Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing
Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to...
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| Published in | Remote sensing of environment Vol. 271; no. C; p. 112914 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Inc
15.03.2022
Elsevier BV Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0034-4257 1879-0704 |
| DOI | 10.1016/j.rse.2022.112914 |
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| Abstract | Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. This study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg−1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R2 = 0.96, RMSE = 30.81 g·kg−1), mineral soils (SOC ≤ 120 g·kg−1, R2 = 0.71, RMSE = 10.60 g·kg−1), and organic soils (SOC > 120 g·kg−1, R2 = 0.78, RMSE = 62.31 g·kg−1). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m3·m−3, green leaf area < 0.3 m2·m−2, plant residue <0.4 m2·m−2) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.
•LSTM and CNN were identified from 7 algorithms for spectra-based SOC predictions.•12 airborne and spaceborne data were simulated by a large soil library to predict SOC.•Impacts of soil moisture, vegetation and plant residues on SOC predictions were assessed.•Spectral SOC concentration models for mineral and organic soils are different.•Satellite hyperspectral and multispectral fusion data have high potential to quantify SOC. |
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| AbstractList | Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. This study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg−1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R2 = 0.96, RMSE = 30.81 g·kg−1), mineral soils (SOC ≤ 120 g·kg−1, R2 = 0.71, RMSE = 10.60 g·kg−1), and organic soils (SOC > 120 g·kg−1, R2 = 0.78, RMSE = 62.31 g·kg−1). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m3·m−3, green leaf area < 0.3 m2·m−2, plant residue <0.4 m2·m−2) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.
•LSTM and CNN were identified from 7 algorithms for spectra-based SOC predictions.•12 airborne and spaceborne data were simulated by a large soil library to predict SOC.•Impacts of soil moisture, vegetation and plant residues on SOC predictions were assessed.•Spectral SOC concentration models for mineral and organic soils are different.•Satellite hyperspectral and multispectral fusion data have high potential to quantify SOC. Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. This study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg−1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R2 = 0.96, RMSE = 30.81 g·kg−1), mineral soils (SOC ≤ 120 g·kg−1, R2 = 0.71, RMSE = 10.60 g·kg−1), and organic soils (SOC > 120 g·kg−1, R2 = 0.78, RMSE = 62.31 g·kg−1). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m3·m−3, green leaf area < 0.3 m2·m−2, plant residue <0.4 m2·m−2) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative. Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. This study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg⁻¹ across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R² = 0.96, RMSE = 30.81 g·kg⁻¹), mineral soils (SOC ≤ 120 g·kg⁻¹, R² = 0.71, RMSE = 10.60 g·kg⁻¹), and organic soils (SOC > 120 g·kg⁻¹, R² = 0.78, RMSE = 62.31 g·kg⁻¹). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m³·m⁻³, green leaf area < 0.3 m²·m⁻², plant residue <0.4 m²·m⁻²) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative. Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg–1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R2 = 0.96, RMSE = 30.81 g·kg–1), mineral soils (SOC ≤ 120 g·kg–1, R2 = 0.71, RMSE = 10.60 g·kg–1), and organic soils (SOC > 120 g·kg–1, R2 = 0.78, RMSE = 62.31 g·kg–1). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m3·m–3, green leaf area < 0.3 m2·m–2, plant residue <0.4 m2·m–2) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative. |
| ArticleNumber | 112914 |
| Author | Zhou, Wang Zhang, Chenhui Lee, DoKyoung Guan, Kaiyu Zhou, Qu Wang, Sheng Margenot, Andrew J. Ge, Yufeng Peng, Jian Huang, Yizhi |
| Author_xml | – sequence: 1 givenname: Sheng surname: Wang fullname: Wang, Sheng email: shengwang12@gmail.com organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 2 givenname: Kaiyu surname: Guan fullname: Guan, Kaiyu email: kaiyug@illinois.edu organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 3 givenname: Chenhui surname: Zhang fullname: Zhang, Chenhui organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 4 givenname: DoKyoung surname: Lee fullname: Lee, DoKyoung organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 5 givenname: Andrew J. surname: Margenot fullname: Margenot, Andrew J. organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 6 givenname: Yufeng surname: Ge fullname: Ge, Yufeng organization: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA – sequence: 7 givenname: Jian surname: Peng fullname: Peng, Jian organization: National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 8 givenname: Wang surname: Zhou fullname: Zhou, Wang organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 9 givenname: Qu surname: Zhou fullname: Zhou, Qu organization: Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA – sequence: 10 givenname: Yizhi surname: Huang fullname: Huang, Yizhi organization: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA |
| BackLink | https://www.osti.gov/servlets/purl/1977612$$D View this record in Osti.gov |
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| ContentType | Journal Article |
| Copyright | 2022 Elsevier Inc. Copyright Elsevier BV Mar 15, 2022 |
| Copyright_xml | – notice: 2022 Elsevier Inc. – notice: Copyright Elsevier BV Mar 15, 2022 |
| CorporateAuthor | Univ. of Illinois at Urbana-Champaign, IL (United States) |
| CorporateAuthor_xml | – name: Univ. of Illinois at Urbana-Champaign, IL (United States) |
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| DOI | 10.1016/j.rse.2022.112914 |
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| Keywords | Spectroscopy Hyperspectral reflectance Machine learning Radiative transfer modeling Long short-term memory SBG Soil organic carbon |
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| Snippet | Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical... |
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| SubjectTerms | Airborne sensing Algorithms Artificial neural networks Atmospheric models Carbon Carbon cycle cost effectiveness Ecosystem services ecosystems environment Environmental monitoring ENVIRONMENTAL SCIENCES Geology Hyperspectral reflectance Infrared signatures Laboratories Leaf area Learning algorithms Learning theory Least squares method Libraries Long short-term memory Machine learning Neural networks Noise levels Organic carbon Organic soils Performance prediction plant residues Radiative transfer Radiative transfer modeling Reflectance Remote sensing Satellite data Satellites SBG Short wave radiation Soil moisture Soil organic carbon soil water Spectra spectral analysis Spectral signatures Spectroscopy Spectrum analysis Vegetation |
| Title | Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing |
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