Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data |
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| Institution: | 1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands;2. Wollo University, Department of Geography and Environmental Studies, P.O Box 1145, Dessie, Ethiopia;3. Department of Environmental Science, Macquarie University, NSW, 2106, Australia;4. Department of Geography and Environmental Science, University of Zimbabwe, P.O Box MP167, Mt Pleasant, Harare, Zimbabwe;5. UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntingdon Road, Cambridge, CB3 0DL, UK;6. University of Zürich UZH, Department of Geography, Remote Sensing Laboratories, Winterthurerstrasse 190, 8057 Zurich, Switzerland;7. Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, 94481 Grafenau, Germany;8. Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Tennenbacher Straße 4, Germany;9. European Space Agency - ESRIN, Via Galileo Galilei, Casella Postale 64, 00044 Frascati RM, Italy |
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| Abstract: | The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A ?eld campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe. |
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| Keywords: | Canopy chlorophyll content (CCC) Comparing methods Statistical methods Radiative transfer model inversion SNAP toolbox Sentinel-2 |
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