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a Dep. of Soil Science, North Carolina State Univ., Raleigh, NC 27695-7619
b Dep. of Crop Science, Vernon James Res. and Ext. Center, 207 Research Rd., Plymouth, NC 27962
c Dep. of Crop Science, North Carolina State Univ., Raleigh, NC 27695-7620
* Corresponding author (jeff_white{at}ncsu.edu)
Received for publication December 20, 2004.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: AOI, areas of interest • B, blue • CIR, color infrared • DGPS, differential global positioning system • DN, digital number • DVI, Difference Vegetation Index • G, green • GDVI, Green Difference Vegetation Index • GNDVI, Green Normalized Difference Vegetation Index • GOSAVI, Green Optimized Soil Adjusted Vegetation Index • GRVI, Green Ratio Vegetation Index • GSAVI, Green Soil Adjusted Vegetation Index • NCDA, North Carolina Department of Agriculture • NDVI, Normalized Difference Vegetation Index • NIR, near-infrared • Norm G, normalized green • Norm NIR, normalized NIR • Norm R, normalized red • NPL, nitrogen applied at planting • NRI, Nitrogen Reflectance Index • NVT, nitrogen applied at VT • OSAVI, Optimized Soil Adjusted Vegetation Index • R, red • RDVI, Relative Difference Vegetation Index • Rel G, relative green • Rel NIR, relative near-infrared • Rel R, relative red • RGDVI, Relative Green Difference Vegetation Index • RGNDVI, Relative Green Normalized Difference Vegetation Index • RGOSAVI, Relative Green Optimized Soil Adjusted Vegetation Index • RGRVI, Relative Green Ratio Vegetation Index • RGSAVI, Relative Green Soil Adjusted Vegetation Index • RMS, root mean square • RNDVI, Relative Normalized Difference Vegetation Index • ROSAVI, Relative Optimized Soil Adjusted Vegetation Index • RRVI, Relative Ratio Vegetation Index • RSAVI, Relative Soil Adjusted Vegetation Index • RVI, Ratio Vegetation Index • SAVI, Soil Adjusted Vegetation Index • UAN, urea–ammonium nitrate solution • VT, pretassel
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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The traditional N fertilization practice for corn production in the southeastern USA has been to apply some amount of N as starter and the remainder as a split application (Crozier, 2002) from before the V4 growth stage to as late as the V8 (Ritchie et al., 1997) growth stage. This can be a problem since approximately one-third of the total N used by a corn crop is taken up after pollination under favorable soil moisture conditions, and N applied earlier may be lost through leaching and denitrification. Due consideration should be given to sidedress N applications at or near tasseling (VT), with adequate N applied earlier in the season to maintain yield potential through VT (Crozier, 2002).
Image-based remote sensing can be used to monitor seasonal variability of soil and crop characteristics such as soil moisture, biomass, crop evapotranspiration, and crop nutrient deficiencies (Blackmer et al., 1996). Remote sensing via aerial color photography has been used to detect N stress in corn (Blackmer and Schepers, 1996; Blackmer et al., 1996), predict corn yield potential (Taylor et al., 1997), and determine N fertilizer requirements for site-specific application by utilizing green (G) digital counts early in the growing season (Scharf and Lory, 2002). These studies showed that color and/or color infrared (CIR) photographs obtained between growth stages V7 and VT could be used to predict yield potential and crop N requirements.
The spectral reflectance of a crop canopy is a combination of the reflectance spectra of plant and soil components as governed by the optical properties of these elements and radiant energy exchange within the canopy (Huete, 1988). High absorption of incident sunlight in the visible red (R, 600–700 nm) and strong reflectance in the near-infrared (NIR, 750–1350 nm) portions of the electromagnetic spectrum by photosynthetically active plant tissue is distinctive from that of soil and water (Lillesand and Kiefer, 1987). Spectral reflectance in the R is inversely related to the in situ chlorophyll concentration, while spectral reflectance in the NIR is directly related to the green leaf density (Gates et al., 1965; Knipling, 1970). Further, it has been reported that vegetation under stress shows decreased NIR reflectance, reduced R absorption in the chlorophyll active band (
680 nm), and a consequent shift of the R edge toward shorter wavelengths (Blackmer et al., 1996). Blackmer et al. (1994) demonstrated that corn leaf reflectance in the G (550 nm) was particularly sensitive to leaf N status.
Vegetation indices developed from spectral observations in the R and NIR wavelengths have shown strong correlations with plant variables such as green leaf area of a tropical rain forest (Jordan, 1969), winter wheat (Triticum aestivum L.) (Wiegand et al., 1979), and soybean [Glycine max (L.) Merr.] (Holben et al., 1980), and with grain yield and severity of drought stress in winter wheat (Tucker et al., 1980). The normalized difference vegetation index (NDVI; Rouse et al., 1973), defined as the ratio of the difference and the sum of the reflectance in the NIR and R regions of the spectrum, has been the most widely used spectral vegetation index. The NDVI is a good indicator of crop stress, and has been considered an indirect measure of crop yield (Shanahan et al., 2001). Jordan (1969) developed the ratio vegetation index (RVI), which is the ratio of the radiance in the NIR to that in the R, and functionally similar to NDVI.
One of the problems with using the spectral reflectance of corn canopies at V7 to determine yield potential or N requirement is the interference of the soil background. Soil influences on incomplete canopy spectra are partly due to dependency of the soil background signal on the optical properties of the overlaying canopy (Heilman and Kress, 1987; Huete, 1987). Differences in R and NIR flux transfers (Kimes et al., 1985; Choudhury, 1987) through a canopy can result in complex soil and vegetation interactions, which make it difficult to correct for soil background influences. However, Huete et al. (1985) found that the sensitivity of vegetation indices to soil background was greatest in canopies with intermediate levels of vegetation cover (50% green cover). Several indices—such as the difference vegetation index (DVI; Tucker, 1979) and the soil adjusted vegetation index (SAVI; Huete, 1988)—have been developed to correct for soil influences.
Another method of correcting for soil interference on incoming radiation is the use of relative indices. Bausch and Duke (1996) developed an N Reflectance Index (NRI) to monitor the N status of irrigated corn from measured G (520–620 nm) and NIR (760–900 nm) canopy reflectance. The NRI was defined as the ratio of NIR/G for an area of interest to NIR/G for a well N-fertilized reference (an area that is never N deficient). Gitelson et al. (1996) proposed that the use of the G band in a vegetation index could prove to be more useful than the R band for assessing canopy variation in biomass. The green NDVI (GNDVI) is the difference between the detected radiation in the NIR and G bands divided by the sum of the the detected radiation in the NIR and G bands [GNDVI = (NIR – G)/(NIR + G)]. Shanahan et al. (2001) suggested that corn GNDVI measured during mid–grain-filling period could be used to produce relative yield maps depicting spatial variability in fields, providing a potential alternative to the use of a combine yield monitor.
Although the ability to predict yield could be used to estimate N requirements, a more accurate method may be to use spectral reflectance or radiance to directly measure crop N requirements. To date, very few studies have been conducted to predict corn side-dress N requirement using remote sensing. Blackmer and Schepers (1995) developed an N sufficiency index (NSI) based on corn chlorophyll meter readings relative to a non–N-limited area to compare N status across fields and for fertigation in the Great Plains. Scharf and Lory (2002) used relative G to predict corn optimum sidedress N at V6 to V7. However, they found that the relationship held only when the following conditions were met: (i) no N applied at planting, (ii) soil pixels removed from the image, and (iii) color expressed relative to the color of well-fertilized corn in the same field.
The objectives of this study were (i) to determine if there is a response to late-season N applied to corn at pretassel (VT) under irrigated and nonirrigated conditions, and (ii) to develop a methodology for predicting in-season N requirement for corn at the VT stage using aerial CIR photography.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Determining Response to Nitrogen Applied at Pretassel
To determine grain yield, the center two rows of each plot were harvested using a Gleaner (AGCO Corp., Duluth, GA) two-row combine. Moisture content and grain yield were recorded using a HarvestMaster Grain Gauge (Juniper Systems, Logan, UT). Grain yield was adjusted to a moisture content of 155 g kg–1. The grain yield response to year, irrigation, and N were analyzed using PROC MIXED in SAS Version 8 (SAS Institute, Cary, NC) with year, irrigation, NPL, and NVT considered as fixed effects and site as a random effect.
Determination of Economic Optimum Pretassel Nitrogen Rates
Grain yield response to N was modeled as a quadratic-plateau function using PROC NLIN in SAS Version 8 (SAS Institute, Cary, NC). Economic optimum N rates were calculated using the first derivative of the quadratic-plateau function and a price ratio of 4:1, defined as the ratio of the price per kilogram of N to the price per kilogram of corn. If a response did not fit a quadratic-plateau function as determined by the significance of the model (
= 0.05), treatment means were compared using Fisher's protected LSD to determine the optimum N level. In situations where the yield response to fertilizer N was not significant as measured by either of the above methods, the economic optimum N rate was set equal to zero.
Image Acquisition and Conversion to Spectral Radiation
Aerial targets were placed at the four corners of each field for obtaining geographic coordinates for use in image georegistration. A differential global positioning system (DGPS) with 1-m accuracy (Trimble AG 132, Trimble Navigation, Sunnyvale, CA) was used to georeference the targets. Aerial CIR photographs were taken at each of these sites at VT using the technique described by Flowers et al. (2001). The aerial CIR images were obtained at altitudes (750–900 m) such that the entire experimental field and surrounding area (6 ha) was covered in a single image and under conditions as cloud free as possible using a belly mounted platform and a 35-mm Canon AE-1 camera (Canon USA, Lake Success, NY). Kodak Ektachrome professional Infrared EIR 135-36 film and a TIFFEN 52 mm Yellow no. 12 filter (Eastman Kodak Co., Rochester, NY) were used. The film was AR-5-processed to obtain false CIR slides. Slides were digitized using the procedure described by Blackmer et al. (1996) with a Konica slide scanner (Konica Q-scan, Konica Corp., Mahwah, NJ) and Adobe Photoshop v. 4.0 (Adobe Systems, San Jose, CA), resulting in a ground resolution of 0.43 to 0.55 m. Differences in ground resolution were due to different altitudes at which the images were obtained. Digital images were georegistered using ERDAS Imagine version 8.7 (ERDAS, Atlanta, GA). The root mean square (RMS) error after the georegistration was <1 m.
The spectral properties of the CIR film used for obtaining images are described by Flowers et al. (2003). The CIR film emulsions respond to light within the visible and NIR regions of the electromagnetic spectrum (490–900 nm). The digitized images are represented by 24-bit true color with three bands: 8-bit red (R), 8-bit green (G), and 8-bit blue (B). For each pixel in the image, the primary color value is represented by a digital number within the range of 0 to 255 for each spectral band. The spectral properties of CIR film result in wide overlapping wavelength bands. With the yellow filter, Band 1 (NIR) of the image covered the wavelengths between 490 and 900 nm, Band 2 (R) covered the wavelengths between 490 and 700 nm, and Band 3 (G) covered the wavelengths between 490 and 620 nm. While these bands overlap, maximum sensitivity in the NIR band occurs at 730 nm, in the R band at 650 nm, and in the G band at 550 nm (Eastman Kodak, 1996). These differences in spectral sensitivity offer increased information through the use of spectral band combinations and vegetation indices (Table 2).
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The digital counts for the NIR, R, and G bands and all of the indices were regressed against the economic optimum N rates using four different models. The linear and quadratic models were fit using PROC REG and the linear-plateau and quadratic plateau models were fit using PROC NLIN in SAS Version 8 (SAS Institute, Cary, NC). The models (linear-plateau) for the relationship between the optimum N rate and an index were tested for differences among years and between irrigation treatments using PROC NLMIXED in SAS Version 8 (SAS Institute, Cary, NC). The parameters of a linear plateau model are the intercept a (the plateau for economic optimum N rate), slope b (of the linear portion of the model), and x0 (the inflection point, the point beyond which there is no change in economic optimum N rate with change in the RGDVI values).
The NLMIXED procedure fits nonlinear mixed models, that is, models in which both fixed and random effects enter nonlinearly. Given the random effect, which in this study was the plateau for the economic optimum N rate, this procedure allows specification of a conditional normal distribution for the data. Successful convergence of the optimization problem results in parameter estimates along with their approximate standard errors based on the second derivative matrix of the likelihood function. The NLMIXED procedure was used to estimate the parameters for the linear-plateau model for each year and irrigation treatment. The estimated parameters were then tested for differences among years and between irrigation treatments using contrast statements.
