On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest
It has drawn growing attention to retrieve heavy metal concentration in naturally contaminated arable soil by its spectral reflectance. However, such a spectral reflectance is generally affected by various heavy metal elements, posing a significant challenge to ensure the inversion accuracy for a sp...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X2200913X |
| _version_ | 1811250924708954112 |
|---|---|
| author | Zi-Hao Zhang Fei Guo Zhen Xu Xin-Yu Yang Kun-Ze Wu |
| author_facet | Zi-Hao Zhang Fei Guo Zhen Xu Xin-Yu Yang Kun-Ze Wu |
| author_sort | Zi-Hao Zhang |
| collection | DOAJ |
| description | It has drawn growing attention to retrieve heavy metal concentration in naturally contaminated arable soil by its spectral reflectance. However, such a spectral reflectance is generally affected by various heavy metal elements, posing a significant challenge to ensure the inversion accuracy for a specified heavy metal concentration. The Deep Forest 2021 (DF21) algorithm shows excellent performance in the deep learning, which provides a potential method for hyperspectral inversion with high precision. This paper takes the Chromium (Cr) and Zinc (Zn) concentrations as examples, it explores the DF21 for retrieving heavy metal concentration by the spectral reflectance of the naturally contaminated arable soil. By so doing, various spectral pretreatments and Principal Component Analysis (PCA) are applied to the spectral reflectance. Subsequently, the obtained spectral data, combined with the DF21, are used to establish inversion models. Further, the performances of established models are compared to identify the optimal one for retrieving the Cr and Zn concentrations. The results show that the spectral pretreatment could potentially impact the inversion accuracy, but it exerts little or even a negative impact on the inversion performance once the PCA is applied. As a result, the DF21, together with the original spectra processed by the PCA, i.e., the ORI-PCA-DF21 model, has the optimum performance for retrieving both Zn and Cr concentrations. It is also found that the Cr concentration, which shows a relatively lower degree of heterogeneity, has higher inversion accuracy, suggesting that the spatial heterogeneity could potentially affect the performance of the ORI-PCA-DF21 model. |
| first_indexed | 2024-04-12T16:12:04Z |
| format | Article |
| id | doaj.art-a84ec58e687b48a0a97ff5139ae719c3 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-12T16:12:04Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-a84ec58e687b48a0a97ff5139ae719c32022-12-22T03:25:53ZengElsevierEcological Indicators1470-160X2022-11-01144109440On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forestZi-Hao Zhang0Fei Guo1Zhen Xu2Xin-Yu Yang3Kun-Ze Wu4Department of Electronic and Information Engineering & Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou University, Shantou 515063, ChinaInstitute of Geophysical & Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China; Key Laboratory of Geochemical Cycling of Carbon and Mercury in the Earth’s Critical Zone, Chinese Academy of Geological Sciences, Langfang 065000, ChinaDepartment of Electronic and Information Engineering & Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou University, Shantou 515063, China; Corresponding author.Department of Intelligent Manufacturing Engineering, Shantou University, Shantou 515063, ChinaDepartment of Electronic and Information Engineering & Guangdong Provincial Key Laboratory of Digital Signal and Image Processing, Shantou University, Shantou 515063, ChinaIt has drawn growing attention to retrieve heavy metal concentration in naturally contaminated arable soil by its spectral reflectance. However, such a spectral reflectance is generally affected by various heavy metal elements, posing a significant challenge to ensure the inversion accuracy for a specified heavy metal concentration. The Deep Forest 2021 (DF21) algorithm shows excellent performance in the deep learning, which provides a potential method for hyperspectral inversion with high precision. This paper takes the Chromium (Cr) and Zinc (Zn) concentrations as examples, it explores the DF21 for retrieving heavy metal concentration by the spectral reflectance of the naturally contaminated arable soil. By so doing, various spectral pretreatments and Principal Component Analysis (PCA) are applied to the spectral reflectance. Subsequently, the obtained spectral data, combined with the DF21, are used to establish inversion models. Further, the performances of established models are compared to identify the optimal one for retrieving the Cr and Zn concentrations. The results show that the spectral pretreatment could potentially impact the inversion accuracy, but it exerts little or even a negative impact on the inversion performance once the PCA is applied. As a result, the DF21, together with the original spectra processed by the PCA, i.e., the ORI-PCA-DF21 model, has the optimum performance for retrieving both Zn and Cr concentrations. It is also found that the Cr concentration, which shows a relatively lower degree of heterogeneity, has higher inversion accuracy, suggesting that the spatial heterogeneity could potentially affect the performance of the ORI-PCA-DF21 model.http://www.sciencedirect.com/science/article/pii/S1470160X2200913XHyperspectral reflectanceNaturally contaminated arable soilChromium (Cr) concentrationZinc (Zn) concentrationDeep ForestSpectral pretreatment |
| spellingShingle | Zi-Hao Zhang Fei Guo Zhen Xu Xin-Yu Yang Kun-Ze Wu On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest Ecological Indicators Hyperspectral reflectance Naturally contaminated arable soil Chromium (Cr) concentration Zinc (Zn) concentration Deep Forest Spectral pretreatment |
| title | On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| title_full | On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| title_fullStr | On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| title_full_unstemmed | On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| title_short | On retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| title_sort | on retrieving the chromium and zinc concentrations in the arable soil by the hyperspectral reflectance based on the deep forest |
| topic | Hyperspectral reflectance Naturally contaminated arable soil Chromium (Cr) concentration Zinc (Zn) concentration Deep Forest Spectral pretreatment |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X2200913X |
| work_keys_str_mv | AT zihaozhang onretrievingthechromiumandzincconcentrationsinthearablesoilbythehyperspectralreflectancebasedonthedeepforest AT feiguo onretrievingthechromiumandzincconcentrationsinthearablesoilbythehyperspectralreflectancebasedonthedeepforest AT zhenxu onretrievingthechromiumandzincconcentrationsinthearablesoilbythehyperspectralreflectancebasedonthedeepforest AT xinyuyang onretrievingthechromiumandzincconcentrationsinthearablesoilbythehyperspectralreflectancebasedonthedeepforest AT kunzewu onretrievingthechromiumandzincconcentrationsinthearablesoilbythehyperspectralreflectancebasedonthedeepforest |