Canopy Chlorophyll Content Index Research Articles

Inversion of rice canopy chlorophyll content and leaf area index based on coupling of radiative transfer and Bayesian network models

The radiative transfer model (RTM) simulates forward spectral reflectance of vegetation and is used to estimate physical parameters using backwards inversion. However, differentiation of spectral reflectances may be hampered due to model parameter combinations, and the cost function within RTM that calculates statistical distance may lead to inconsistent inversions. Bayesian network (BN) is a probabilistic model that is used to solve problems of model ambiguity and incompleteness. Here, we constructed a model to estimate rice growth parameters using data collected by an unmanned aerial vehicle (UAV). We collected rice canopy spectral information using a MiniMCA-6 multispectral camera fitted to an UAV that was used to determine BN structure using parameters derived from the PROSAIL model. We calculated conditional probability distributions of different observed combinations of rice canopy chlorophyll content (CCC) and leaf area index (LAI) and a look up table of maximum conditional probabilities of rice growth parameters based on BN was developed. Results indicated that most accurate inversions of LAI and CCC as BN nodes were achieved at reflectances of 720 nm, 800 nm under the red normalized difference vegetation index and at reflectances of 550, 720, 800 nm under the modified simple ratio index, respectively. Compared with the cost function inversion method, the BN method mitigated the ill-posed problem of inversion and obtained higher inversion accuracy with model test R2, RRMSE, and RE values of 0.81, 0.31, and 0.38, and 0.83, 0.36, and 0.43 for LAI and CCC, respectively. We conclude that application of the BN method to the inversion process of crop RTM could improve inversion accuracy of estimation of crop parameters.

Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPARps) and (ii) using direct quantum yield by classifying the vegetation into C3 or C4 classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm−2day−1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm−2day−1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr−1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.

Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

Reflectance measurements at leaf and canopy scales were made in a red-blush pear (Pyrus communis) orchard for two growing seasons. Canopy reflectance measurements were obtained using a multispectral camera flown on board an unmanned aerial vehicle (UAV), and leaf reflectance measurements were undertaken in a laboratory using a portable spectrometer. These measurements were used to compute reflectance indices as surrogates for direct leaf nitrogen (N) concentration measurements. The indices were evaluated against laboratory analysis of leaf N concentration. Regression results for leaf %N on canopy-level measurements with the multispectral camera resulted in the highest R2 value [R2 = 0.67; root mean square error (RMSE) = 0.24%N] with a new index, Modified Canopy Chlorophyll Content Index (M3CI)_710 nm. Regression results for leaf %N on leaf-level measurements in-laboratory resulted in the highest R2 value (R2 = 0.65) with two other indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-edge Index (NDRE)_720 nm. The corresponding RMSE values were 0.26%N. The results indicate that reflectance indices measured at the leaf level, with a controlled light source and calibration, could be used to estimate leaf %N. An analysis of uncertainty indicated that if leaf %N is estimated from leaf-level reflectance values, 10 or more leaves (from the same tree) should be averaged. The results support the use of a UAV-based assessment for canopy %N using the M3CI_710 nm, which could provide spatial information of leaf N concentration across an orchard.

Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery

The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha−1. Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure- and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha−1) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha−1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.

