*

Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

Quan Wang (1)   , Jia Jin (2-3)

iForest - Biogeosciences and Forestry, Volume 9, Issue 1, Pages 30-37 (2015)
doi: https://doi.org/10.3832/ifor1634-008
Published: Oct 05, 2015 - Copyright © 2015 SISEF

Research Articles


A clear understanding of plant transpiration is a crucial step for water cycle and climate modeling, especially for arid ecosystems in which water is one of the major constraints. Traditional field measurements of leaf scale transpiration are always time-consuming and often unfeasible in the context of large spatial and temporal scales. This study focused on a dominant native plant in the arid land of central Asia, Haloxylon ammondendron, with the aim of deriving the leaf-scale transpiration through hyperspectral reflectance using Partial Least Squares Regression (PLSR) analysis. The results revealed that the PLSR model based on the first-order derivative spectra at wavelengths selected through stepwise regression analysis can closely trace leaf transpiration with a high accuracy (R2 = 0.78, RMSE = 1.62 µmol g-1 s-1). The accuracy is also relatively stable even at a spectral resolution of 10 nm, which is very close to the bandwidths of several running satellite-borne hyperspectral sensors such as Hyperion. The results also proved that the first-order derivative spectra within the shortwave infrared (SWIR) domain, especially at 2435, 2440, 2445, and 2470 nm, were critical for PLSR models to predict leaf transpiration. These findings highlight a promising strategy for developing remote sensing methods to potentially characterize transpiration at broad scales.

  Keywords


Arid Land, Leaf Transpiration, PLSR, Derivative Spectra, Drought-tolerant, Haloxylon ammondendron

Authors’ address

(1)
Quan Wang
Graduate School of Agriculture, Shizuoka University, Shizuoka 422-8529 (Japan)
(2)
Jia Jin
Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011 (China)
(3)
Jia Jin
University of Chinese Academy of Sciences, Beijing 100049 (China)

Corresponding author

Citation

Wang Q, Jin J (2015). Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis. iForest 9: 30-37. - doi: 10.3832/ifor1634-008

Academic Editor

Davide Travaglini

Paper history

Received: Mar 05, 2015
Accepted: Aug 06, 2015

First online: Oct 05, 2015
Publication Date: Feb 21, 2016
Publication Time: 2.00 months

Breakdown by View Type

(Waiting for server response...)

Article Usage

Total Article Views: 11357
(from publication date up to now)

Breakdown by View Type
HTML Page Views: 6737
Abstract Page Views: 319
PDF Downloads: 3768
Citation/Reference Downloads: 24
XML Downloads: 509

Web Metrics
Days since publication: 1531
Overall contacts: 11357
Avg. contacts per week: 51.93

Article Citations

Article citations are based on data periodically collected from the Clarivate Web of Science web site
(last update: Aug 2019)

Total number of cites (since 2016): 2
Average cites per year: 0.50

 

Publication Metrics

by Dimensions ©

Articles citing this article

List of the papers citing this article based on CrossRef Cited-by.

