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Title: MONITORING OF SPRING WHEAT CROP ROTATION AND WEED INFESTATION ESTIMATION USING MODIS

Author
item SULTANGAZIN, U - SPACE RESEARCH INSTITUE
item MURATOVA, N - SPACE RESEARCH INSTITUTE
item Doraiswamy, Paul
item TEREKHOV, A - SPACE RESEARCH INSTITUTE

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 8/14/2004
Publication Date: 9/20/2004
Citation: Sultangazin, U., Muratova, N., Doraiswamy, P.C., Terekhov, A. 2004. Monitoring of spring wheat crop rotation and weed infestation estimation using MODIS. In: Proceedings of the International Geoscience and Remote Sensing Symposium, September 20-24, 2004, Anchorage, Alaska. p. 4066-4069.

Interpretive Summary:

Technical Abstract: Spring wheat production in Northern Kazakhstan accounts for one of the most extensive areas in the world. Weed infestation is a major problem because farmers cannot afford to apply herbicides over large areas. Wild oats is the major weed population and the use of standard classification procedures are not applicable to separate the weed population from the spring wheat crop. The common cultural practice to minimize weed population is to keep fields in fallow for several years and tilling the soil regularly. The objective of this research was to develop an indirect method to classify the weed infestation and its intensity using remote sensing techniques.' Multi-year land use inventory was used to study the number of years a field was left fallow before cultivating spring wheat crop. Multi-year MODIS reflectance data (band 2, 250 m resolution) was used for spring wheat classification and tracking of fallow fields. Based on the number of years a field was left fallow, four levels of weed infestation were developed. Ground surveys were conducted to define the weed population in spring wheat fields and to determine weed infestation levels. It was demonstrated that this approach offers a potential tool to estimate the weed population and intensity for croplands in Northern Kazakhstan. Result of this research will be applied for assessment reduction in grain production.