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VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 24500

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Special Issue Editors

Dr. Jaehyung Yu

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Guest Editor

Department of Geological Sciences, Chungnam National University, Daejeon, Korea
Interests: spectral variation; heavy metal contamination; UAV images; spectral reflectance

Dr. Lei Wang

E-Mail Website
Co-Guest Editor

Louisiana State University, USA
Interests: spatial analysis models; water quality; soil moisture; drone remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid growth in high spectral and spatial resolution remote sensing especially the drone-based systems, it is critical to validate the quality and quantity assessment models established from surface reflectance and earth surface materials. The highly dynamic and spatially heterogeneous local environmental conditions complicate the inversion models for surface properties observed by multiple platforms. The objectives of this special issues are to assemble concurrent contributions on calibrating VNIR-SWIR sensors at different levels of remote sensing platforms from spectroscopic analyses. Case studies of spectroscopic analysis of earth and environmental issues such as water quality, mineral resources, soil peoperties, and organic matters are welcome, as well as review contributions.

Dr. Jaehyung Yu
Dr. Lei Wang
Guest Editors

Manuscript Submission Information

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Keywords

VNIR spectroscopy
SWIR spectroscopy
Multi-scale platforms
Hyperspectral remote sensing
UAV remote sensing
Earth environment

Published Papers (7 papers)

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Research

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18 pages, 11123 KiB

Open AccessArticle

Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks

by Huy Hoa Huynh, Jaehung Yu, Lei Wang, Nam Hoon Kim, Bum Han Lee, Sang-Mo Koh, Sehyun Cho and Trung Hieu Pham

Remote Sens. 2021, 13(15), 3037; https://doi.org/10.3390/rs13153037 - 03 Aug 2021

Cited by 7 | Viewed by 3693

Abstract

This paper demonstrates an integrative 3D model of short-wave infrared (SWIR) hyperspectral mapping and unmanned aerial vehicle (UAV)-based digital elevation model (DEM) for a carbonate rock outcrop including limestone and dolostone in a field condition. The spectral characteristics in the target outcrop showed the limestone well coincided with the reference spectra, while the dolostone did not show clear absorption features compared to the reference spectra, indicating a mixture of clay minerals. The spectral indices based on SWIR hyperspectral images were derived for limestone and dolostone using aluminum hydroxide (AlOH), hydroxide (OH), iron hydroxide (FeOH), magnesium hydroxide (MgOH) and carbonate ion (CO₃²⁻) absorption features based on random forest and logistic regression models with an accuracy over 87%. Given that the indices were derived from field data with consideration of commonly occurring geological units, the indices have better applicability for real world cases. The integrative 3D geological model developed by co-registration between hyperspectral map and UAV-based DEM using best matching SIFT descriptor pairs showed the 3D rock formations between limestone and dolostone. Moreover, additional geological information of the outcrop was extracted including thickness, slope, rock classification, strike, and dip. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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16 pages, 6844 KiB

Open AccessArticle

Characteristics of Satellite-Based Ocean Turbulent Heat Flux around the Korean Peninsula and Relationship with Changes in Typhoon Intensity

by Jaemin Kim and Yun Gon Lee

Remote Sens. 2021, 13(1), 42; https://doi.org/10.3390/rs13010042 - 24 Dec 2020

Cited by 1 | Viewed by 1769

Abstract

Ocean-atmosphere energy exchange is an important factor in the maintenance of oceanic and atmospheric circulation and the regulation of meteorological and climate systems. Oceanic sensible and latent heat fluxes around the Korean Peninsula were determined using satellite-based air-sea variables (wind speed, sea surface temperature, and atmospheric specific humidity and temperature) and the coupled ocean-atmosphere response experiment (COARE) 3.5 bulk algorithm for six years between 2014 and 2019. Seasonal characteristics of the marine heat flux and its short-term fluctuations during summer typhoons were also investigated. air-sea variables were produced through empirical relationships and verified with observational data from marine buoys around the Korean Peninsula. Satellite-derived wind speed, sea surface temperature, atmospheric specific humidity, and air temperature were strongly correlated with buoy data, with R² values of 0.80, 0.97, 0.90, and 0.91, respectively. Satellite-based sensible and latent heat fluxes around the peninsula were also validated against fluxes calculated from marine buoy data, and displayed low values in summer and higher values in autumn and winter as the difference between air-sea temperature and specific humidity increased. Through analyses of spatio-temporal fluctuations in the oceanic turbulent heat flux and variations in intensities of typhoons, this study assessed the possibility of monitoring air-sea energy exchange using satellite-based ocean turbulent heat fluxes during high-impact weather. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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20 pages, 4853 KiB

Open AccessArticle

Variations in Spectral Signals of Heavy Metal Contamination in Mine Soils Controlled by Mineral Assemblages

by Hyesu Kim, Jaehyung Yu, Lei Wang, Yongsik Jeong and Jieun Kim

Remote Sens. 2020, 12(20), 3273; https://doi.org/10.3390/rs12203273 - 09 Oct 2020