 
(1)
Abdel-Rahman EM, Ahmed FB, Ismail R (2012)
Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. International Journal of Remote Sensing 34: 712-728.
CrossRef | Gscholar
(2)
Abdel-Rahman EM, Mutanga O, Odindi J, Adam E, Odindo A, Ismail R (2014)
A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data. Computers and Electronics in Agriculture 106: 11-19.
CrossRef | Gscholar
(3)
Ahlrichs JS, Bauer ME (1983)
Relation of agronomic and multispectral reflectance characteristics of spring wheat canopies. Agronomy Journal 75: 987-993.
CrossRef | Gscholar
(4)
Ansley R, Jacoby P, Hicks R (1991)
Leaf and whole plant transpiration in honey mesquite following severing of lateral roots. Journal of Range Management 44 (6): 577.
CrossRef | Gscholar
(5)
Baret F, Guyot G, Begue A, Maurel P, Podaire A (1988)
Complementarity of middle-infrared with visible and near-infrared reflectance for monitoring wheat canopies. Remote Sensing of Environment 26: 213-225.
CrossRef | Gscholar
(6)
Carter GA (1994)
Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing 15: 697-703.
CrossRef | Gscholar
(7)
Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire JM (2001)
Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 77: 22-33.
CrossRef | Gscholar
(8)
Chappelle EW, Kim MS, McMurtrey JE (1992)
Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment 39: 239-247.
CrossRef | Gscholar
(9)
Chen S, Hong X, Harris CJ, Sharkey PM (2004)
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Transactions on Systems, Man, and Cybernetic, Part B: Cybernetics 34: 898-911.
CrossRef | Gscholar
(10)
Cho MA, Skidmore A, Corsi F, Van Wieren SE, Sobhan I (2007)
Estimation of green grass/ herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation 9: 414-424.
CrossRef | Gscholar
(11)
Chuvieco E, Riaño D, Aguado I, Cocero D (2002)
Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing 23: 2145-2162.
CrossRef | Gscholar
(12)
Curran P (1994)
Imaging spectrometry - its present and future role in environmental researc. In: “Imaging Spectrometry - a Tool for Environmental Observations” (Hill J, Mégier J eds). Springer, The Netherlands, pp. 1-23.
Online | Gscholar
(13)
Dauzat J, Rapidel B, Berger A (2001)
Simulation of leaf transpiration and sap flow in virtual plants: model description and application to a coffee plantation in Costa Rica. Agricultural and Forest Meteorology 109: 143-160.
CrossRef | Gscholar
(14)
Demetriades-Shah TH, Steven MD, Clark JA (1990)
High resolution derivative spectra in remote sensing. Remote Sensing of Environment 33: 55-64.
CrossRef | Gscholar
(15)
Dorigo WA, Zurita-Milla R, De Wit AJW, Brazile J, Singh R, Schaepman ME (2007)
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation 9: 165-193.
CrossRef | Gscholar
(16)
Doughty CE, Asner GP, Martin RE (2011)
Predicting tropical plant physiology from leaf and canopy spectroscopy. Oecologia 165: 289-299.
CrossRef | Gscholar
(17)
Fu Y, Yang G, Wang J, Song X, Feng H (2014)
Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Computers and Electronics in Agriculture 100: 51-59.
CrossRef | Gscholar
(18)
Hansen PM, Schjoerring JK (2003)
Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86: 542-553.
CrossRef | Gscholar
(19)
He D, Liu Y, Pan Z, An P, Wang L, Dong Z, Zhang J, Pan X, Zhao P (2013)
Climate change and its effect on reference crop evapotranspiration in central and western Inner Mongolia during 1961-2009. Frontiers of Earth Science 7: 417-428.
CrossRef | Gscholar
(20)
Huang Z, Turner BJ, Dury SJ, Wallis IR, Foley WJ (2004)
Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment 93: 18-29.
CrossRef | Gscholar
(21)
Hunsmann S, Wunderle K, Wagner S, Rascher U, Schurr U, Ebert V (2008)
Absolute, high resolution water transpiration rate measurements on single plant leaves via tunable diode laser absorption spectroscopy (TDLAS) at 1. 37 μm. Applied Physics B 92: 393-401.
CrossRef | Gscholar
(22)
Imanishi J, Sugimoto K, Morimoto Y (2004)
Detecting drought status and LAI of two Quercus species canopies using derivative spectra. Computers and Electronics in Agriculture 43: 109-129.
CrossRef | Gscholar
(23)
Inoue Y, Sakaiya E, Zhu Y, Takahashi W (2012)
Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sensing of Environment 126: 210-221.
CrossRef | Gscholar
(24)
Jarvis P, Catsky J (1971)
Chamber microclimate and principles of assimilation chamber design. In: “Plant Photosynthetic Production. Manual of Methods”. Dr. W. Junk N.V. Publishers, The Hague, The Netherlands, pp. 59-77.
Gscholar
(25)
Jennrich RI (1977)
Stepwise regression. In: “Statistical methods for digital computers” (Enslein K, Ralston A, Wilf HS eds). Wiley, New York, USA, pp. 58-75.
Gscholar
(26)
Knipling EB (1970)
Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1: 155-159.
CrossRef | Gscholar
(27)
Kochubey SM, Kazantsev TA (2007)
Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology 164: 1648-1655.
CrossRef | Gscholar
(28)
Kumar L, Schmidt K, Dury S, Skidmore A (2002)
Imaging spectrometry and vegetation science. In: “Imaging Spectrometry”. Springer Science+Business Media BV, The Netherlands, pp. 111-155.
CrossRef | Gscholar
(29)
Li P, Wang Q (2012)
Retrieval of chlorophyll for assimilating branches of a typical desert plant through inversed radiative transfer models. International Journal of Remote Sensing 34: 2402-2416.
