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

Crop water stress significantly reduces crop yield. Several studies have employed optical and remote-sensing methods to obtain nondamage monitoring crop water content to understand the agriculture drought process. In this paper, the spectral information (i.e., the canopy absorption feature at the 350–2500 nm band range) from the field experiments was used to estimate and identify the canopy water stress. Five different levels of water treatments exist in the spring wheat field in the semiarid regions of Loess Plateau, Northwest China. The hyperspectral reflectance, soil moisture content, soil water potential, canopy water content, amount of chlorophyll, leaf area index, and environmental parameters were measured. The relationship between canopy reflectance and canopy water content was analyzed at different water stress levels. In addition, various spectral indices were tested by measurements. Results showed that a high correlation exists in semiarid water index-1, semiarid water index-2, and red-edge normalized difference vegetation index, thus denoting that these indices can indicate water stress effectively. We can conclude that canopy reflectance can identify crop water stress and can be used to develop a certain index for monitoring agriculture drought.

[1]  R. Burgan.  Use of remotely sensed data for fire danger estimation , 1996 .

[2]  R. G. Brown,et al.  Estimating Leaf Water Content by Reflectance Measurements1 , 1971 .

[3]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[4]  Claudia M. Castaneda,et al.  Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .

[5]  J. Peñuelas,et al.  The reflectance at the 950–970 nm region as an indicator of plant water status , 1993 .

[6]  J. Eitel,et al.  Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. , 2006 .

[7]  Pablo J. Zarco-Tejada,et al.  Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale , 2005 .

[8]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[9]  Michael E. Schaepman,et al.  Estimating canopy water content using hyperspectral remote sensing data , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Qiming Qin,et al.  Estimating crop water stress with ETM+ NIR and SWIR data , 2008 .

[11]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[12]  N. M. Kelly,et al.  Spectral absorption features as indicators of water status in coast live oak ( Quercus agrifolia ) leaves , 2003 .

[13]  E. Davidson,et al.  Equivalent water thickness in savanna ecosystems: MODIS estimates based on ground and EO-1 Hyperion data , 2011 .

[14]  Anming Bao,et al.  Estimation of leaf water content in cotton by means of hyperspectral indices , 2013 .

[15]  Yunseop Kim,et al.  Hyperspectral image analysis for water stress detection of apple trees , 2011 .

[16]  Robert O. Green,et al.  Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS , 1997 .

[17]  A. Morte,et al.  Effect of drought stress on growth and water relations of the mycorrhizal association Helianthemum almeriense-Terfezia claveryi , 2000, Mycorrhiza.

[18]  S. Ustin,et al.  Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .

[19]  Q. Qin,et al.  A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index , 2007 .

[20]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[21]  Pablo J. Zarco-Tejada,et al.  Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery , 2010 .

[22]  Pol Coppin,et al.  Determining the water status of Satsuma mandarin trees [Citrus Unshiu Marcovitch] using spectral indices and by combining hyperspectral and physiological data , 2010 .

[23]  A. Goetz,et al.  Column atmospheric water vapor and vegetation liquid water retrievals from Airborne Imaging Spectrometer data , 1990 .

[24]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[25]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[26]  P. Gong,et al.  A feasibility study on diagnosing wheat water status using spectral reflectance , 2001 .

[27]  David Riaño,et al.  Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona , 2008 .

[28]  F. M. Danson,et al.  High-spectral resolution data for determining leaf water content , 1992 .

[29]  Peter R. J. North,et al.  The Propagation of Foliar Biochemical Absorption Features in Forest Canopy Reflectance , 1999 .

[30]  Gregory A. Carter,et al.  Responses of leaf spectral reflectance to plant stress. , 1993 .

[31]  C. V. M. Bartona,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index Model and sensitivity analysis , 2000 .

[32]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[33]  F. M. Danson,et al.  Spectral reflectance of dehydrating leaves: Measurements and modelling , 1997 .

[34]  T. Faurtyot.  Vegetation water and dry matter contents estimated from top-of-the-atmosphere reflectance data: A simulation study , 1997 .

[35]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[36]  M. Hardisky.  The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies , 2008 .

[37]  A. Gitelson,et al.  Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation , 1994 .

[38]  H. Gausman,et al.  Leaf Reflectance vs. Leaf Chlorophyll and Carotenoid Concentrations for Eight Crops1 , 1977 .

[39]  P. Zarco-Tejada,et al.  Modelling PRI for water stress detection using radiative transfer models , 2009 .

[40]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[41]  C. Giardino,et al.  Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling , 2008 .

[42]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .