The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyper-spectral and simulated multi-spectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max (L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyper-spectral Imager and CM readings were collected at four or five different growth stages. The results indicated that single multi-spectral and hyper-spectral band or vegetation index could explain 64–86% and 73–88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2–4 broad-bands or 3–8 narrow-bands could explain 41–92% or 61–94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyper-spectral or multi-spectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly in-season site-specific N management in large continuous corn fields or at later stages in corn-soybean rotation fields.

Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn

Spectral reflectance (SR) indices (NDVI (R900 -R 680/R900 + R680); GNDVI (R780 -R 550/R780 + R550); and water index, WI (R900/R970)); and 6 chlorophyll indices (R740/R720, NDI = R750 − R705/R750 + R705, R780 − R710/R780 − R680 ,R 850 − R710/R850 − R680, mND = R750 -R 705/R750 + R705 -2 R445, and mSR = R750 - R445/R705 -R 445) were measured with a FieldSpec spectroradiometer (Analytical Spectral Devices, Boulder, CO) on bread wheat (Triticum aestivum L.) genotypes adapted to irrigated and drought conditions to establish their relationship with yield in field-grown plots. Bread wheat genotypes from the International Maize and Wheat Improvement Center (CIMMYT) were used for this study in 3 experiments: 8 genotypes in a trial representing historical progress in yield potential, and 3 pairs of near-isolines for Lr19, both of which were grown under well- watered conditions; and the third experiment included 20 drought tolerant advanced genotypes grown under moisture stress. These were grown during the 2000 and 2001 spring cycles in a temperate, high radiation environment in Obreg´ on, NW M´ exico. The 9 SR indices were determined during grain filling along with canopy temperature depression (CTD), flag leaf photosynthetic rate, and chlorophyll estimates using a SPAD meter. The relationship of SR indices with grain yield and biomass fitted best with a linear model. NDVI and GNDVI showed positive relationships with grain yield and biomass under well-irrigated conditions (r = 0.35-0.92), whereas NDVI showed a stronger association with yield under drought conditions (r = 0.54). The 6 chlorophyll indices showed significant association with yield and biomass of wheat genotypes grown under well-irrigated conditions (r = 0.39-0.90). The association between chlorophyll indices and chlorophyll estimates was correlated (r = 0.38-0.92), as was the case for photosynthetic rate (r = 0.36-0.75). WI showed a significant relationship with grain yield in wheat genotypes grown under drought stress conditions (r = 0.60) as well as with grain yield and biomass under well-irrigated conditions (r = 0.52-0.91). The relationship between WI and CTD was significant (P ≤ 0.05) in both environments (r = 0.44-0.84). In conclusion, the SR showed potential for identifying higher-yielding genotypes in a breeding program under dry or irrigated conditions, as well as for estimating some physiological parameters.

Association between canopy reflectance indices and yield and physiological traits in bread wheat under drought and well-irrigated conditions

Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon® EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5 month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130 kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPIb), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (NL) and canopy nitrogen content (NC). SRPIb showed the best correlation (R
 2 = 0.7) with NL, independently of the water and the N treatment. NDVI and GNDVI were best correlated with NC values with correlation coefficients of 0.7 and 0.64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPIb could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring.

A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring

Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance

Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal changes in biomass and nutritional quality of forage plants in the Arctic remains largely un-evaluated at landscape and fine-scale levels. We modeled the relationships between NDVI and seasonal changes in aboveground biomass and nitrogen concentration in halophytic graminoids, a key food source for arctic-nesting geese. The model was calibrated based on data collected at one site and validated using data from another site. Effects of spatial scale on model accuracy were determined by comparing model predictions between NDVI derived from moderate resolution (250 × 250 m pixels) satellite data and high resolution (20 cm diameter area) handheld spectrometer data. NDVI derived from the handheld spectrometer was a superior estimator (R2 ≥ 0.67) of seasonal changes in aboveground biomass compared to satellite-derived NDVI (R2 ≤ 0.40). The addition of temperature and precipitation variables to the model for biomass improved fit, but provided minor gains in predictive power beyond that of the NDVI-only model. This model, however, was only a moderately accurate estimator of biomass in an ecologically-similar halophytic graminoid wetland located 100 km away, indicating the necessity for site-specific validation. In contrast to assessments of biomass, satellite-derived NDVI was a better estimator for the timing of peak percent of nitrogen than NDVI derived from the handheld spectrometer. We confirmed that the date when NDVI reached 50% of its seasonal maximum was a reasonable approximation of the period of peak spring vegetative green-up and peak percent nitrogen. This study demonstrates the importance of matching the scale of NDVI measurements to the vegetation properties of biomass and nitrogen phenology.

https://www.mdpi.com/2072-4292/9/12/1234/pdf

Normalized Difference Vegetation Index as an Estimator for Abundance and Quality of Avian Herbivore Forage in Arctic Alaska

In–season site–specific nitrogen application has the potential to improve the efficiency of nitrogen use and reduce
environmental contamination. Identification of within–field crop nitrogen stress is essential for improved nitrogen
management. Corn nitrogen stress is frequently associated with leaf chlorophyll content, which can be characterized by
spectral reflectance measurements. Therefore, satellite remote sensing has potential for characterizing corn nitrogen
deficiency on a whole–field scale. To evaluate the use of satellite imagery to detect nitrogen deficiency in corn during the
growing season, this study compared spectral variables extracted from SPOT satellite imagery and digital aerial imagery,
and investigated the relationship between spectral variables of the SPOT imagery and the measurement data of a hand–held
chlorophyll meter (or SPAD meter). SPOT images and aerial images were acquired on one commercial field in 1999 and
another one in 2000. 
The correlation coefficient (r) between the NDVIs derived from the SPOT image and the aerial image was 0.73 in 1999,
and 0.54 in 2000, respectively. When the field had large spatial variability in crop development in 1999, the spectral variables
from the SPOT image were strongly correlated with those from the aerial image. However, when the crop development was
more uniform in 2000, the correlation between the SPOT image and the aerial image was not as good. The limited range of
available digital counts that can be used to represent the reflectance from an individual farm field for the SPOT system limited
its sensitivity in detecting crop stress. Nevertheless, the NDVIs from SPOT images were significantly correlated with SPAD
data in both fields, with correlation coefficients of 0.90 in 1999 and 0.68 in 2000. The correlation between SPOT images and
SPAD data was similar to that between aerial images and SPAD data, indicating that SPOT imagery may have potential for
detecting chlorophyll levels and nitrogen stress in corn during the growing season.

B. Ni

Papers

Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn