44 5. GeoEye-1 satellite versus ground-based multispectral data for estimating nitrogen status of turfgrasses
Remote sensing of leaf nitrogen content in precision agriculture is interesting for both economic and environmental reasons (Perry and Davenport 2007). It allows the monitoring of crop nitrogen nutrition in order to calibrate its application as close as possible to actual plant needs, thus reducing the cost for N fertilizer and the N-related pollution (Samborski et al. 2009). Nitrogen (N) fertilization on turfgrasses is one of the most influential factors on physiological and aesthetic aspects. Nitrogen represents the most important nutrient in turfgrass to maintain green color, adequate density and to allow recovery from drought, diseases and wear stress (Walters and Bingham 2007, Dordas 2008).
In Italy the most cultivated turfgrass are cool-season species, such as
Festuca arundinacea (Grossi et al. 2004) and Lolium perenne. However, in
the last two decades the adaptation of new turfgrass species has been documented and the three species Cynodon dactylon x transvaalensis,
Paspalum vaginatum and Zoysia matrella have been introduced in Southern
Europe (Croce et al. 2004, Macolino et al. 2010, Volterrani et al. 2010, Lulli et
al. 2012, Pompeiano et al. 2012).
To date, spectral reflectance acquisition for turfgrass research or management has been carried out solely by aerial or proximity devices, and the use of satellite imagery has been limited to urban environmental studies (Thomas et al. 2003, Hester et al. 2008, Huang et al. 2011).
5.1 Aim
The objective of the trial was a) to compare the spectral reflectance of five different turfgrasses acquired via satellite imagery or by ground-based instruments commonly used on turfgrasses for research or management purposes; b) to test their sensitivity in detecting artificially induced variations in nitrogen nutritional status of the different canopies. In particular the association between the three different reading methods of NDVI, nitrogen applied on turf and clippings nitrogen content were studied in order to verify if i) GeoEye-1 satellite imagery could be useful as a diagnostic tool to identify the different N status of a turfgrass; ii) NDVI satellite data are well correlated
45 with the ground-based sensors. On a broader view the aim is a potential large scale management and control of turfgrass fertilization via remotely sensed satellite data.
5.2 Materials and Methods
The trial was carried out from July to September 2013 in S. Piero a Grado, Pisa, at the Department of Agriculture, Food and Environment (DAFE) of the University of Pisa (43°40’N, 10° 19’E, 6 m. a.s.l.) on 5 mature turfs including both warm and cool-season species. Warm-season (C4) species were Cynodon dactylon x transvaalensis ‘Patriot’, Paspalum vaginatum ‘Salam’
and Zoysia matrella ‘Zeon’, while cool-season (C3) species were Festuca arundinacea ‘Grande’ and Lolium perenne ‘Regal 5’. The swards were all
established on a calcaric fluvisoil (Coarse-silty, mixed, thermic, Typic Xerofluvents) with pH 7.8 and 18 g kg-1 of organic matter. In year 2013, no fertilizer had been applied to the turfs before the trial started. In order to create a linear nitrogen gradient, on August 22, 2013 fertilization was carried out with a Scotts AccuPro 2000 rotary centrifugal spreader, starting from a non fertilized control and progressing up to the greatest N rate. For warm season species 20 application rates were carried out: 0, 18, 36, 54, 72, 90, 108, 126, 144, 162, 180, 198, 216, 234, 252, 270, 288, 306, 324, 342, kg ha-1 of N (increases of 18 kg ha-1 of N every 1m). For cool-season species 20 application rates were carried out: 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190 kg ha-1 of N (increases of 10 kg ha-1 of N every 1m). Extreme N rates have been applied in order to reach the saturation of NDVI values, regardless of the practical benefits to turfgrasses. Within the linear gradient 11 equally spaced (2m) sampling zones were identified. For each species the experimental area was: 20 x 3m x 3 replications (180 m2). Ammonium sulphate was used as nitrogen source. After the fertilization, 5 mm of irrigation was applied to incorporate the fertilizer into the soil. During the trial period a turf height of 2.0 cm was maintained with a reel mower and the clippings were removed (thrice a week). Irrigation was applied as needed to avoid wilt, in order to maintain the soil moisture constant and equal in all the area. No weed or pest control was necessary during the trial. The entire experimental area was subjected to
46 identical maintenance practices, in order to evaluate nitrogen fertilization as the only variability source.
On September 4 and 5, 2013, 11 proximity and remote sensed readings were acquired, from the unfertilized control to the greatest nitrogen rate. The climate conditions were: air temperature avg. 24°C, relative humidity avg. 68%; solar radiation max. 1031 W/m2; wind avg. 9 km/h; no clouds; no rain). Each ground-based measurement was geo-referenced to sub-metre accuracy with a Differential Global Positioning System (DGPS) receiver.
Ground-based measurements of spectral reflectance were carried out with a spectroradiometer and with a handheld crop sensor. The spectroradiometer was a LICOR 1800 (LI-COR Inc., Lincoln, NE, USA) with a fiber optic wire and LICOR 1800-06 telescope. The telescope was mounted on a purpose-built trolley at 130 cm from the ground with a vision angle of 15°. The monitored surface corresponded at ground level to approximately 2000 cm2 diameter = 50cm). Measurements were taken between 11.30 am and 1.30 pm (local time), in complete absence of clouds. The radiation reflected by a white panel made from barium sulphate was measured as reference in order to detect any possible variation in irradiance. Reflectance readings were carried out in the 390-1100 nm region at 5 nm intervals. The output of LICOR is provided as NDVI value. The reflectance at NIR was centered at 850 nm and the reflectance centered at red 670 nm. The ratio between reflection from the turf and reflection from the white panel gave the value of spectral reflectance.
The Trimble’s GreenSeeker Handheld Crop Sensor, Model HSC-100 (Trimble Navigation Unlimited, Sunnyvale, CA) was held at approximately a height of 110 cm from the ground assuming this resulting in a monitored surface of about 2000 cm2 (Ø = 50cm). The GreenSeeker has an active light source that makes readings unaffected by sunlight (Bell et al. 2009, Govaerts and Verhulst 2006). Reflectance is measured in the near infrared region of the spectrum centered at 780 nm and in the red region centered at 660 nm. The output of the Trimble’s GreenSeeker is directly provided as NDVI value. Satellite measurements of spectral reflectance data were acquired by GeoEye-1 satellite. The passage of the satellite was planned on September
47 5, 2013, and the image was acquired at 10:14 h. GMT (12:14 a.m. local time) (Fig.7).
Figure 7 Geo-referenced GeoEye-1 color image of the study site (Piero a Grado, Pisa,
Tuscany, Italy).
The acquired image covered an area of about 100 km2, which included the trial plots. GeoEye-1 panchromatic image was geo-referenced using identifiable buffer zones of 1 x 1m outside the borders of each species linear N gradient in order to well identify the points where the ground-based measurements were taken (Cipra 2003, Baush and Khosla 2010). Every pixel (0.50 x 0.50 m) of the image contains coordinates and an NDVI value. The reflectance of NIR was centered at 850 nm and the reflectance of red centered at 670 nm. Pixel NDVI values were extracted using ENVI software (RSI Inc., Boulder, CO). Thus, the plots were identified and NDVI values were obtained in the same position where the ground NDVI readings were performed.
On the same day as satellite and ground readings, samples of clippings were collected with a John Deere 20SR7 reel mower from a surface of about 0.5 m2 (1 x 0.5 m). Clippings were collected in the same area where the readings were carried out. Fresh clippings were put in a ventilated stove at 70 °C, dried to constant weight, and the total nitrogen (N) was determined by the micro-Kjedahl method (Bremner 1965).
48 The association between the three different NDVI reading methods (Licor spectroradiometer, GreenSeeker and GeoEye-1 satellite), applied nitrogen and clippings nitrogen content were studied using CoStat software (CoHort, Monterey, CA, USA). Pearson’s correlation coefficient (r) was calculated for the three NDVI reading instruments and for clippings nitrogen content.Linear regression equations were calculated for the correlations showing significant coefficients.
