Remote Sensing of Nitrogen Stress in Creeping Bentgrass

Abstract

Development of a remote sensing system that can reliably identify nutrient deficiencies may reduce time spent sampling turfgrass areas and allow for site‐specific applications of fertilizers. The objectives of this research were to evaluate the use of a ground‐based remote sensing system and partial least‐squares (PLS) regression to predict the N concentration, biomass production, chlorophyll content, and visual quality of creeping bentgrass (Agrostis stolonifera L. ‘Penncross’) growing under varying N rates, and to compare PLS regression to other vegetative indices. The study consisted of three N treatments (0.0, 12.2, and 24.4 kg ha−1 15 d−1) arranged in a randomized complete block design. Spectral radiance measurements were obtained from plots using a fiber‐optic spectrometer to calculate vegetative indices. The PLS regression analysis yielded a strong relationship between actual and predicted N concentration of creeping bentgrass plant tissue during 2002 and 2003 (r2 = 0.95 and 0.71 respectively). However, PLS regression failed to produce a prediction for the chlorophyll concentration. Regressing the normalized vegetation index (NDVI), Stress1 (R706/R760), and Stress2 (R706/R813) ratios against N concentration yielded better results in 2003 when there were distinct differences in N concentration between the N rates. These results indicate that the traditional vegetation indices like NDVI might be better suited for determining the relative N status of turfgrass plants when compared against a well‐fertilized control. More research will be required to determine if the PLS regression analysis produces prediction models that are able to specifically identify a particular nutrient deficiency or plant stress, and how the results will vary between grass species.

Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy

“…This enables the quantification of chemical attributes of the leaves. For example, spectral absorption features have been used successfully to estimate foliar N concentration (Jain et al, 2007;Kruse et al, 2006;Tarpley et al, 2000;White et al, 2000;Zhao et al, 2005). White et al (2000) selected the 2028 nm wavelength to form a model of estimating vegetation leaf N concentration from in situ spectroscopic data.…”

“…Read et al (2002) found the highest correlation between cotton leaf N content and reflectance at a ratio of reflectance between 700 and 710 nm. Furthermore, Kruse et al (2006) indicated that reflectance ratio 706-760 nm was the best index of predicting leaf N concentration of bentgrass. Additionally, Zhao et al (2005) who investigated the relationship between leaf N concentration and reflectance of sorghum crops reported that firstorder derivatives of the reflectance in the red edge centered at 730 or 740 nm yielded r 2 of 0.73.…”

Relationship of cotton nitrogen and yield with Normalized Difference Vegetation Index and plant height

“…Lee et al (2008) found that leaf N concentration was significantly associated with NDVI (R 2 = 0.36-0.47) when both were measured at the panicle formation stage for rice grown under three to five rates of N fertilizer ranging from 0 to 200 kg ha -1 . Kruse et al (2006) observed that N concentration in plant biomass related to NDVI (R 2 = 0.23-0.63) of creeping bentgrass grown under three N rates of 0, 12.2 and 24.4 kg ha -1 when both biomass N and NDVI were measured simultaneously during the growing season.…”

Detecting heavy metal pollution of floodplain vegetation in a pot experiment using reflectance spectroscopy

“…In the ecosystems of the floodplains, the relationship between heavy metal concentration and chlorophyll content is much more complex. Kruse et al (2006) showed the positive relationship between nitrogen status and the chlorophyll content in plants. In contrast, Horler et al (1983) reported that heavy metal treated plants contained lower chlorophyll concentrations than healthy plants.…”

“…Clevers et al (2004) stated that grasslands with a higher biomass level grew in soils with a high organic matter and soil moisture content. Ackermann et al (2010) and Kruse et al (2006) showed that there was no simple relationship between soil heavy metal content and heavy metal concentration in plants. Sites with high accumulation rates of contaminants like heavy metals largely vary in nutrient content and water supply.…”