摘要 Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWTleaf), the fuel moisture content (FMC), the canopy equivalent water thickness (EWTcanopy) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWTleaf were much better than that with FMC, and the relationships between vegetation indices and EWTcanopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation indices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R2 of 0.262 and 0.306 for EWTleaf-WI and EWTcanopy-NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status measuring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.
Abstract: Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWTleaf), the fuel moisture content (FMC), the canopy equivalent water thickness (EWTcanopy) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWTleaf were much better than that with FMC, and the relationships between vegetation indices and EWTcanopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation indices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R2 of 0.262 and 0.306 for EWTleaf-WI and EWTcanopy-NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status measuring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.
West Light Foundation of Chinese Academy of Sciences (XBBS200902), the Knowledge Innovation Project of Chinese Academy of Sciences (KZCX2-YW-BR-12), the National Natural Science Foundation of China (41104130), the West Light Foundation of Chinese Academy of Sciences (XBBS201006), and the China Postdoctoral Science Foundation (20100471681).
通讯作者:
QiuXiang YI
E-mail: yiqx@ms.xjb.ac.cn
引用本文:
QiuXiang YI, AnMing BAO, Yi LUO, Jin ZHAO. Measuring cotton water status using water-related vegetation indices at leaf and canopy levels[J]. 干旱区科学, 2012, 4(3): 310-319.
QiuXiang YI, AnMing BAO, Yi LUO, Jin ZHAO. Measuring cotton water status using water-related vegetation indices at leaf and canopy levels. Journal of Arid Land, 2012, 4(3): 310-319.
Ahmad S, Simonovic S P. 2005. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology, 315(1–4): 236–251.Boyer J S. 1995. Measuring the Water Status of Plants and Soils. Beijing: Academic Press, 56–64. Carlson J D, Burgan R E. 2003. Review of user needs in operational fire danger estimation: the Oklahoma example. International Journal of Remote Sensing, 24: 1601–1620.Carter G A. 1993. Responses of leaf spectral reflectance to plant stress. American Journal of Botany, 80: 239–243.Ceccato P, Gobron N, Flasse S, et al. 2002a. Designing a spectral index to estimate vegetation water content fromremote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment, 82: 88–197.Ceccato P, Flasse S, Gregoire J M. 2002b. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sensing of Environment, 82: 98–207.Chen D Y, Huang J F, Thomas J J. 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sensing of Environment, 98: 225–236.Danson F M, Steven M D, Malthus T J, et al. 1992. Highspectral resolution data for determining leaf water content. International Journal of Remote Sensing, 13: 461–470.Datt B. 1999. Remote sensing of water content in Eucalyptus leaves. Australian Journal of Botany, 47: 909–923.Davidson A, Wang S, Wilmshurst J. 2006. Remote sensing of grassland-shrubland vegetation water content in the shortwave do-main. International Journal of Applied Earth Observation and Geoinformation, 8: 225–236.Despagne F, Massart D L. 1998. Neural networks in multivariate calibration. Analyst, 123: 157–178.Diker K, Bausch W C. 2003. Potential use of nitrogen reflectance index to estimate plant parameters and yield of maize. Biosystems Engineering, 85: 437–447. Gao B C, Goetz F H. 1995. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sensing of Environment, 52: 155–162.Gao B C. 1996. NDWI–a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58: 257–266.Gorr W L, Nagin D, Szczypula J. 1994. Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting, 10: 17–34.Hardisky M A, Klemas V, Smart R M. 1983. The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering & Remote Sensing, 49: 77–83.Hinzman L D, Bauer M E, Daughtry C S T. 1986. Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote Sensing of Environment, 19: 47–61.Hsu K I, Gupta H V, Sorooshian S. 1995. Artificial neural network modeling of the rainfall–runoff process. Water Resources Research, 31: 2517–2530.Hunt E R. 1991. Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. International Journal of Remote Sensing, 12: 643–649.Jackson T J, Schmugge T J, Wang J R. 1982. Passive microwave remote sensing of soil moisture under vegetation canopies. Water Resources Research, 18: 1137–1142.Jacquemoud S, Baret F, Andrieu B, et al. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT+SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sensing of Environment, 52: 163–172.Keiner L E, Yan X. 1998. A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote Sensing of Environment, 66: 153–165.Kimes D S, Markham B L, Tucker C J. 1981. Temporal relationships between spectral response and agronomic variables of a corn canopy. Remote Sensing of Environment, 11: 401–411.Kumar U A. 2005. Comparison of neural networks and regression analysis: a new insight. Expert Systems with Applications, 29: 424–430.Maier H R, Dandy G C. 1996. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research, 32: 1013–1022.Marta Y, Emilio C, David R. 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, 148: 523–536.McMurtrey III R F, Chappella E W, Kim M S, et al. 1994. Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with active induced fluoresing and passive reflectance measurements. Remote Sensing of Environment, 47: 36–44. Paltridge G W, Barber J. 1988. Monitoring grassland dryness and fire potential in Australia with NOAA/AVHRR data. Remote Sensing of Environment, 25: 381–394.Peñuelas J, Filella I, Biel C, et al. 1993. The reflectance at the 950– 970 nm region as an indicator of plant water status. International Journal of Remote Sensing, 14: 1887–1905.Peñuelas J, Gamon J A, Fredeen A L, et al. 1994. Reflectance indices associated with physiological changes in nitrogen and water limited sunflower leaves. Remote Sensing of Environment, 48: 135–146.Peñuelas J, Pinol J, Ogaya R, et al. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 14: 1887–1905.Riaño D, Vaughan P, Chuvieco E, et al. 2005. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Transactions on Geoscience and Remote Sensing, 43: 819–826.Rock B N, Vogelmann J E, Williams D L, et al. 1986. Remote detection of forest damage. Journal of Bioscience, 36: 439–445.Rouse J W, Haas R H, Schell J A, et al. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium. Greenbelt: NASA SP-351, 301–307.Sepulcre-CantóG, Zarco-Tejada P J, Jiménez-Muñoz J C, et al. 2006. Detection of water stress in an olive orchard with thermal remote sensing imagery. Agricultural and Forest Meteorology, 136: 31–44.Tian Q, Tong Q, Pu R, et al. 2001. Spectroscopic determinations of wheat water status using 1650–1850 nm spectral absorption features. International Journal of Remote Sensing, 22: 2329–2338.Twarakavi N K C, Mishra D, Bandopadhyay S. 2006. Prediction of arsenic in bedrock derived stream sediments at a gold mine site under conditions of sparse data. Natural Resources Research, 15(1): 15–26.Viegas D X, Viegas T P, Ferreira A D. 1992. Moisture content of fine forest fuels and fire occurrence in central Portugal. International Journal of Wildland Fire, 2: 69–85.Yilmaz M T, Hunt Jr E R, Jackson T J. 2008. Remote sensing of vegetation water content from equivalent water thickness using satellite imagery. Remote Sensing of Environment, 112: 2514–2522.Zarco-Tejada P J, Miller J R, Noland T L, et al. 2001. Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39: 1491–1507.Zarco-Tejada P J, Rueda C A, Ustin S L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85: 109–124.
2012, Vol. 4 
