Volatility characteristics and hyperspectral-based detection models of diesel in soils
This study developed an efficient method using hyperspectral camera for detecting diesel content in soils with spectral indices. Over 70 days of the experiment, clean soils were saturated with diesel, and 186 measurements were taken to monitor the evaporation rate and spectral variation. The diesel...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017225000070 |
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| author | Jihye Shin Jaehyung Yu Jihee Seo Lei Wang Hyun-Cheol Kim |
| author_facet | Jihye Shin Jaehyung Yu Jihee Seo Lei Wang Hyun-Cheol Kim |
| author_sort | Jihye Shin |
| collection | DOAJ |
| description | This study developed an efficient method using hyperspectral camera for detecting diesel content in soils with spectral indices. Over 70 days of the experiment, clean soils were saturated with diesel, and 186 measurements were taken to monitor the evaporation rate and spectral variation. The diesel evaporation followed a logarithmic pattern, where the diesel volatility decreased from 1.57% per day during the initial period to 0.06% per day during the late period. Using the hull-quotient reflectance at 2236 nm, the diesel content prediction model derived from a stepwise multiple linear regression (SMLR) achieved satisfactory accuracy with sufficient statistical significance (R2 = 0.89, RPD = 2.52). This spectral band was well visualized for diesel presence in hyperspectral images as the band infers variations in two absorptions (CH/AlOH and CH) concurrently. Additionally, this study presented an age estimation model based on the diesel evaporation rate using the same spectral band. Given the fact that this study is based on the largest number of samples with the longest observation period and models were developed excluding atmospheric absorption bands, the simple form of the spectral index makes it applicable to large-scale diesel pollution detection with hyperspectral scanners or narrow-band multispectral cameras in real-world cases. |
| format | Article |
| id | doaj-art-519c4ee5fe23476f86a9fa5eb543efd9 |
| institution | Kabale University |
| issn | 2666-0172 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj-art-519c4ee5fe23476f86a9fa5eb543efd92025-02-07T04:48:18ZengElsevierScience of Remote Sensing2666-01722025-06-0111100201Volatility characteristics and hyperspectral-based detection models of diesel in soilsJihye Shin0Jaehyung Yu1Jihee Seo2Lei Wang3Hyun-Cheol Kim4Department of Earth, Environmental & Space Sciences, Chungnam National University, Daejeon, 34134, South Korea; Department of Water Environment Research, Chungcheongnam-do Insititute of Health and Environment Research, Hongseong, 32254, South KoreaDepartment of Geological Sciences, Chungnam National University, Daejeon, 34134, South Korea; Corresponding author.Department of Geography and the Environment, The University of Alabama, Tuscaloosa, AL, 35487, USADepartment of Geography & Anthropology, Louisiana State University, Baton Rouge, LA, 70803, USACenter of Remote Sensing and GIS, Korea Polar Researh Institute, Incheon, 21990, South KoreaThis study developed an efficient method using hyperspectral camera for detecting diesel content in soils with spectral indices. Over 70 days of the experiment, clean soils were saturated with diesel, and 186 measurements were taken to monitor the evaporation rate and spectral variation. The diesel evaporation followed a logarithmic pattern, where the diesel volatility decreased from 1.57% per day during the initial period to 0.06% per day during the late period. Using the hull-quotient reflectance at 2236 nm, the diesel content prediction model derived from a stepwise multiple linear regression (SMLR) achieved satisfactory accuracy with sufficient statistical significance (R2 = 0.89, RPD = 2.52). This spectral band was well visualized for diesel presence in hyperspectral images as the band infers variations in two absorptions (CH/AlOH and CH) concurrently. Additionally, this study presented an age estimation model based on the diesel evaporation rate using the same spectral band. Given the fact that this study is based on the largest number of samples with the longest observation period and models were developed excluding atmospheric absorption bands, the simple form of the spectral index makes it applicable to large-scale diesel pollution detection with hyperspectral scanners or narrow-band multispectral cameras in real-world cases.http://www.sciencedirect.com/science/article/pii/S2666017225000070Hyperspectral imageDiesel pollutionVolatilitySpectral indexEnvironmental monitoring |
| spellingShingle | Jihye Shin Jaehyung Yu Jihee Seo Lei Wang Hyun-Cheol Kim Volatility characteristics and hyperspectral-based detection models of diesel in soils Science of Remote Sensing Hyperspectral image Diesel pollution Volatility Spectral index Environmental monitoring |
| title | Volatility characteristics and hyperspectral-based detection models of diesel in soils |
| title_full | Volatility characteristics and hyperspectral-based detection models of diesel in soils |
| title_fullStr | Volatility characteristics and hyperspectral-based detection models of diesel in soils |
| title_full_unstemmed | Volatility characteristics and hyperspectral-based detection models of diesel in soils |
| title_short | Volatility characteristics and hyperspectral-based detection models of diesel in soils |
| title_sort | volatility characteristics and hyperspectral based detection models of diesel in soils |
| topic | Hyperspectral image Diesel pollution Volatility Spectral index Environmental monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S2666017225000070 |
| work_keys_str_mv | AT jihyeshin volatilitycharacteristicsandhyperspectralbaseddetectionmodelsofdieselinsoils AT jaehyungyu volatilitycharacteristicsandhyperspectralbaseddetectionmodelsofdieselinsoils AT jiheeseo volatilitycharacteristicsandhyperspectralbaseddetectionmodelsofdieselinsoils AT leiwang volatilitycharacteristicsandhyperspectralbaseddetectionmodelsofdieselinsoils AT hyuncheolkim volatilitycharacteristicsandhyperspectralbaseddetectionmodelsofdieselinsoils |