Please wait a minute...
Journal of Arid Land  2012, Vol. 4 Issue (1): 52-62    DOI: 10.3724/SP.J.1227.2012.00052
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
Download:   PDF(1122KB)
Export: BibTeX | EndNote (RIS)      

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.

Key wordsTarim River Basin      climate change      hydrological change      water resources      streamflow     
Received: 27 June 2011      Published: 05 March 2012

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:
Cite this article:

ZhongGuo MA, Xi CHEN, Quan WANG, PingHeng LI, Guli Jiapaer. Retrieval of leaf biochemical properties by inversed PROSPECT model and hyperspectral indices: an application to Populus euphratica polymorphic leaves. Journal of Arid Land, 2012, 4(1): 52-62.

URL:     OR

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.

[1] ZHAO Xuqin, LUO Min, MENG Fanhao, SA Chula, BAO Shanhu, BAO Yuhai. Spatiotemporal changes of gross primary productivity and its response to drought in the Mongolian Plateau under climate change[J]. Journal of Arid Land, 2024, 16(1): 46-70.
[2] Mitiku A WORKU, Gudina L FEYISA, Kassahun T BEKETIE, Emmanuel GARBOLINO. Projecting future precipitation change across the semi-arid Borana lowland, southern Ethiopia[J]. Journal of Arid Land, 2023, 15(9): 1023-1036.
[3] QIN Guoqiang, WU Bin, DONG Xinguang, DU Mingliang, WANG Bo. Evolution of groundwater recharge-discharge balance in the Turpan Basin of China during 1959-2021[J]. Journal of Arid Land, 2023, 15(9): 1037-1051.
[4] MA Jinpeng, PANG Danbo, HE Wenqiang, ZHANG Yaqi, WU Mengyao, LI Xuebin, CHEN Lin. Response of soil respiration to short-term changes in precipitation and nitrogen addition in a desert steppe[J]. Journal of Arid Land, 2023, 15(9): 1084-1106.
[5] ZHANG Hui, Giri R KATTEL, WANG Guojie, CHUAI Xiaowei, ZHANG Yuyang, MIAO Lijuan. Enhanced soil moisture improves vegetation growth in an arid grassland of Inner Mongolia Autonomous Region, China[J]. Journal of Arid Land, 2023, 15(7): 871-885.
[6] ZHANG Zhen, XU Yangyang, LIU Shiyin, DING Jing, ZHAO Jinbiao. Seasonal variations in glacier velocity in the High Mountain Asia region during 2015-2020[J]. Journal of Arid Land, 2023, 15(6): 637-648.
[7] GAO Xiang, WEN Ruiyang, Kevin LO, LI Jie, YAN An. Heterogeneity and non-linearity of ecosystem responses to climate change in the Qilian Mountains National Park, China[J]. Journal of Arid Land, 2023, 15(5): 508-522.
[8] Reza DEIHIMFARD, Sajjad RAHIMI-MOGHADDAM, Farshid JAVANSHIR, Alireza PAZOKI. Quantifying major sources of uncertainty in projecting the impact of climate change on wheat grain yield in dryland environments[J]. Journal of Arid Land, 2023, 15(5): 545-561.
[9] Sakine KOOHI, Hadi RAMEZANI ETEDALI. Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset[J]. Journal of Arid Land, 2023, 15(4): 377-392.
[10] Mehri SHAMS GHAHFAROKHI, Sogol MORADIAN. Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?[J]. Journal of Arid Land, 2023, 15(4): 424-438.
[11] ZHANG Yixin, LI Peng, XU Guoce, MIN Zhiqiang, LI Qingshun, LI Zhanbin, WANG Bin, CHEN Yiting. Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process[J]. Journal of Arid Land, 2023, 15(4): 439-459.
[12] Adnan ABBAS, Asher S BHATTI, Safi ULLAH, Waheed ULLAH, Muhammad WASEEM, ZHAO Chengyi, DOU Xin, Gohar ALI. Projection of precipitation extremes over South Asia from CMIP6 GCMs[J]. Journal of Arid Land, 2023, 15(3): 274-296.
[13] ZHAO Lili, LI Lusheng, LI Yanbin, ZHONG Huayu, ZHANG Fang, ZHU Junzhen, DING Yibo. Monitoring vegetation drought in the nine major river basins of China based on a new developed Vegetation Drought Condition Index[J]. Journal of Arid Land, 2023, 15(12): 1421-1438.
[14] CAO Yijie, MA Yonggang, BAO Anming, CHANG Cun, LIU Tie. Evaluation of the water conservation function in the Ili River Delta of Central Asia based on the InVEST model[J]. Journal of Arid Land, 2023, 15(12): 1455-1473.
[15] YAN Xue, LI Lanhai. Spatiotemporal characteristics and influencing factors of ecosystem services in Central Asia[J]. Journal of Arid Land, 2023, 15(1): 1-19.