Hyperspectral imagery vegetation index and temporal analysis for corn yield estimation

Aerial hyperspectral imagery has been used to find the temporal relationship between image and corn yield. A total of five hyperspectral images were taken during the growing season. For each image, the optimal vegetation index was selected among many candidate vegetation indices. At the same time, the optimal band subset was selected to calculate the vegetation index. The optimal band subset has the minimum number of bands and represents the most significant image bands (or wavelength) for yield prediction. The optimization process used the EAVI (Evolutionary Algorithm based Vegetation Index generation) algorithm. Results showed that the EAVI algorithm generated the best vegetation index among many comparison indices for yield estimation. For image taken at different date, the algorithm selected a different optimal vegetation index and image bands. The most common sensitive wavelength identified was in the red edge at 700 nm and in the NIR region at 826 nm. This study showed that images taken from the beginning of full canopy coverage to the corn ear formation period provided the best and stable result for corn yield estimation. It is suggested that this period of time during the growing season would have great potential for remote sensing based corn yield prediction.

[1]  M. S. Rasmussen.  Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability , 1997 .

[2]  A. Huete,et al.  A review of vegetation indices , 1995 .

[3]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[4]  J. Stafford,et al.  Hyperspectral image feature extraction and classification for soil nutrient mapping. , 2003 .

[5]  Shiv O. Prasher,et al.  Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn , 2003 .

[6]  Lei Tian,et al.  IN-FIELD VARIABILITY DETECTION AND SPATIAL YIELD MODELING FOR CORN USING DIGITAL AERIAL IMAGING , 1999 .

[7]  Chenghai Yang,et al.  Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum , 2002, Precision Agriculture.

[8]  Randal K. Taylor,et al.  USING YIELD MONITOR DATA TO DETERMINE SPATI AL CROP PRODUCTION POTENTIAL , 2001 .

[9]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[10]  H. Yao.  Hyperspectral Imaging System Optimization and Image Processing , 2001 .

[11]  Lei Tian,et al.  Corn Canopy Reflectance Study With A Real-Time High-Density Spectral-Image Mapping System , 2002 .

[12]  Dale F. Heermann,et al.  Monitoring Temporal Changes Of Irrigated Corn By Aerial Images , 2001 .

[13]  N. Kalra,et al.  Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices , 1993 .

[14]  Chenghai Yang,et al.  Mapping grain sorghum growth and yield variations using airborne multispectral digital imagery. , 2000 .

[15]  G. Carter,et al.  Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands , 1994 .

[16]  Lei Tian,et al.  A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction , 2003, IEEE Trans. Geosci. Remote. Sens..

[17]  C. Yang,et al.  Airborne multispectral imagery for mapping variable growing conditions and yields of cotton, grain sorghum, and corn , 2001 .

[18]  J. C. Price,et al.  Spectral band selection for visible-near infrared remote sensing: spectral-spatial resolution tradeoffs , 1997, IEEE Trans. Geosci. Remote. Sens..