5.3 Results
5.3.1. Cynodon dactylon x transvaalensis (Cd x t)
Among the Pearson’s correlation coefficients (r) observed by correlating NDVI data obtained with the different instruments and the nitrogen rates applied on the Cd x t plots, the highest value was found through the satellite image (r = 0.92). The proximity sensing readings via GreenSeeker (r = 0.91) and through Licor 1800 (r = 0.83) were still highly correlated with the doses of nitrogen applied on the surfaces (Tab.17). Figure 8 shows the regression line between the clippings nitrogen content and NDVI spectral reflectance data, obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer; GreenSeeker) and the GeoEye-1 satellite. It is interesting to point out how all the regression coefficients are high for all the instruments (above 0.84). However, the NDVI index obtained with GreenSeeker handheld crop sensor and the GeoEye-1 satellite showed the highest degree of association with clippings nitrogen % (r = 0.89). Comparing NDVI data obtained with the different instruments, the highest r value (r = 0.99) was found between GeoEye-1 satellite and GreenSeeker ground-based sensor (Tab.17).
49
Table 17 Pearson product-moment correlation coefficients (r) among the nitrogen applied,
clippings nitrogen content, NDVI measured with ground-based instruments (LICOR 1800 spectroradiometer; Trimble’s GreenSeeker sensor) and NDVI measured with GeoEye-1 satellite on a) Cynodon dactylon x transvaalensis ‘Patriot’; b) Paspalum vaginatum ‘Salam’; c) Zoysia matrella ‘Zeon’. Correlation coefficients are calculated across all entries.
r N applied N % clippings NDVI GreenSeeker (780,660) NDVI Satellite (850,670) NDVI Licor (850,670) a) Cd x t N applied - 0.98*** 0.91*** 0.92*** 0.83** N % clippings - - 0.89*** 0.89*** 0.84** NDVI GreenSeeker (780,660) - - - 0.99*** 0.88*** NDVI Satellite (850,670) - - - - 0.90*** b) Pv N applied - 0.87*** 0.97*** 0.87*** 0.91*** N % clippings - - 0.94*** 0.94*** 0.94*** NDVI GreenSeeker (780,660) - - - 0.93*** 0.97*** NDVI Satellite (850,670) - - - - 0.91*** c) Zm N applied - 0.98*** 0.94*** 0.96*** 0.88*** N % clippings - - 0.88*** 0.91*** 0.83** NDVI GreenSeeker (780,660) - - - 0.97*** 0.98*** NDVI Satellite (850,670) - - - - 0.90***
50
Figure 8 Relationships between the clippings nitrogen content of Cynodon dactylon x
transvaalensis ‘Patriot’ and NDVI data obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer; Trimble’s GreenSeeker sensor) and the GeoEye-1 satellite.
5.3.2 Paspalum vaginatum (Pv)
The study of statistical comparisons performed on Paspalum vaginatum has shown that the NDVI values obtained from Trimble’s GreenSeeker sensor are highly correlated with the nitrogen rates applied on the turfgrass (r = 0.97) (Tab.17). In the comparisons between the NDVI values obtained with the different instruments and the clippings nitrogen content, the Pearson’s correlation coefficients (r) were high and all the same (r = 0.94) (Fig.9). Relating the NDVI data of the different sensors with each other, the highest value was found between the two ground-based instruments GreenSeeker and Licor 1800 spectroradiometer (r = 0.97). However the proximity sensed readings via GreenSeeker (r = 0.93) and through Licor 1800 (r = 0.91) were still highly correlated with the GeoEye-1 NDVI reflectance data.
51
Figure 9 Relationships between the clippings nitrogen content of Paspalum vaginatum
‘Salam’ and NDVI data obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer; Trimble’s GreenSeeker sensor) and the GeoEye-1 satellite.
