Determining the Canopy Water Stress for Spring Wheat Using Canopy Hyperspectral Reflectance Data in Loess Plateau Semiarid Regions

References

• Xu, C. H.; Luo, Y.; Xu, Y. Simulation and prediction of the drought variations in China by multi-model ensemble. Journal of Glaciology and Geocryology 2010, 32(5), 867–874.
   Google Scholar
• Peñuelas, J.; Filella, I.; Biel, C. The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing 1993, 14, 1887–1905.
   Web of Science ®Google Scholar
• Abuwasit, G.; Li, Z. L.; Qin, Q. M. A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index. Science in China Series D 2007, 37(7), 957–965.
   Google Scholar
• Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment 2001, 77, 22–33.
   Web of Science ®Google Scholar
• Suárez, L.; Zarco-Tejada, P. J. Modeling PRI for water stress detection using radioactive transfer models. Remote Sensing of Environment 2009, 113, 730–744.
   Web of Science ®Google Scholar
• Suárez, L.; Zarco-Tejada, P. J. Detecting water stress effects on fruit quality in orchards with time series PRI airborne imagery. Remote Sensing of Environment 2010, 114, 286–298.
   Web of Science ®Google Scholar
• Yunseop, K.; David, M.; Glenn, J. Hyperspectral image analysis for water stress detection of apple trees. Computers and Electronics in Agriculture 2011, 77, 155–160.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J.; Rueda, C. A.; Ustin, S. L. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment 2003, 85, 109–124.
   Web of Science ®Google Scholar
• Abuwasit, G.; Li, Z. L.; Qin, Q. M. Estimation crop water stress with ETM+NIR and SWIR data. Agricultural and Forest Meteorology 2008, 148, 1679–1695.
   Web of Science ®Google Scholar
• Gao, B. C. NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996, 58, 257–266.
   Web of Science ®Google Scholar
• Peñuelas, J.; Piñol, J.; Ogaya, R. Estimate of plant water concentration by the reflectance water index WI(R900/970). International Journal of Remote Sensing 1997, 18, 2869–2875.
   Web of Science ®Google Scholar
• Colombo, R.; Meroni, M.; Marchesi, A. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sensing of Environment 2008, 112(4), 1820–1834.
   Web of Science ®Google Scholar
• Carter, G. A. Responses of leaf spectral reflectance to plant stresses. American Journal of Botany 1993, 80, 239–243.
   Web of Science ®Google Scholar
• Thomas, J. R.; Namken, J. L.; Oerther, G. F. Estimating leaf water content by reflectance measurements. Agronomy Journal 1971, 63, 845–847.
   Web of Science ®Google Scholar
• Pu, R.; Ge, S.; Kelly, N. M. Spectral absorption features and inditoes of water status in coast live oak (Quercus agrifolia) leaves. International Journal of Remote Sensing 2003, 24, 1799–1810.
   Web of Science ®Google Scholar
• Serrano, L.; Ustin, S. L.; Roberts, D. A. Deriving water content of Chaparral vegetation from AVIRIS data. Remote Sensing of Environment 2000, 74(3), 570–581.
   Web of Science ®Google Scholar
• Gao, B. C.; Goetz, F. H. Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. Journal of Geophysical Research 1990, 95, 3459–3564.
   Web of Science ®Google Scholar
• Roberts, D. A.; Green, R. O.; Adams, J. B. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sensing of Environment 1997, 62, 223–240.
   Web of Science ®Google Scholar
• Eitel, J. U.; Gessler, P. E.; Smith, A. M. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecology and Management 2006, 229, 170–182.
   Web of Science ®Google Scholar
• Hunt, E. R., Rock, B. N. Detection of changes in leaf water content using near and middle-infrared reflectance. Remote Sensing of Environment 1989, 30, 43–54.
   Web of Science ®Google Scholar
• Aldakheel, Y. Y.; Danson, F. M. Spectral reflectance of dehydration leaves: Measurements and modeling. International Journal of Remote Sensing 1997, 18, 3683–3690.
   Web of Science ®Google Scholar
• Fourty, T.; Baret, F. Vegetation water and dry matter contents estimated from top of the atmosphere reflectance data. Remote Sensing of Environment 1997, 61, 34–45.
   Web of Science ®Google Scholar
• Ustin, S. L.; Roberts, D. A.; Pinrzon, J. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sensing of Environment 1998, 65, 280–191.
   Web of Science ®Google Scholar
• Dawson, T. P.; Curran, P. R. J.; Plummer, S. E. The propagation of foliar biochemical absorption features in forest canopy reflectance: Theoretical analysis. Remote Sensing of Environment 1999, 67, 147–159.