CrossRef | Gscholar
(30)
Li Z, Li R (1981)
Anatomical observation of assimilating branches of nine xerophytes in Gansu. Acta Botanica Sinica 23: 181-185.
Online | Gscholar
(31)
Marino G, Pallozzi E, Cocozza C, Tognetti R, Giovannelli A, Cantini C, Centritto M (2014)
Assessing gas exchange, sap flow and water relations using tree canopy spectral reflectance indices in irrigated and rainfed Olea europaea L. Environmental and Experimental Botany 99: 43-52.
CrossRef | Gscholar
(32)
McDowell NG, White S, Pockman WT (2008)
Transpiration and stomatal conductance across a steep climate gradient in the southern Rocky Mountains. Ecohydrology 1: 193-204.
CrossRef | Gscholar
(33)
Monteith J (1965)
Evaporation and environment. Symposia of the Society for Experimental Biology 19: 205-224.
Online | Gscholar
(34)
Mutanga O, Skidmore AK, Prins HHT (2004)
Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sensing of Environment 89: 393-408.
CrossRef | Gscholar
(35)
Naithani KJ, Ewers BE, Pendall E (2012)
Sap flux-scaled transpiration and stomatal conductance response to soil and atmospheric drought in a semi-arid sagebrush ecosystem. Journal of Hydrology 464-465: 176-185.
CrossRef | Gscholar
(36)
Nguyen HT, Lee BW (2006)
Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy 24: 349-356.
CrossRef | Gscholar
(37)
Osborne SD, Künnemeyer R, Jordan RB (1997)
Method of wavelength selection for partial least squares. Analyst 122: 1531-1537.
CrossRef | Gscholar
(38)
Panigrahy RK, Ray S, Panigrahy S (2009)
Study on the utility of IRS-P6 AWIFS SWIR band for crop discrimination and classification. Journal of Indian Society of Remote Sensing 37: 325-333.
CrossRef | Gscholar
(39)
Pearcy R, Schulze ED, Zimmermann R (1989)
Measurement of transpiration and leaf conductance. In: “Plant Physiological Ecology” (Pearcy R, Ehleringer J, Mooney H, Rundel P eds). Springer, The Netherlands, pp. 137-160.
CrossRef | Gscholar
(40)
Peñuelas J, Filella I (1998)
Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science 3: 151-156.
CrossRef | Gscholar
(41)
Rady AM, Guyer DE, Kirk W, Donis-González IR (2014)
The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes. Journal of Food Engineering 135: 11-25.
CrossRef | Gscholar
(42)
Ramoelo A, Skidmore AK, Cho MA, Mathieu R, Heitkönig IMA, Dudeni-Tlhone N, Schlerf M, Prins HHT (2013)
Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS Journal of Photogrammetry and Remote Sensing 82: 27-40.
CrossRef | Gscholar
(43)
Raupach M, Finnigan J (1988)
Single-layer models of evaporation from plant canopies are incorrect but useful, whereas multilayer models are correct but useless: discussion. Functional Plant Biology 15: 705-716.
Online | Gscholar
(44)
Raymond Hunt E, Rock BN, Nobel PS (1987)
Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment 22 (3): 429-435.
CrossRef | Gscholar
(45)
Ryu C, Suguri M, Umeda M (2009)
Model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing. Biosystems Engineering 104: 465-475.
CrossRef | Gscholar
(46)
Ryu C, Suguri M, Umeda M (2011)
Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing. Field Crops Research 122: 214-224.
CrossRef | Gscholar
(47)
Serbin SP, Dillaway DN, Kruger EL, Townsend PA (2012)
Leaf optical properties reflect variation in photosynthetic metabolism and its sensitivity to temperature. Journal of Experimental Botany 63: 489-502.
CrossRef | Gscholar
(48)
Shao J (1993)
Linear model selection by cross-validation. Journal of the American Statistical Association 88: 486-494.
CrossRef | Gscholar
(49)
Smith W, Geller G (1980)
Leaf and environmental parameters influencing transpiration: theory and field measurements. Oecologia 46: 308-313.
CrossRef | Gscholar
(50)
Tian Q, Tong Q, Pu R, Guo X, Zhao C (2001)
Spectroscopic determination of wheat water status using 1650-1850 nm spectral absorption features. International Journal of Remote Sensing 22: 2329-2338.
CrossRef | Gscholar
(51)
Tucker CJ (1980)
Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment 10: 23-32.
CrossRef | Gscholar
(52)
Wold S, Sjöström M, Eriksson L (2001)
PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58: 109-130.
CrossRef | Gscholar
(53)
Yao X, Ren H, Cao Z, Tian Y, Cao W, Zhu Y, Cheng T (2014)
Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds. International Journal of Applied Earth Observations and Geoinformation 32: 114-124.
CrossRef | Gscholar
(54)
Yi Q, Jiapaer G, Chen J, Bao A, Wang F (2014)
Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression. ISPRS Journal of Photogrammetry and Remote Sensing 91: 72-84.
CrossRef | Gscholar
(55)
Zhang X, Liu F, He Y, Gong X (2013)
Detecting macronutrients content and distribution in oilseed rape leaves based on hyperspectral imaging. Biosystems Engineering 115: 56-65.
CrossRef | Gscholar
(56)
Zhao D, Reddy KR, Kakani VG, Read JJ, Koti S (2007)
Canopy reflectance in cotton for growth assessment and lint yield prediction. European Journal of Agronomy 26: 335-344.
CrossRef | Gscholar
(57)
Zheng C, Wang Q (2014)
Spatiotemporal variations of reference evapotranspiration in recent five decades in the arid land of Northwestern China. Hydrological Processes 28 (25): 6124-6134.
CrossRef | Gscholar
 

This website uses cookies to ensure you get the best experience on our website