5.3.3 Zoysia matrella (Zm)
In the statistical comparisons between the nitrogen applied on Zoysia
matrella plots and the NDVI obtained with the three different instruments, the
highest coefficient (r = 0.96) was found with GeoEye-1 satellite (Tab.17). Among the Pearson’s correlation coefficients (r) observed by correlating NDVI data obtained with the different instruments and the clippings nitrogen content, the highest value was found through the satellite image (r = 0.91) (Fig.10). In the relationships between the NDVI data of the different instruments with each other, the highest r value was found between the two ground-based instruments GreenSeeker and Licor 1800 spectroradiometer (r = 0.98). However the proximity sensed readings via GreenSeeker (r = 0.97) were still highly correlated with the GeoEye-1 NDVI reflectance data.
52
Figure 10 Relationships between the clippings nitrogen content of Zoysia matrella ‘Zeon’
and NDVI data obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer; Trimble’s GreenSeeker sensor) and the GeoEye-1 satellite.
5.3.4 Warm season turfgrass species
In the comparison between the clippings nitrogen content and NDVI spectral reflectance data, both at the highest and lowest content of nitrogen, the NDVI values are higher in Cd x t than in the other two warm-season species: Pv and Zm (Fig. 8, 9 and 10). To point out the differences, in the relationship between the NDVI acquired through the satellite image and the clippings N % the highest Pearson correlation coefficient is in Pv (r = 0.94). Comparing the NDVI data obtained through the GeoEye-1 satellite and the clippings nitrogen content, the most reactive species to nitrogen fertilization is
Cd x t with a range of N values between 1.20 % and 4.1 %. Also Zm shows a
nitrogen content value of 1.0 % in the non fertilized control, but with increasing nitrogen rates applied on the turf, the plants uptake is significantly lower than Cd x t, with a peak value of 2.8 % of nitrogen content. In Pv without fertilization (control), a nitrogen content of 1.5% is recorded, higher than the other two warm-season turf species (Zm 1.1%; Cd x t 1.2%).
53 The study of statistical comparisons performed on Festuca arundinacea has shown that the NDVI values obtained from GeoEye-1 satellite and with GreenSeeker sensor, are highly correlated with the nitrogen rates applied on the ground (respectively r = 0.99 and r = 0.97) (Tab.18). Correlating NDVI data obtained with the different instruments and the clippings nitrogen content, higher values were found through the satellite image (r = 0.90) and with Licor 1800 spectroradiometer (r = 0.90) (Fig.11). In the comparisons between the NDVI data obtained with the different instruments, the highest coefficient was found between the ground-based sensor GreenSeeker and the GeoEye-1 satellite (r = 0.97).
Table 18 Pearson product-moment correlation coefficients (r) among the nitrogen applied,
clippings nitrogen content, NDVI measured with ground-based instruments (LICOR 1800 spectroradiometer; Trimble’s GreenSeeker sensor) and NDVI measured with GeoEye-1 satellite on a) Festuca arundinacea ‘Grande’; b) Lolium perenne ‘Regal 5’. Correlation coefficients are calculated across all entries.
r N applied N % clippings NDVI GreenSeeker (780,660) NDVI Satellite (850,670) NDVI Licor (850,670) a) Fa N applied - 0.92*** 0.97*** 0.99*** 0.92*** N % clippings - - 0.87*** 0.90*** 0.90** NDVI GreenSeeker (780,660) - - - 0.97*** 0.91*** NDVI Satellite (850,670) - - - - 0.92*** b) Lp N applied - 0.84** 0.99*** 0.97*** 0.91*** N % clippings - - 0.88*** 0.87** 0.82** NDVI GreenSeeker (780,660) - - - 0.95*** 0.93*** NDVI Satellite (850,670) - - - - 0.83**
54
Figure 11 Relationships between the clippings nitrogen content of Festuca arundinacea
‘Grande’ and NDVI data obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer ; Trimble’s GreenSeeker sensor) and the GeoEye-1 satellite.
