Abstract
In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.
Highlights
1.1 General InstructionsThe Nitrogen concentration is one of the most important N nutrient diagnosis indicators of plants, which the plant growth, yield, and quality have a close relation with; it is often positively associated with leaf chlorophyll content and photosynthetic capacity (Stroppiana et al, 2009; Li et al, 2014b)
Because Normalized difference vegetation index (NDVI) saturates at moderate to high canopy coverage conditions, green waveband substituted the red region and incorporated into the normalized vegetation index, the Green normalized difference vegetation index (GNDVI)-based model didn’t perform significantly better than the aforementioned vegetation indices (Fig 2)
The result showed that Normalized difference red edge index (NDRE), Red edge position index (REP) and TCARI/OSAVI which performed well in estimating crop nitrogen concentration (Fig 3) while lower R2 values were generated by the other red-edge vegetation indices (CIred edge, MERIS terrestrial chlorophyll index (MTCI) and MCARI/OSAVI)
Summary
1.1 General InstructionsThe Nitrogen concentration is one of the most important N nutrient diagnosis indicators of plants, which the plant growth, yield, and quality have a close relation with; it is often positively associated with leaf chlorophyll content and photosynthetic capacity (Stroppiana et al, 2009; Li et al, 2014b). One of them is a physically-based method, which uses models that simulate reflectance spectra from leaf biochemical parameters and vice versa (Darvishzadeh et al, 2012). This approach generally involves the “ill-posed” inverse problem for lack of sufficient prior knowledge. An alternative approach is the empirical retrieval methods, which typically consist of relating the nitrogen concentration against spectral vegetation indices (SVIs) through linear or nonlinear algorithmic techniques (Kanke et al, 2012). The red edge-based SVIs showed promising potential for estimating the nitrogen concentration in many studies, but they are mostly constructed by using field hyperspectral reflectance.
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