A spectral index to monitor the head-emergence of wheat in semi-arid conditions

Abstract Harvesting wheat (Triticium aestivum L.) for forage or leaving it for grain is the main decision uncertainty growers face in semi-arid regions during mid-season. To facilitate decision-making, a decision support system (DSS) has recently been proposed that requires information about crop water and nutritional status during spike emergence. Though remote sensing has been used to provide site-specific crop status information, a spectral vegetation index is needed to ensure that the information has been acquired during spike emergence. The objective of this study was to propose a spectral index sensitive to spike emergence and validate its suitability across different commercial farm fields by using ground spectral measurements and multispectral satellite imagery. To develop the index, controlled experiments with commonly grown wheat varieties were conducted during the 2004/2005 and 2005/2006 growing season in the agricultural area of the northern Negev desert of Israel. The experiments showed that spike emergence correlated most strongly (r = 0.7, p

[1]  Sean B. Eom,et al.  A survey of decision support system applications (1988–1994) , 1998, J. Oper. Res. Soc..

[2]  Anatoly A. Gitelson,et al.  Monitoring Maize (Zea mays L.) Phenology with Remote Sensing , 2004 .

[3]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[4]  D. Bonfil,et al.  Wheat Grain Yield and Soil Profile Water Distribution in a No-Till Arid Environment , 1999 .

[5]  Eyal Ben-Dor,et al.  A NEW MODEL-DRIVEN CORRECTION FACTOR FOR BRDF EFFECTS IN HRS DATA , 2005 .

[6]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

[7]  A. Fischer A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters , 1994 .

[8]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[9]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[10]  Daniel Schläpfer,et al.  1st EARSEL Workshop on Imaging Spectroscopy , 1998 .

[11]  Arnon Karnieli,et al.  Decision support system for improving wheat grain quality in the Mediterranean area of Israel , 2004 .

[12]  J. R. Jensen Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

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

[14]  Jerome J. Workman,et al.  Near-infrared spectroscopy in agriculture , 2004 .

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

[16]  M. Flowers,et al.  Remote Sensing of Winter Wheat Tiller Density for Early Nitrogen Application Decisions , 2001 .

[17]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[18]  H. Macpherson,et al.  Bread wheat: improvement and production. , 2002 .

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

[20]  J. Bouma,et al.  Future Directions of Precision Agriculture , 2005, Precision Agriculture.

[21]  Arnon Karnieli,et al.  Wheat and maize monitoring based on ground spectral measurements and multivariate data analysis , 2007 .

[22]  D. Bonfil,et al.  Decision support system for improving wheat quality in semi-arid regions. , 2004 .

[23]  Christopher B. Field,et al.  Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance , 1993 .

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

[25]  E. Kim,et al.  A survey of decision support system applications (1995–2001) , 2006, J. Oper. Res. Soc..

[26]  J. Zadoks A decimal code for the growth stages of cereals , 1974 .

[27]  E. J. Milton,et al.  Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Content Information , 1998 .

[28]  T. Kobayashi,et al.  Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. , 2001, Phytopathology.

[29]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[30]  Sang M. Lee,et al.  A Survey of Decision Support System Applications (1971–April 1988) , 1990 .

[31]  Graham Pervan,et al.  A critical analysis of decision support systems research , 2005, J. Inf. Technol..

[32]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[33]  D. Long,et al.  Method for Precision Nitrogen Management in Spring Wheat: II. Implementation , 2000, Precision Agriculture.