Active Optical Sensors in Irrigated Durum Wheat: Nitrogen and Water Effects

Core Ideas Interest in the use of active optical sensors for guiding N fertilizer management of crops like wheat has grown rapidly since the mid‐1990s. Recently, active optical sensors have been used to assess water status of crops in addition to plant N status. We conducted a 2‐yr study on a Casa Grande sandy loam soil in Maricopa, AZ, with durum wheat under an overhead sprinkler system. Uniquely, this study had 10 unrandomized levels of irrigation and five rates of N fertilizer. The objectives were to compare 12 vegetation indices for their ability to distinguish irrigation and N fertilizer rates and to determine how well the vegetation indices estimated biomass, plant N, grain yield, grain N, and yellow berry. The canopy chlorophyll content index, Datt, and Meris terrestrial chlorophyll index were the most consistent vegetation indices in responding well to N, with minimal water effects. No vegetation indices detected water stress with minimal N effect as well as canopy temperature measured with infrared thermometers. Interest in active optical sensors (AOS) for guiding N fertilizer management of crops like wheat (Triticum aestivum L.) has grown rapidly since their introduction in the mid‐1990s. Recently, AOS have been used to assess water status of crops in addition to plant N status. Specific vegetation indices (VIs) might assess N stress while minimizing effects of water stress. A 2‐yr study (2013–2014) was conducted on a Casa Grande sandy loam soil in Maricopa, AZ, with durum wheat (T. durum Desf) under an overhead sprinkler system. Uniquely, this study had 10 unrandomized levels of irrigation and five rates of N fertilizer. The objectives were to compare 12 VIs for their ability to distinguish irrigation and N fertilizer effects and to determine how well the VIs estimated biomass, plant N, grain yield, grain N, and yellow berry (opaque starchy grain). Two Crop Circle 470 AOS were passed at a fixed height, 1 m above the tallest plants in the field, every 7 to 10 d during the growing season. The normalize difference vegetation indices (NDVIs) showed highly significant response to N rate in three of four growth‐stage‐years, but significant water and small N effects at Zadoks 32 (early stem elongation) in 2014. The canopy chlorophyll content index (CCCI), DATT (Datt, 1999), and Meris terrestrial chlorophyll index (MTCI) were the most consistent VIs in distinguishing N rates, with minimal water effects. No VIs detected water stress with minimal N effect as well as the infrared thermometer (IRT) measurements of canopy temperature did.

Variable rate nitrogen fertilizer response in wheat using remote sensing

Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha−1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro® and RapidEye™, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N2O emissions, nitrate leaching, and farmer’s profit.

Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status

Temporal and spatial variability of plant available N in the humid southeastern region of the United States often results in within-field over- and under- application of this nutrient to cotton. Although ground-based canopy reflectance has the potential to quantify crop N status in real-time and drive variable rate fertilizer N applications, currently utilized vegetation indices often fail to correlate strongly with crop N status. Therefore, the objective of this study was to examine relationships between canopy reflectance across wavelengths and calculated ratios and vegetation indices prior to and at flowering to biomass, leaf tissue N concentration, aboveground total N content and lint yield. Data was collected during the third week of flower bud formation and the first week of flowering during the 2008–2010 growing seasons at Mississippi State, MS, USA. Analysis of wavelength sensitivities indicated reflectance near 670 nm was most highly correlated to plant height, but relatively poorly correlated to lint yield, total plant N content and leaf N concentration. The strongest wavelength correlations with leaf N concentration, lint yield and plant total N content were noted near 700 nm. Subsequent analysis indicated indices utilizing reflectance in the red edge region correlated more strongly to leaf N status and total plant N content when compared to indices relying on reflectance in green or red regions. Comparisons between simple red edge indices and more complex calculations of the red edge inflection point suggested a simplified version of the Canopy Chlorophyll Content Index calculation may provide reasonable reliability for real-time detection of cotton N status.

Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g∙N∙m−2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p &lt; 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g∙N∙m−2) (r2 = 0.73; SE = 0.603; p &lt; 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.

Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices

In recent decades, many spectral indices have been proposed to estimate crop nitrogen (N) status parameters. However, most of the indices based on red radiation lose their sensitivity under high aboveground biomass conditions. The objectives of this study were to (i) evaluate red-edge based spectral indices for estimating plant N concentration and uptake of summer maize (Zea mays L.) and (ii) study the influence of bandwidth and crop growth stage changes on the performance of various vegetation indices. Nitrogen rate experiments for maize were conducted in 2009 and 2010 at Quzhou Experimental Station of China Agricultural University in the North China Plain. The spectral indices were calculated from hyperspectral narrow bands, simulated Crop Circle ACS-470 active crop canopy sensor bands and simulated WorldView-2 satellite broad bands. The results indicated that red edge-based canopy chlorophyll content index (CCCI) performed the best across different bandwidths for estimating summer maize plant N concentration and uptake at the V6 and V7 and V10–V12 stages. The second best index was MERIS terrestrial chlorophyll index (MTCI). The four red edge-based indices, CCCI, MTCI, normalized difference red edge (NDRE) and red edge chlorophyll index (CIred edge), performed similarly better across bandwidths for estimating plant N uptake (R2=0.76–0.91) than normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) (R2=0.54–0.80) at the V10–V12 and V6–V12 stages. More studies are needed to further evaluate these red edge-based vegetation indices using real Crop Circle ACS 470 sensor and satellite remote sensing images for maize as well as other crops under on-farm conditions.

Quantification of leaf pigments in soybean (Glycine max (L.) Merr.) based on wavelet decomposition of hyperspectral features

Accurate prediction of leaf pigments from spectral reflectance is important because it allows non-destructive, rapid assessment of crop-N status under field conditions. Canopy reflectance and leaf pigments (chlorophyll and carotenoids concentrations) were measured on 385 field-grown soybean genotypes during flowering and seed development stages each in 2009 and 2010. Spectral features related to pigments were extracted based on several known spectral indices and using a number of analytical methods to develop prediction models incorporating reflectance data at single waveband (single-band), two (simple-ratio) or more (multiple linear regression, MLR) wavebands. Among the tested methods, fitness and accuracy (measured as coefficient of determination, R2; root mean square error, RMSE; and relative error, %RE) of the prediction models developed using MLR was greatest. The accuracy of known indices such as the Maccioni-index and canopy chlorophyll content index showed potential for estimation of pigment concentrations using soybean canopy reflectance data. Though, models developed using transformed spectra outperformed the original reflectance spectra irrespective of the analytical method used. In general, the validation of the MLR models revealed limited accuracy across sampling dates and types of spectra used. Continuous wavelet transformed spectra using ‘Mexican hat’ wavelet family (CWT-mexh) produced the best model with the highest accuracy. The selected wavebands in the models primarily consisted of the visible (400–750nm) as compared to the NIR (750–1350nm) spectrum. A general-purpose MLR model using CWT-mexh spectra that was strongly related with pigment concentrations (R2=0.86, RMSE=2.12 and RE=12.5%; chlorophyll and R2=0.83, RMSE=0.56 and RE=12.7%; carotenoids) was developed. The analytical and transformation methods employed in the current study can be useful to develop models for estimation of leaf pigment concentration based on canopy reflectance.

Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany

Red light based broadband vegetation indices are widely applied to derive aerial nitrogen (N) status parameters. With the advance of growth stages, however, crop canopy structure and aerial biomass will vary greatly, which negatively influences the relationships between spectral indices and the crop canopy N status. The current research aimed to assess the performance of red edge based vegetation indices, derived from simulated broadband WorldView-2 data, to remotely sense aerial N concentration and uptake in winter wheat (Triticum aestivum L.). Six experiments with different N rates for five German cultivars and four Chinese cultivars of winter wheat were conducted in southeast Germany and in the North China Plain from 2007 to 2010. The results showed that aerial biomass strongly affected the relationships between broadband vegetation indices and aerial N concentration before the heading stage. Normalising by using the planar domain index approach significantly improved the prediction power of red edge dependent broadband vegetation indices in estimating aerial N status. The two-dimensional broadband canopy chlorophyll content index (CCCI) and a newly proposed nitrogen planar domain index (NPDI) involving the WorldView-2 satellite red edge region were found to be more stable and better predictors than traditional red light based broadband vegetation indices in estimating aerial N concentration after the heading stage and in assessing aerial N uptake before the heading stage. The findings from this study may be useful for managing the application of N fertiliser for winter wheat in Zadoks growth stages 30–55 and in indirectly monitoring aerial N content in Zadoks growth stages 59–75 at landscape scales.