Cited by 7 | Viewed by 2379

Abstract

This paper illustrates a spectroscopic analysis of heavy metal concentration in mine soils with the consideration of mineral assemblages originated by weathering and mineralization processes. The mine soils were classified into two groups based on the mineral composition: silicate clay mineral group (Group A) and silicate–carbonate–skarn–clay mineral group (Group B). Both soil groups are contaminated with Cu, Zn, As, and Pb, while the contamination level was higher for Group A. The two groups exhibit different geochemical behaviors with different heavy metal contamination. The spectral variation associated with heavy metal was highly correlated with absorption features of clay and iron oxide minerals for Group A, and the absorption features of skarn minerals, iron oxides, and clay minerals for Group B. It indicates that the geochemical adsorption of heavy metal elements mainly occurs with clay minerals and iron oxides from weathering, and of skarn minerals, iron oxides, and clay minerals from mineralization. Therefore, soils from different secondary mineral production processes should be analyzed with different spectral models. We constructed spectral models for predicting Cu, Zn, As, and Pb in soil group A and Zn and Pb in soil group B using corresponding absorptions. Both models were statistically significant with sufficient accuracy. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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29 pages, 22571 KiB

Open AccessArticle

Intercomparison of Satellite-Derived Solar Irradiance from the GEO-KOMSAT-2A and HIMAWARI-8/9 Satellites by the Evaluation with Ground Observations

by Chang Ki Kim, Hyun-Goo Kim, Yong-Heack Kang, Chang-Yeol Yun and Yun Gon Lee

Remote Sens. 2020, 12(13), 2149; https://doi.org/10.3390/rs12132149 - 04 Jul 2020

Cited by 12 | Viewed by 3689

Abstract

Solar irradiance derived from satellite imagery is useful for solar resource assessment, as well as climate change research without spatial limitation. The University of Arizona Solar Irradiance Based on Satellite–Korea Institute of Energy Research (UASIBS-KIER) model has been updated to version 2.0 in order to employ the satellite imagery produced by the new satellite platform, GK-2A, launched on 5 December 2018. The satellite-derived solar irradiance from UASIBS-KIER model version 2.0 is evaluated against the two ground observations in Korea at instantaneous, hourly, and daily time scales in comparison with the previous version of UASIBS-KIER model that was optimized for the COMS satellite. The root mean square error of the UASIBS-KIER model version 2.0, normalized for clear-sky solar irradiance, ranges from 4.8% to 5.3% at the instantaneous timescale when the sky is clear. For cloudy skies, the relative root mean square error values are 14.5% and 15.9% at the stations located in Korea and Japan, respectively. The model performance was improved when the UASIBS-KIER model version 2.0 was used for the derivation of solar irradiance due to the finer spatial resolution. The daily aggregates from the proposed model are proven to be the most reliable estimates, with 0.5 km resolution, compared with the solar irradiance derived by the other models. Therefore, the solar resource map built by major outputs from the UASIBS-KIER model is appropriate for solar resource assessment. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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26 pages, 4925 KiB

Open AccessArticle

Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing—A Laboratory Approach

by Baru Chung, Jaehyung Yu, Lei Wang, Nam Hoon Kim, Bum Han Lee, Sangmo Koh and Sangin Lee

Remote Sens. 2020, 12(8), 1325; https://doi.org/10.3390/rs12081325 - 22 Apr 2020

Cited by 18 | Viewed by 4619

Abstract

This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO₃, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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Other

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12 pages, 3389 KiB

Open AccessLetter

Comparison of Canopy Shape and Vegetation Indices of Citrus Trees Derived from UAV Multispectral Images for Characterization of Citrus Greening Disease

by Anjin Chang, Junho Yeom, Jinha Jung and Juan Landivar

Remote Sens. 2020, 12(24), 4122; https://doi.org/10.3390/rs12244122 - 17 Dec 2020

Cited by 26 | Viewed by 3328

Abstract

Citrus greening is a severe disease significantly affecting citrus production in the United States because the disease is not curable with currently available technologies. For this reason, monitoring citrus disease in orchards is critical to eradicate and replace infected trees before the spread of the disease. In this study, the canopy shape and vegetation indices of infected and healthy orange trees were compared to better understand their significant characteristics using unmanned aerial vehicle (UAV)-based multispectral images. Individual citrus trees were identified using thresholding and morphological filtering. The UAV-based phenotypes of each tree, such as tree height, crown diameter, and canopy volume, were calculated and evaluated with the corresponding ground measurements. The vegetation indices of infected and healthy trees were also compared to investigate their spectral differences. The results showed that correlation coefficients of tree height and crown diameter between the UAV-based and ground measurements were 0.7 and 0.8, respectively. The UAV-based canopy volume was also highly correlated with the ground measurements (R² > 0.9). Four vegetation indices—normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), modified soil adjusted vegetation index (MSAVI), and chlorophyll index (CI)—were significantly higher in healthy trees than diseased trees. The RedEdge-related vegetation indices showed more capability for citrus disease monitoring. Additionally, the experimental results showed that the UAV-based flush ratio and canopy volume can be valuable indicators to differentiate trees with citrus greening disease. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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13 pages, 3851 KiB

Open AccessLetter

Utilizing Hyperspectral Remote Sensing for Soil Gradation

by Jordan Ewing, Thomas Oommen, Paramsothy Jayakumar and Russell Alger

Remote Sens. 2020, 12(20), 3312; https://doi.org/10.3390/rs12203312 - 12 Oct 2020

Cited by 10 | Viewed by 4010

Abstract

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil. Full article

(This article belongs to the Special Issue VNIR-SWIR Spectroscopic and Remote Sensing Applications to Earth Science and Environmental Issues)

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