Ali, A.M.

Darvishzadeh, R.

Gara, T.W.

Heurich, Marco

Marc, Paganini

Skidmore, A.K.

English

Assessment of canopy chlorophyll content (CCC) is an essential variable in developing indicators for biodiversity monitoring and climate change studies. The Sentinel-2 Multi-Spectral Imager (MSI) 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 CCC from Sentinel-2 in Bavarian forest national park, Germany. Fourteen statistical-based methods, including 13 different vegetation indices (VIs) and a non-parametric statistical approach, and two physical-based methods such as INFORM and PROSAIL radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A ﬁeld data collected in July 2017, and cloud-free Sentinel-2 image acquired on 13 July 2017 were used for evaluating the performance of the methods. 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. 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. Further, 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 large scale mapping

University of Twente Research Information

Statistical and Physical methods for Mapping Canopy Chlorophyll Content from Sentinel-2 Data1. Introduction Canopy chlorophyll content (CCC) is one of the plant pigments that provide valuable infor-mation about plant physiology and ecosystem processes to assess the infl uence of climate change, and other anthropogenic and natural factors on plant functions and adaptation. Chlorophyll content at different scales can be monitored using Remote sensing. Al-though numerous methods have been proposed for estimating CCC, to date, there has been little agreement on a method that suitably applies to different remote sensing data across different vegetation types. This study aimed at evaluating the performance of the state-of-art methods for mapping CCC from Sentinel-2.2. Field data and methods The fl owchart of comparing the different CCC retrieval algorithms is illustrated in Figure 1. Statistical-based methods were calibrated and validated by applying the leave-one-out cross-validation technique on in situ measured CCC. whereas RTMs were parameterized to simulate canopy refl ectance, inverted on Sentinel 2 data to predict CCC and validated using the in situ measured CCC. Finally, the CCC products with relatively higher accu-racy  were checked for their spatial consistency. Figure 1: Analytical framework for comparison of CCC retrieval methods form from Sentinel 2 data. Figure 2: Relationship between measured canopy chlorophyll content (CCC) and selected vegetation indices (VIs). The non-parametric method-PLSR trained on eight spectral band subsets of Sentinel 2 (band 2, 3, 4, 5, 6, 8a, 11 & 12) with fi ve components was better predictor of CCC. The spectral infor-mation in the red and red-edge region were the optimal spectral subset for  RTMs inversion.  3.2. Validation Many of the statistical-based methods provide high R2 and low RMSE/Bias combina-tions. R2 ranges from 0.48 to 0.78 (Table 1). The highest R2 was observed for PLSR (R2 = 0.78), and the minimum (R2 = 0.48) for S2REP. 3. Results   3.1. Calibration of the selected methods All the tested vegetation indices showed markedly strong linear positive correlation (r) to CCC (Figure 2) (p ≤ 0.01). The maximum r = 0.88 was observed for three VIs: mSR3, mSR2, and CIred edge. 4. Discussion This study compared the wide range of methods available in the literature in re-trieving CCC from the Sentinel-2 image. Many of the tested methods provided encouraging results.Our results identifi ed several methods, applicable at Sentinel-2 spectral resolution, that can be used for computationally effi cient prediction of CCC. This study did not fi nd a signifi cant difference in accuracy between the physical-based method-INFORM inver-sion and the SNAP toolbox approach.These fi ndings suggest that inversion of RTMs ei-ther using LUT or by applying statistical methods have comparable predictive accuracy in retrieving CCC from remote sensing data.Figure 3: (a-d) scatter plot of the in situ CCC and predictions made by the four methods and (e) their corresponding boxplot.One method with relatively higher accuracy from each category was  further compared for statistical difference and consistency. The scatterplots are presented in fi gure 3.  The Boxplot in Figure 3e illustrates the summary of the four methods prediction compared to in situ CCC data. Paired t-test demonstrated that only prediction performed by the SNAP toolbox showed a signifi cant difference from the in situ CCC (p = 0.05).Table 1: Accuracy of the estimates of CCC in the Bavarian forest national park obtained using the selected methods on Sentinel-2 data.  Conclusion The results indicate that the CCC products from Sentinel-2 imagery will enable a spatial assessment of terrestrial ecosystems condition. The fi ndings will be of interest to inves-tigate the effectiveness of the proposed methods to quantify CCC of different vegeta-tion types for long-term terrestrial ecosystem monitoring efforts across the globe.Acknowledgement This study received funding from the European Space Agency ‘Globdiversity project. We acknowledge the assistance of the the Bavarian Forest National Park staffs. AuthorAbebe Ali, (a.m.ali@utwente.nl) University of Twente Faculty of Geoinformation and Earth Observation (ITC)Department of Natural Resources,

Statistical and physical models for mapping canopy chlorophyll content from Sentinel-2 Data

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Statistical and physical models for mapping canopy chlorophyll content from Sentinel-2 Data

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