensing reflectance measured from the motorboat at many points of the reservoir. The accuracy of four common atmospheric correction (AC) algorithms was examined. All of them showed unsatisfactory accuracy due to incorrect determination of atmospheric aerosol parameters and aerosol radiance. The calculated aerosol optical depth (AOD) spectra varied widely (AOD(865) = 0.005 − 0.692) even over a small area (up to 10 × 10 km) and correlated with the measured chlorophyll a. As a result, a part of the high water-leaving signal caused by phytoplankton bloom was taken as an atmosphere signal. A significant overestimation of atmospheric aerosol parameters, as a consequence, led to a strong underestimation of the remote sensing reflectance and low accuracy of the considered AC algorithms. To solve this problem, an algorithm with a fixed AOD was proposed. The fixed AOD spectrum was determined in the area with relatively “clean” water as 5 percentiles of AOD in all water pixels. The proposed algorithm made it possible to obtain the remote sensing reflectance with high accuracy. The slopes of linear regression are close to 1 and the intercepts tend to zero in almost all spectral bands. The determination coefficients are more than 0.9; the bias, mean absolute percentage error, and root-mean-square error are notably lower than for other AC algorithms. Full article
(This article belongs to the Special Issue Remote Sensing of Land Water Bodies)
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18 pages, 6333 KiB  
Article
Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR
by Xingmin Zhao, Shuo Shi, Jian Yang, Wei Gong, Jia Sun, Biwu Chen, Kuanghui Guo and Bowen Chen
Sensors 2020, 20(3), 935; https://doi.org/10.3390/s20030935 - 10 Feb 2020
Cited by 18 | Viewed by 4456
Abstract
Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced [...] Read more.
Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system’s performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring. Full article
(This article belongs to the Special Issue Imaging Sensors and Applications)
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29 pages, 8118 KiB  
Article
Regional Models for High-Resolution Retrieval of Chlorophyll a and TSM Concentrations in the Gorky Reservoir by Sentinel-2 Imagery
by Alexander A. Molkov, Sergei V. Fedorov, Vadim V. Pelevin and Elena N. Korchemkina
Remote Sens. 2019, 11(10), 1215; https://doi.org/10.3390/rs11101215 - 22 May 2019
Cited by 62 | Viewed by 6311
Abstract
The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During [...] Read more.
The possibilities of chlorophyll a (Chl a) and total suspended matter (TSM) retrieval using Sentinel-2/MSI imagery and in situ measurements in the Gorky Reservoir are investigated. This water body is an inland freshwater ecosystem within the territory of the Russian Federation. During the algal bloom period, the optical properties of water are extremely heterogeneous and vary on scales of tens of meters. Additionally, they vary in time under the influence of currents and wind forcing. In this case, the usage of the traditional station-based sampling to describe the state of the reservoir may be uninformative and not rational. Therefore, we proposed an original approach based on simultaneous in situ measurements of the remote sensing reflectance by a single radiometer and the concentration of water constituents by an ultraviolet fluorescence LiDAR from a high-speed gliding motorboat. This approach provided fast data collection including 4087 synchronized LiDAR and radiometric measurements with high spatial resolutions of 8 m for two hours. A part of the dataset was coincided with Sentinel-2 overpass and used for the development of regional algorithms for the retrieval of Chl a and TSM concentrations. For inland waters of the Russian Federation, such research was performed for the first time. The proposed algorithms can be used for regular environmental monitoring of the Gorky Reservoir using ship measurements or Sentinel-2 images. Additionally, they can be adapted for neighboring reservoirs, for example, for other seven reservoirs on the Volga River. Moreover, the proposed ship measurement approach can be useful in the practice of limnological monitoring of inland freshwater ecosystems with high spatiotemporal variability of the optical properties. Full article
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12 pages, 2697 KiB  
Article
Remote Detection of the Fluorescence Spectrum of Natural Pollens Floating in the Atmosphere Using a Laser-Induced-Fluorescence Spectrum (LIFS) Lidar
by Yasunori Saito, Kentaro Ichihara, Kenzo Morishita, Kentaro Uchiyama, Fumitoshi Kobayashi and Takayuki Tomida
Remote Sens. 2018, 10(10), 1533; https://doi.org/10.3390/rs10101533 - 24 Sep 2018
Cited by 37 | Viewed by 5574
Abstract
A mobile laser-induced fluorescence spectrum (LIFS) lidar was developed for monitoring pollens floating in the atmosphere. The fluorescence spectrum of pollens excited at 355 nm was measured with a fluorescence spectrometer and the results suggested that in general they had peaks at around [...] Read more.
A mobile laser-induced fluorescence spectrum (LIFS) lidar was developed for monitoring pollens floating in the atmosphere. The fluorescence spectrum of pollens excited at 355 nm was measured with a fluorescence spectrometer and the results suggested that in general they had peaks at around 460 nm and the ranges were 400–600 nm. A fluorescence spectrum database of 25 different pollens was made with the 355 nm excitation. Based on these results, we developed a LIFS lidar that had features in pollen species identification and daytime operation. The former was achieved by the database and the latter was possible by introducing a synchronous-delay detection to a gated CCD spectrometer in an operation time of 200 ns. Fluorescence detection of pollens floating in the atmosphere was performed using the LIFS lidar in a field where cedars grow in the spring and ragweed in the autumn. The LIFS lidar system successfully detected fluorescence spectrums of the pollens at a distance of approximately 20 m away. We discussed the performance of the LIFS lidar by estimating the number of cedar pollens using a lidar equation, introducing a fluorescence cross section of cedar pollens and a sensitivity of the CCD spectrometer that was measured by ourselves. Full article
(This article belongs to the Special Issue Optical and Laser Remote Sensing of the Atmosphere)
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14 pages, 923 KiB  
Article
Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data
by Lin Du, Shuo Shi, Jian Yang, Jia Sun and Wei Gong
Remote Sens. 2016, 8(6), 526; https://doi.org/10.3390/rs8060526 - 22 Jun 2016
Cited by 34 | Viewed by 6603
Abstract
Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral [...] Read more.
Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R2) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation. Full article
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