Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest
Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | http://dx.doi.org/10.1080/17538947.2020.1794064 |
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| author | Abebe Mohammed Ali Roshanak Darvishzadeh Andrew Skidmore Tawanda W. Gara Marco Heurich |
| author_facet | Abebe Mohammed Ali Roshanak Darvishzadeh Andrew Skidmore Tawanda W. Gara Marco Heurich |
| author_sort | Abebe Mohammed Ali |
| collection | DOAJ |
| description | Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R2 = 0.26, RMSE = 3.9 µg/cm2), CCC (R2 = 0.65, RMSE = 0.33 g/m2), and LAI (R2 = 0.47, RMSE = 0.73 m2/m2), comparable to the estimations based on the machine learning method Random forest regression of LCC (R2 = 0.34, RMSE = 4.06 µg/cm2), CCC (R2 = 0.65, RMSE = 0.34 g/m2), and LAI (R2 = 0.47, RMSE = 0.75 m2/m2). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data. |
| first_indexed | 2024-03-11T23:01:02Z |
| format | Article |
| id | doaj.art-ea8625b38ff242a1a62942284472f3f7 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:01:02Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-ea8625b38ff242a1a62942284472f3f72023-09-21T14:57:09ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552021-01-0114110612010.1080/17538947.2020.17940641794064Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forestAbebe Mohammed Ali0Roshanak Darvishzadeh1Andrew Skidmore2Tawanda W. Gara3Marco Heurich4Faculty of Geo-Information Science and Earth Observation (ITC), University of TwenteFaculty of Geo-Information Science and Earth Observation (ITC), University of TwenteFaculty of Geo-Information Science and Earth Observation (ITC), University of TwenteFaculty of Geo-Information Science and Earth Observation (ITC), University of TwenteDepartment of Visitor Management and National Park Monitoring, Bavarian Forest National ParkAssessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R2 = 0.26, RMSE = 3.9 µg/cm2), CCC (R2 = 0.65, RMSE = 0.33 g/m2), and LAI (R2 = 0.47, RMSE = 0.73 m2/m2), comparable to the estimations based on the machine learning method Random forest regression of LCC (R2 = 0.34, RMSE = 4.06 µg/cm2), CCC (R2 = 0.65, RMSE = 0.34 g/m2), and LAI (R2 = 0.47, RMSE = 0.75 m2/m2). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.http://dx.doi.org/10.1080/17538947.2020.1794064leaf area indexleaf/canopy chlorophyll contentradiative transfer modellook-up tablemachine learning algorithms |
| spellingShingle | Abebe Mohammed Ali Roshanak Darvishzadeh Andrew Skidmore Tawanda W. Gara Marco Heurich Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest International Journal of Digital Earth leaf area index leaf/canopy chlorophyll content radiative transfer model look-up table machine learning algorithms |
| title | Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest |
| title_full | Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest |
| title_fullStr | Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest |
| title_full_unstemmed | Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest |
| title_short | Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest |
| title_sort | machine learning methods performance in radiative transfer model inversion to retrieve plant traits from sentinel 2 data of a mixed mountain forest |
| topic | leaf area index leaf/canopy chlorophyll content radiative transfer model look-up table machine learning algorithms |
| url | http://dx.doi.org/10.1080/17538947.2020.1794064 |
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