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 < 0.05) with spectral changes near the 1.2 μm water absorption feature in contrast to the band at 1.1 μm which appeared to be only weakly correlated. Thus, the spike emergence sensitive band at 1.2 μm has been combined with the insensitive band at 1.1 μm as reference to form the ratio-based normalized heading index (NHI). Experimental data were then used to establish an index threshold that helps separate data acquired before and after spike emergence. The proposed NHI was able to identify spike emergence with a classification accuracy varying between 53 and 83%. Accuracy was influenced by season, and whether narrow or broad spectral bands were used. Validation of the index in commercial farm fields in Israel and the United States showed that the classification accuracy was similar for ground spectral measurements and the advanced land imager (ALI) satellite imagery. These results suggest that the NHI is suited for identifying the onset of heading throughout wheat-growing areas without the need for characterizing seasonal trends.
- Advanced land imager (ALI) satellite images
- Canopy reflectance
- Decision support system (DSS)
- Normalized heading index (NHI)
- Vegetation index
- Water status