| Research Articles |
|
|
|
|
| Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves |
| ZhongGuo MA1, Xi CHEN1, Quan WANG1,2, PingHeng LI1, Guli Jiapaer1 |
1 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
2 Faculty of Agriculture, Shizuoka University, Shizuoka 422-8529, Japan |
|
|
|
Abstract Leaf biochemical properties have been widely assessed using hyperspectral reflectance information by inversion of PROSPECT model or by using hyperspectral indices, but few studies have focused on arid ecosystems. As a dominant species of riparian ecosystems in arid lands, Populus euphratica Oliv. is an unusual tree species with polymorphic leaves along the vertical profile of canopy corresponding to different growth stages. In this study, we evaluated both the inversed PROSPECT model and hyperspectral indices for estimating biochemical properties of P. euphratica leaves. Both the shapes and biochemical properties of P. euphratica leaves were found to change with the heights from ground surface. The results indicated that the model inversion calibrated for each leaf shape performed much better than the model calibrated for all leaf shapes, and also better than hyperspectral indices. Similar results were obtained for estimations of equivalent water thickness (EWT) and leaf mass per area (LMA). Hyperspectral indices identified in this study for estimating these leaf properties had root mean square error (RMSE) and R2 values between those obtained with the two calibration strategies using the inversed PROSPECT model. Hence, the inversed PROSPECT model can be applied to estimate leaf biochemical properties in arid ecosystems, but the calibration to the model requires special attention.
|
|
Received: 27 June 2011
Published: 05 March 2012
|
| Fund: The West Light Talents Cultivation Program of Chinese Academy of Sciences (XBBS 200801), the National Natural Science Foundation of China (40801146), and the JSPS Project (21403001). |
|
Corresponding Authors:
Xi CHEN
E-mail: chenxi@ms.xjb.ac.cn
|
|
|
| Baret F, Fourty T. 1997. Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements. Agronomie, 17(9–10): 455–464.Broge N H, Mortensen J V. 2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 81(1): 45–57.Ceccato P, Flasse S, Tarantola S. 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77(1): 22–33.Colombo R, Meroni M, Marchesi A. 2008. Estimation of leaf and canopy water content in poplar plantations by means of hyperspec-tral indices and inverse modeling. Remote Sensing of Environment, 112(1): 1820–1834.Eitel J U H, Gessler P E, Smith A M S. 2006. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecology and Management, 229(1–3): 170–182.Feret J B, François C, Asner, G P. 2008. PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, 112(6): 3030–3043.Gamon J A, Surfus J S. 1999. Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1): 105–117.Gitelson A A, Gritz Y, Merzlyak M N. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160(3): 271–282.Jacquemoud S, Bacour C, Poilve H. 2000. Comparison of four radiative transfer models to simulate plant canopies reflectance, direct and inverse mode. Remote Sensing of Environment, 74(3): 471–481.Jacquemoud S, Baret F. 1990. PROSPECT: a model of leaf optical properties spectra. Remote Sensing of Environment, 34(2): 75–91.Le Maire G, François C, Dufrêne E. 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment, 89(1): 1–28.Ma Z G, Chen X, Jiapaer G, et al. 2009. Study on LAI estimation of broadleaf forests in arid areas using digital hemispherical photography. In: 2009 International Conference on Environmental Science and Information Application Technology. Beijing: China Environmental Science Press, Volume II, 375–378.Maccioni A, Agati G, Mazzinghi P. 2001. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. Journal of Photochemistry and Photobiology, B: Biology, 61(1–2): 52–61.Newnham G J, Burt T. 2001. Validation of a leaf reflectance and transmittance model for three agricultural crop species. In: IEEE Geoscience and Remote Sensing Symposium (IGARSS'01). New York: IEEE, (7): 2976–2978.Pablo J Z, John R M, John H. 2004. Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies. Remote Sensing of Environment, 89(2): 189–199.Peñuelas J, Gamon J A, Fredeen A L. 1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing of Environment, 48(2): 135–146.Porra R J, Thomson W A, Kriedmann P E. 1989. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta, 975(3): 384–394.Riaño D, Vaughan P, Chuvieco E. 2005. Estimation of fuel moisture content by inversion of radiative transfermodels to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transaction on Geoscience and Remote Sensing, 43(1): 819–826.Schaepman-Strub G, Schaepman M E, Painter T H. 2006. Reflectance quantities in optical remote sensing—definitions and case studies. Remote Sensing of Environment, 103(1): 27–42.Seelig H D, Hoehn A, Stodieck L S. 2008. Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants. Remote Sensing of Environment, 112(2): 445–455.Sims D A, Gamon J A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2–3): 337–354.Smith R C G, Adams J, Stephens D J. 1995. Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite. Australian Journal of Agricultural Research, 46(1): 113–125.Strachan I B, Pattey E, Boisvert J. 2002. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80(2): 213–224.Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16(2): 125–141.Vogelman J E, Rock B N, Moss D M. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing, 14(2): 1563–1575.Wang R H, Wang X, You X. 2002. Analysis on the structure of the desert riparian forest ecosystems. Arid Zone Research, 19(2): 7–11.Yang S D, Zheng W J, Chen G C, et al. 2005. Difference of ultrastructure and photosynthetic characteristics between lanceolate and broad-ovate leaves in Populus euphratica. Acta Botanica Boreali-Occidentalia Sinica, 25(1): 14–20.Yue N, Zheng C X, Bai X, et al. 2009. Proteomics analysis of heteromorphic leaves of Populus euphratica Oliv. China Biotechnolog, 29(9): 40–44. Zarco-Tejada P J, Miller J R, Mohammed G H. 2002. Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. Journal of Environmental Quality, 31(4): 1433–1441.Zarco-Tejada P J, Miller J R, Morales A. 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90(4): 463–476.Zhao D L, Reddy K R, Kakani V G. 2005. Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agronomy Journal, 97(1): 89–98.Zheng C X, Qiu J, Jiang C N, et al. 2006. Comparison of characteristics of stomas and photosynthesis of Populus euphratica polymorphic leaves. Scientia Silvae Sinicae, 42(8): 19–24. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
