Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis

被引:82
作者
Cheng, Tao [1 ]
Rivard, Benoit [2 ]
Sanchez-Azofeifa, Arturo G. [2 ,3 ]
Feret, Jean-Baptiste [4 ]
Jacquemoud, Stephane [5 ]
Ustin, Susan L. [1 ]
机构
[1] Univ Calif Davis, Dept Land Air & Water Resources, CSTARS, Davis, CA 95616 USA
[2] Univ Alberta, Dept Earth & Atmospher Sci, Earth Observat Syst Lab, Edmonton, AB T6G 2E3, Canada
[3] Smithsonian Trop Res Inst, Panama City, Panama
[4] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA
[5] Univ Paris Diderot, Inst Phys Globe Paris, Sorbonne Paris Cite, UMR CNRS 7154, F-75013 Paris, France
基金
加拿大自然科学与工程研究理事会;
关键词
Leaf mass per area; Dry matter content; Specific leaf area; PROSPECT model; Remote sensing; Wavelet analysis; PHOTOSYNTHESIS-NITROGEN RELATIONS; FUEL MOISTURE-CONTENT; OPTICAL-PROPERTIES; WATER-CONTENT; HYPERSPECTRAL INDEXES; CHLOROPHYLL CONTENT; RETRIEVAL; PROSPECT; DECOMPOSITION; CONSEQUENCES;
D O I
10.1016/j.isprsjprs.2013.10.009
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R-2: 0.51-0.82, p < 0.0001). The best robustness (R-2 = 0.74, RMSE = 18.97 g/m(2) and Bias = 0.12 g/m(2)) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or space-borne imaging spectroscopy data for mapping canopy foliar dry biomass. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:28 / 38
页数:11
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