Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets
Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears ne...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4264 |
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| author | Hayfa Zayani Youssef Fouad Didier Michot Zeineb Kassouk Nicolas Baghdadi Emmanuelle Vaudour Zohra Lili-Chabaane Christian Walter |
| author_facet | Hayfa Zayani Youssef Fouad Didier Michot Zeineb Kassouk Nicolas Baghdadi Emmanuelle Vaudour Zohra Lili-Chabaane Christian Walter |
| author_sort | Hayfa Zayani |
| collection | DOAJ |
| description | Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively). |
| first_indexed | 2024-03-10T23:14:23Z |
| format | Article |
| id | doaj.art-6f9f596457b6460c8236e4598e6249cb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:23Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6f9f596457b6460c8236e4598e6249cb2023-11-19T08:46:44ZengMDPI AGRemote Sensing2072-42922023-08-011517426410.3390/rs15174264Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral DatasetsHayfa Zayani0Youssef Fouad1Didier Michot2Zeineb Kassouk3Nicolas Baghdadi4Emmanuelle Vaudour5Zohra Lili-Chabaane6Christian Walter7SAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, FranceSAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, FranceSAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, FranceUniversité de Carthage, Institut National Agronomique de Tunisie, LR 17AGR01 (Lr GREEN-TEAM), 43 Avenue Charles Nicolle, Tunis 1082, TunisiaCIRAD, CNRS, INRAE, TETIS, Université de Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, FranceINRAE, Université Paris-Saclay, AgroParisTech, UMR EcoSys, 91120 Palaiseau, FranceUniversité de Carthage, Institut National Agronomique de Tunisie, LR 17AGR01 (Lr GREEN-TEAM), 43 Avenue Charles Nicolle, Tunis 1082, TunisiaSAS, Institut Agro, INRAE, 65 Rue de St Brieuc, 35000 Rennes, FranceUnderstanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).https://www.mdpi.com/2072-4292/15/17/4264soil organic carbondeep learningSentinel-2spectral indicesSentinel-1soil moisture |
| spellingShingle | Hayfa Zayani Youssef Fouad Didier Michot Zeineb Kassouk Nicolas Baghdadi Emmanuelle Vaudour Zohra Lili-Chabaane Christian Walter Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets Remote Sensing soil organic carbon deep learning Sentinel-2 spectral indices Sentinel-1 soil moisture |
| title | Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets |
| title_full | Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets |
| title_fullStr | Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets |
| title_full_unstemmed | Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets |
| title_short | Using Machine-Learning Algorithms to Predict Soil Organic Carbon Content from Combined Remote Sensing Imagery and Laboratory Vis-NIR Spectral Datasets |
| title_sort | using machine learning algorithms to predict soil organic carbon content from combined remote sensing imagery and laboratory vis nir spectral datasets |
| topic | soil organic carbon deep learning Sentinel-2 spectral indices Sentinel-1 soil moisture |
| url | https://www.mdpi.com/2072-4292/15/17/4264 |
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