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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To test the consistency of the NPL and NVT yield responses across different sites within 1 yr under nonirrigated conditions, Sites 2 to 6 were chosen for analysis (Table 4), due to the unbalanced nature of the larger data set. The two-way interactions of NVT with site and NPL were significant. The main effects of these factors (and of site) were significant, indicating again that the factors interacting with N affected the degree of response. For example, in 2000 and 2001, a wide range of yield responses to NVT were observed. These ranged from considerable yield responses to NVT at Sites 1, 2, 5, 8, and 9, to low or no response at Sites 3, 6, 7, and 11 where corn was preceded by peanut (Arachis hypogea L.) and NVT rates of 168 and 224 kg N ha–1 resulted in no yield increase. This was probably due to high mineralization of residual soil N following the legume (Smith and Sharpley, 1990).
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Predicting Economic Optimum Pretassel Nitrogen Rates from Spectral Data
The different NPL rates created a range of canopy color and NIR radiance in the field that was evident in the aerial CIR photographs, and subsequently resulted in a wide range of economic optimum NVT rates. The range of economic optimum NVT rates was 0 to 220 kg N ha–1 with a mean of 104 kg N ha–1. The relationships of economic optimum NVT with the absolute NIR and G bands were not significant, but the relationship with R was, albeit with a low r2 = 0.19 (Table 5). However, the normalized bands were all significantly correlated with economic optimum NVT, and the correlation with normalized R was stronger than with absolute R. One possible interpretation for the significance of the normalized bands compared with the absolute bands is that the normalization effect of simply dividing by the sum of all bands helps correct for different illumination conditions. The regression analyses (Table 5) for indices composed of the NIR and G bands (GDVI, RGDVI) showed somewhat better relationships with economic optimum NVT compared with the indices composed of the NIR and R bands (RVI, NDVI). Thomas and Gaussman (1977) obtained similar results for relationships between the reflectance at 550 nm (G) and the chlorophyll concentration, compared with using reflectance at 675 nm (R).
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In contrast to the individual spectral bands, all of the absolute spectral indices were correlated with economic optimum NVT (Table 5). However, none of the absolute indices (indices not adjusted relative to high-N plots) accounted for more than 40% of the variability in optimum NVT. Walburg et al. (1982) evaluated radiometer single waveband response to N effects on field-grown corn as well as the NIR/R ratio and a greenness index (Kauth and Thomas, 1976). The NIR/R ratio was shown to have enhanced response to N treatment differences in canopy reflectance compared with single wavebands.
In our study, better prediction of economic optimum NVT was observed with the relative indices (Table 5) than with individual spectral bands or absolute indices. Using indices computed relative to high-N reference strips in fields can help eliminate the potential errors that occur with images captured at different times and/or places. Schepers et al. (1992)—using a SPAD meter to measure chlorophyll concentration of corn leaves—suggested that readings be normalized to high-N strips in the field. Blackmer et al. (1996) used Rel R digital counts with reasonable success for qualitative assessment of within-field variation in corn N deficiency. Among the relative indices investigated in the present study, RGDVI accounted for the greatest amount of variability in the linear prediction of economic optimum NVT. Overall, linear-plateau models using RDVI (not shown) and RGDVI (Fig. 2) were the best predictors (R2 = 0.69 and 0.67, respectively; Table 5) of optimum NVT.
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The use of a relative index to predict N application rates at VT requires the availability of high-N reference strips in the field, which is a potential limitation to the application of this technique. Rather than having one reference strip located at random, a better method may be to have a series of reference strips across the field based on the farmers' knowledge of field variability. Though this technique can provide NVT application rates on a site-specific basis, the ability of application equipment to adjust rates and the potential need for multiple calibration strips can limit the use of this technique in precision application of N.
Scharf and Lory (2002) conducted similar work at a much earlier corn growth stage (V7) and observed a linear relationship between predicted economic optimum N rates and G (R2 = 0.70) or B (R2 = 0.79) reflectance. However, these relationships only held under conditions where no N was applied at planting and required that soil pixels in the image be eliminated before obtaining the digital counts. These are serious problems, since most growers apply N at planting, and the process for removing soil pixels from an image requires high resolution images and additional time and cost.
Given the positive results obtained in our study, it would be interesting to investigate how early in the season N requirements for corn can be predicted using our methods. The major technical obstacle in applying this technique earlier in the growing season is the influence of soil pixels on the calculation of the index values. Since this study was done at VT when canopy closure and groundcover were nearly complete, we would not expect significant interference from soil pixels. Another agronomic obstacle to applying this technique earlier in the season is the unpredictability of available soil moisture in rain-fed situations.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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