GeoEye-1 satellite versus ground-based multispectral data for estimating nitrogen status of turfgrasses

Satellite remote sensing of leaf nitrogen (N) content is an interesting technique for agricultural crops for both economic and environmental reasons since it allows the monitoring of fertilization, and hence can potentially reduce the application of N according to real plant needs. The objective of this trial was to compare the N status in different turfgrasses using both remote multispectral data acquired by GeoEye-1 satellite and two ground-based instruments. The study focused on creating a N content gradient on three warm-season turfgrasses, (Cynodon dactylon x transvaalensis 'Patriot', Paspalum vaginatum 'Salam', Zoysia matrella 'Zeon'), and two cool-season (Festuca arundinacea 'Grande', Lolium perenne 'Regal 5'). The linear gradient of applied N ranged from 0 to 342 kg ha(-1) for the warm-season and from 0 to 190 kg ha(-1) for the cool-season turfgrasses. Proximity and remote-sensed reflectance measurements were acquired and used to determine the normalized difference vegetation index (NDVI). Our results proved that proximity-sensed NDVI is highly correlated with data acquired from satellite imagery. The correlation coefficients between data from the satellite and the other sensors ranged from 0.90 to 0.99 for the warm-season and from 0.83 to 0.97 for the cool-season species. 'Patriot' had a clippings N content ranging from 1.20% to 4.1%, thus emerging as the most reactive species to N fertilization. As such, the GeoEye-1 satellite can adequately assess the N status of different turfgrass species and its spatial variability within a field, depending on the N rates applied. In future, information obtained from satellites could allow precision fertilizer management on sports fields, golf courses, or other extended green areas.