Abstract
Numerous efforts have been made to develop various indices using remote sensing data such as normalized differencevegetation index (NDVI), and vegetation condition index (VCI) for mapping and monitoring of yield estimating andassessment of vegetation health and productivity. NDVI and other indices that derive from satellite images are valuablesources of information for the estimation and prediction of crop conditions. In the present paper, NDVI data ofDasht-e-Naz in Iran in 2006 have been considered for crop yield assessment and estimating. The results showed thatthere is acceptable relationship between NDVI and Soya plant population. The correlation between NDVI and plantpopulation in high plant population area of field was (R2=0.923) and for low plant population area was (R2=0.249). Thecrop population models were discussed about high and low plant population in the present paper and could improve infuture with the use of long period dataset. Similar model can be developed for different crops of other locations.
Highlights
Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), and vegetation condition index (VCI) for mapping and monitoring of yield estimating and assessment of vegetation health and productivity
This study employed NDVI to examine the relationship between Soya plant population and the Normalized Difference Vegetation Index (NDVI) in the Dasht-e-Naz agri-industry
The results showed that estimation of plant population in accumulated fileds were more accurate than fileds with low plant population because in high plant population fileds there was good relationship between the bands of satellite images
Summary
Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), and vegetation condition index (VCI) for mapping and monitoring of yield estimating and assessment of vegetation health and productivity. NDVI and other indices that derive from satellite images are valuable sources of information for the estimation and prediction of crop conditions. Introduction Precision farming is a new agricultural system concept with the goals of optimizing returns in agricultural production and environment This concept involves the development and adoption of remote sensing (Barnes et al, 1996), Geographic Information System (GIS) technology applications, and knowledge-based technical management systems (NRC, 1997). More correlation between satellite image bands can show more information about satellite image (Drost et al, 1997)
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