Remote sensing of nitrogen and water stress in wheat

Tilling, Adam K., O'Leary, Garry J., Ferwerda, Jelle G., Jones, Simon D., Fitzgerald, Glenn J., Rodriguez, Daniel and Belford, Robert (2007). Remote sensing of nitrogen and water stress in wheat. In: Field Crops Research. 13th Biennial Conference of the Australian Society of Agronomy, Perth, Australia, (77-85). 10-14 September 2006. doi:10.1016/j.fcr.2007.03.023

Author Tilling, Adam K.
O'Leary, Garry J.
Ferwerda, Jelle G.
Jones, Simon D.
Fitzgerald, Glenn J.
Rodriguez, Daniel
Belford, Robert
Title of paper Remote sensing of nitrogen and water stress in wheat
Conference name 13th Biennial Conference of the Australian Society of Agronomy
Conference location Perth, Australia
Conference dates 10-14 September 2006
Proceedings title Field Crops Research   Check publisher's open access policy
Journal name Field Crops Research   Check publisher's open access policy
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2007
Sub-type Fully published paper
DOI 10.1016/j.fcr.2007.03.023
ISSN 0378-4290
Volume 104
Issue 1-3
Start page 77
End page 85
Total pages 8
Language eng
Formatted Abstract/Summary
Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers.
Keyword Hyperspectral
Remote Sensing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

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Created: Mon, 07 Mar 2011, 15:24:46 EST