Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.     

Improvement of nitrogen management in rice paddy fields using chlorophyll meter (SPAD)

Optimum rate and timing application of nitrogen (N) fertilizer are most crucial in achieving high yield in irrigated lowland rice. In order to assess leaf N status, a semidwarf rice cultivar (Khazar) was grown with different N application treatments (0, 40, 80, and 120 kg N ha⁻¹ splited at transplanting, midtillering, and panicle initiation stages) in a sandy soil in Guilan Province, Iran, in 2003. The chlorophyll meter (SPAD 502) readings were recorded and leaf N concentrations were measured on the uppermost fully expanded leaf in rice plants at 10-day internals from 19 days after transplanting to grain maturity. Regression analysis showed that the SPAD readings predicted only 23% of changes in the leaf N concentration based on pooled data of leaf dry weight (N _dw) for all growth stages. However, adjusting the SPAD readings for specific leaf weight (SPAD/SLW) improved the estimation of N _dw, up to 88%. Specific leaf weight (SLW), SPAD readings, leaf area and weight as independent variables in a multiple regression analysis predicted 96% of the N _dw changes, while SPAD readings independently predicted about 80% of leaf N concentration changes on the basis of leaf area (N _a). It seems that chlorophyll meter provides a simple, rapid, and nondestructive method to estimate the leaf N concentration based on leaf area, and could be reliably exploited to predict the exact N fertilizer topdressing in rice.     

Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars

Several sensor systems are available for ground-based remote sensing in crop management. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This work describes a study comparing active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors, including the Crop Circle, GreenSeeker, and an active flash sensor (AFS), were tested for their ability to assess six destructively determined crop parameters. Over 2 years, seven wheat (Triticum aestivum L.) cultivars were grown with nitrogen supplies varying from 0 to 220 kg ha⁻¹. At three developmental stages, the crop reflectance was recorded and sensor-specific indices were calculated and related to N levels and the crop parameters, fresh weight, dry weight, dry matter content, as percent of dry weight to fresh weight, N content, aboveground N uptake, and the nitrogen nutrition index. The majority of the tested indices, based on different combinations of wavelengths in the visible and near infrared spectral ranges, showed high r²-values when correlated with the crop parameters. However, the accuracy of discriminating the influence of varying N levels on various crop parameters differed between sensors and showed an interaction with growing seasons and developmental stage. Visible- and red light-based indices, such as the NDVI, simple ratio (R₇₈₀/R₆₇₀), and related indices tended to saturate with increasing crop stand density due to a decreased sensitivity of the spectral signal. Among the destructively assessed biomass parameters, the best relationships were found for N-related parameters, with r²-values of up to 0.96. The near infrared-based index R₇₆₀/R₇₃₀ was the most powerful and temporarily stable index indicating the N status of wheat. This index was delivered by the BDR, Crop Circle, and AFS. Active spectral remote sensing is more flexible in terms of timeliness and illumination conditions, but to date, it is bound to a limited number of indices. At present, the broad spectral information from bi-directional passive sensors offers enhanced options for the future development of crop- or cultivar-specific algorithms.     

Responses of rice leaf thickness,SPAD readings and chlorophyll a/b ratios to different nitrogen supply rates in paddy field

The objective of this paper was to investigate the influence of different nitrogen (N) application rates to some morphological and physiological features of leaf blades, including leaf thickness, chlorophyll content at different leaf ages and chlorophyll a/b ratios. A paddy field and a cement tank experiments were conducted simultaneously. Rice leaf thickness was measured through a specially developed displacement sensor. Meanwhile, chlorophyll content was estimated using chlorophyll meter (SPAD) and spectrophotometer after ethanol extraction of leaf samples. With the increase of N application, leaf thickness became thinner and chlorophyll a/b ratios decreased. Moreover, the sensitivity of the SPAD readings of the same leaf at different leaf ages to N rates was assessed through coefficients of variation (CV). CV of SPAD readings increased from 8.8% to 21.6% during leaf lifetime, which indicates that SPAD readings became more and more sensitive to nitrogen rates as leaf aged. Therefore, SPAD readings of the lower leaves, which were physiologically older than the upper ones, were more sensitive to nitrogen rates.     

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI)

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the “red edge” of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI–CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m⁻²) from Zadoks 14–37 with an r² of 0.97 and RMSE of 0.65 g N m⁻² when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.     

Yield, water use efficiency and nitrogen uptake in potato: influence of drought stress

Summary A lysimeter experiment was performed to study the optimal allocation of limited water supply in potatoes. Irrigation regimes equal to 40, 60 and 80% of maximum evapotranspiration (ET) were evenly applied over the crop cycle. Other treatments involved withholding 80 mm of irrigation, based on ET, beginning at each of three designated growth stages (tuber initiation, early and late tuber growth). An irrigated control treatment, restoring the entire ET, was included for comparison. Continuous drought stress reduced photosynthesis as irrigation volumes were reduced. Plant biomass and tuber yield decreased almost proportionally to water consumption, so that WUE was roughly constant. N uptake was highest in the control and in 80% ET treatment. Withholding water during tuberisation severely hindered plant physiological processes and penalized tuber yield. Reductions in photosynthesis, total biomass and yield were the greatest when drought was imposed during tuber initiation. The earliest stress resulted in the lowest WUE and N uptake. A new crop water stress index (SI) was proposed, which combines atmospheric demand for water and canopy temperature.