气候变化领域集成服务门户(CCPortal): RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits

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DOI	10.1016/j.jag.2020.102219
	RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits
	Wocher M.; Berger K.; Danner M.; Mauser W.; Hank T.
发表日期	2020
ISSN	15698432
卷号	93
英文摘要	Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (Cab), carotenoids (Ccx), and water (Cw) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural Ccx-Cab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for Ccx, R2 = 0.85 (i) and 0.53 (ii) for Cab, and R2 = 0.97 (i) and 0.67 (ii) for Cw, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (Ccx: R2 = 0.86, RMSE = 2.1 μg cm−2; Cab: R2 = 0.67, RMSE = 12.5 μg cm-2; Cw: R2 = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding Cab (R2 = 0.84; RMSE = 9.06 μg cm-2) and canopy water content (CWC, R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite. © 2020 The Authors
英文关键词	Carotenoid content; Chlorophyll content; Hyperspectral; LUT; PROSAIL RTM; Spectroscopy; Water content
语种	英语
scopus关键词	absorption; biochemical composition; carotenoid; chlorophyll; concentration (composition); photosynthesis; spectral analysis; water content; Albacete [Castilla-La Mancha]; Barrax; Bavaria; Castilla-La Mancha; Germany; Munich; Spain
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156422
作者单位	Department of Geography, Ludwig-Maximilians-University Munich, Luisenstraße 37, Munich, 80333, Germany
推荐引用方式 GB/T 7714	Wocher M.,Berger K.,Danner M.,et al. RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits[J],2020,93.
APA	Wocher M.,Berger K.,Danner M.,Mauser W.,&Hank T..(2020).RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits.International Journal of Applied Earth Observation and Geoinformation,93.
MLA	Wocher M.,et al."RTM-based dynamic absorption integrals for the retrieval of biochemical vegetation traits".International Journal of Applied Earth Observation and Geoinformation 93(2020).