   Web of Science ®Google Scholar
• Abuwasit, G.; Li, Z. L.; Qin, Q. M. Estimation crop water stress with ETM+NIR and SWIR data. Agricultural and Forest Meteorology 2008, 148, 1679–1695.
   Web of Science ®Google Scholar
• Hardisky, M. A.; Klemas, V.; Smart, R. M. The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 1983, 49, 77–83.
   Web of Science ®Google Scholar
• Dobrowski, S. Z.; Pushnik, J. C.; Zarco, T. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sensing of Environment 2005, 97, 403–414.
   Web of Science ®Google Scholar
• Gitelson, A.; Merzlyak, M. N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves: Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology 1994, 143, 286–292.
   Web of Science ®Google Scholar
• Burgan, R. E. Use of remotely sensed data for fire danger estimation. EARSeL Advances in Remote Sensing 1996, 4(4), 1–8.
   Google Scholar
• Danson, F. M.; Steven, M. D.; Malthus, T. J.; Clark, J. A. High-spectral resolution data for determining leaf water content. International Journal of Remote Sensing 1992, 13, 461–470.
   Web of Science ®Google Scholar
• Ceccato, P.; Gobron, N.; Flasse, S. Designing a spectral index to estimate vegetation water content from remote sensing data. Remote Sensing of Environment 2002, 82, 188–197.
   Web of Science ®Google Scholar
• Rouse, J. W.; Haas, R. H.; Schell, J. A.; Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. 3rd ERTS Symposium, NASA SP-351 I., 1973, 309–317.
   Google Scholar
• Gamon, J. A.; Peñuelas, J.; Field, C. B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 1992, 41, 34–44.
   Web of Science ®Google Scholar
• Rondeaux, G.; Steven, M.; Baret, F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 1996, 55(2), 95–107.
   Web of Science ®Google Scholar
• Ferreira, L. G.; Asner, G. P.; Knapp, D. E.; Davidson, E. A.; Coe, M.; Bustamante, M. M. C.; de Oliveira, E. L. Equivalent water thickness in savanna ecosystems: MODIS estimates based on ground and EO-1 Hyperion data. International Journal of Remote Sensing 2011, 32(22), 7423–7440.
   Web of Science ®Google Scholar
• Fang, J. Y.; Zhang, R. Z. Effects of mineral nutrition and water stress contents of chlorophyll and malondialdehyde of spring wheat and their correlation. Journal of Gansu Agricultural University 2001, 36(1), 89–94.
   Google Scholar
• Morte, A. Effect of drought stress on growth and water relations of the mycorrhizal association Helianthemum almeriense–Terfezia claveryi. Mycorrhiza 2000, 10(3), 115–119.
   Web of Science ®Google Scholar
• Clevers, J. G. P. W.; Kooistra, L.; Schaepman, M. E. Estimating canopy water content using hyperspectral remote sensing data. Journal of Applied Earth Observation and Geoinformation 2010, 2, 119–125.
   Web of Science ®Google Scholar
• Yi, Q. X.; Bao, A. M.; Wang, Q. Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and Electronics in Agriculture 2013, 90, 144–151.
   Web of Science ®Google Scholar
• Dzikiti, S.; Vwrreynne, J. S.; Stuckens, J. Determining the water status of Satsuma mandarin trees [Citrus unshiu Marcovich] using indices and by combining hyperspectral and physiological data. Agricultural and Forest Meteorology 2010, 150, 369–379.
   Web of Science ®Google Scholar
• Thomas, J. R.; Gausman, H. W. Leaf reflectance vs. leaf chlorophyll and carotenoid concentration for eight crops. Agronomy Journal 1977, 69, 799–802.
   Web of Science ®Google Scholar
• Tian, Q. J.; Gong, P.; Zhao, C. J. A feasibility study on diagnosing wheat water status using spectral reflectance. Chinese Science Bulletin 2001, 46(8), 666–669.
   Web of Science ®Google Scholar
• Dzikiti, S.; Verreynne, J. S.; Stuckens, J. Determining the water status of satsuma mandarim trees using spectral indices and by bombining hyperspectral and physiological data. Agricultural and Forest Meteorology 2010, 150, 369–379.
   Web of Science ®Google Scholar
• Cheng, Y. B.; Ustin, S. L.; Riano, D.; Vanderbilt, V. C. Water content estimation from hyperspectral images and MODIS indices in Southeastern Arizona. Remote Sensing of Environment 2008, 112(3), 363–374.
   Web of Science ®Google Scholar
• Barton, C. V. M.; North, P. R. J. Remote sensing of canopy light use efficiency using the photochemical reflectance index. Remote Sensing of Environment 2001, 78, 264–273.
   Web of Science ®Google Scholar