5.3.6 Lolium perenne (Lp)
The NDVI values obtained from GeoEye-1 satellite and GreenSeeker sensor, are highly correlated with the nitrogen rates applied on the Lolium
perenne turf (respectively r = 0.97 and r = 0.99) (Tab.18). Among the
correlation coefficients (r) observed by correlating NDVI data obtained with the different instruments and the clippings nitrogen content, higher values were found through the GreenSeeker sensor (r = 0.88) and with the satellite (r = 0.87) (Fig.12). In the comparisons between the NDVI data obtained with the different instruments, the highest r value was found between the ground-based sensor GreenSeeker and the GeoEye-1 satellite (r = 0.95).
55
Figure 12 Relationships between the clippings nitrogen content of Lolium perenne ‘Regal 5’
and NDVI data obtained with the ground-based spectral reflectance instruments (LICOR 1800 spectroradiometer ; Trimble’s GreenSeeker sensor) and the GeoEye-1 satellite.
5.3.7 Cool-season turfgrass species
Comparing NDVI data with the clippings N content of the two cool-season turfgrass species, Fa (Fig. 11) and Lp (Fig. 12), Lolium perenne shows higher clippings nitrogen content values (range 2.12 % - 4.88 %) than Festuca
arundinacea (range 1.84 % - 4.28 %). These values are also higher than all
the warm-season species, maybe for their darkest color or different structure of their leaves (Agati et al. 2013). Furthermore to point out the differences, in the relationship between the NDVI acquired through the satellite image and the clippings N % the highest Pearson correlation coefficient is in Fa (r = 0.90).
5.4 Discussion
Like all modern agriculture sectors, turfgrass production and management is headed towards resources optimization, costs and environmental impact reduction. The application of vegetation indices helps to highlight spectral differences including turf quality, color, dry matter, chlorophyll, carotenoids, nitrogen concentration and other important information. As Hansen et al. (2003) found, NDVI has been correlated to many variables such as crop
56 nutrient deficiency, final yield in small grains, and long-term water stress. However, rather than exclusively reflecting the effect of one parameter, NDVI has to be considered as a measurement of many plant growth factors, also in turfgrasses (Johnsen et al. 2009, Agati et al. 2013). Satellite reflectance data could be used for the detection of physiological and nutritional conditions of the various turfgrass species. Hence, the evaluation of the spectral reflectance of plants using satellite spectroradiometry allows a potential large scale management and control of agricultural resources. This study allowed 1) the evaluation of different nitrogen rates applied to various turf species and the clippings nitrogen concentration; 2) to compare these values with the NDVI obtained with proximity and remote sensed instruments; 3) to carry out an evaluation of results showed that NDVI data and the analyzes obtained with the ground-based instruments are highly correlated with GeoEye-1 satellite data. Looking at all the correlation data we note that the relationship between the measured clippings N concentration and the NDVI data obtained with the satellite are well correlated. The satellite NDVI values are also well correlated to the other two ground-based instruments. Thus, if the area is relatively small as a park or a garden, it could be useful the GreenSeeker handheld crop sensor because it is less expensive and more practical. If the area is greater, such as a golf course or sod farms or turfgrass seed production farms etc., in addition to the use of proximal sensors, it could be necessary to monitor the entire surface with a satellite, as GeoEye-1. We demonstrate experimentally for what is to our knowledge the first time that a satellite was used for estimating nitrogen status of turfgrasses, and the results of our trial appear encouraging since the current satellite imagery provides an opportunity to extend the measurements both in space and through time, to reduce wastes.
5.5 Conclusions
In conclusion we verified that GeoEye-1 satellite multispectral data can adequately assess the N status of the different turfgrass species studied in the trial, and its spatial variability within a field depending on the nitrogen rates applied on the surfaces. On surfaces uniformly managed, to reduce the environmental variability, GeoEye-1 satellite NDVI data could be particularly
57 useful in the administration of the turfgrass fertilization. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially into a turfgrass. Further studies could be useful to investigate the possibility of discriminating, through the use of the NDVI or other vegetation indices, between different species or different water and climate conditions, in order to carry out a target management of all the resources.
