Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha−1 for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.