Rapid estimation of canopy nitrogen of cereal crops at paddock scale using a Canopy Chlorophyll Content Index

Rising nitrogen (N) fertiliser costs and concerns over environmental impacts have emphasized the need for rapid, non-destructive, paddock-scale tools to support optimal management of N fertiliser in cereal crops. Remote sensing of canopy reflectance is one approach to address the information needed to guide N applications. One remote sensing vegetation index, the Canopy Chlorophyll Content Index (CCCI) was developed to address issues of confounding effects of biomass and chlorophyll concentration, and Fitzgerald et al. (2010) linked the CCCI to the Canopy Nitrogen Index (CNI), a scaled index of the plant N. In the research presented here, we extend the work by Fitzgerald et al. (2010) and Cammarano et al. (2011) to whole paddocks with ground-based, airborne and satellite imagery. The methodology was applied to 11 datasets using multispectral cameras deployed on ground-based and airborne platforms, and to satellite imagery. The previously published relationship between CNI and CCCI was applied to the CCCI and biomass measurements from these new datasets to estimate CNI. Regression of the measured on estimated plant N values resulted in r2 values for individual datasets ranging from 0 to 0.70. A regression using the means from each dataset produced an r2 of 0.60, and a standard error of 0.82 (%N). Exclusion of the airborne platform data increased the r2 to 0.87 and reduced the standard error to 0.45 (%N). A crop model (APSIM v7) was used to simulate above ground dry matter to substitute for measured biomass, and plant N was determined from the measured CCCI and simulated biomass. Regression of the measured on estimated plant N values resulted in r2 values for individual datasets ranging from 0 to 0.49. The regression using the averages from each dataset was not significant (Fpr>0.10); however, exclusion of the airborne platform data increased the r2 to 0.89 and reduced the standard error to 0.42 (%N). To demonstrate the utility of the methods, simulated biomass values coupled with CNI estimated from RapidEye imagery were used to generate paddock maps of plant N. While the agreement between measured and estimated plant N at the paddock scales is not as close as the previous work done at small plot scales, the method shows promise, particularly if biomass can be estimated through modeling or non-destructive measures.

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R2 = 0.78 and RMSE = 0.39 for CCC and R2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g∙m−2] achieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value &lt; 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and Moderate Resolution Imaging Spectrometer (MODIS) LAI was performed by comparison with the fine spatial resolution LAI map. Results indicated a better accuracy in LAI estimation of MERIS (RMSE = 0.33) compared to MODIS (RMSE = 0.81) data.

Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops

Aerial images obtained using an unmanned aerial system (UAS) were used to create a classified image showing tree canopy health in a macadamia orchard. The resulting map was used to modify management of a macadamia plantation. Vegetation indices, principally the Canopy Chlorophyll Content Index (CCCI), derived from both UAS and WorldView2 satellite imagery, were compared and correlated with spectral radiometry and leaf nitrogen levels determined by field sampling. Classified CCCI images from both sensor types were integrated into farm management software (PAM Ultracrop) and processed into a suitable format for driving a GPS-controlled fertilizer spreader for more effective control of nitrogen application rates. Applying fertilizer at a variable rate according to tree health will result in cost savings to the industry and potentially increase production.

Application of chlorophyll-related vegetation indices for remote estimation of maize productivity

Crop gross primary productivity (GPP) is an important characteristic for evaluating crop nitrogen content and yield, as well as the carbon exchange. Based on the close relationship observed between GPP and total chlorophyll content in crops, we applied a model that relies on a product of chlorophyll-related vegetation index and incoming photosynthetically active radiation for remote estimation of GPP in maize. In this study, we tested the performance of this model for maize GPP estimation based on spectral reflectance collected at a close range, 6 m above the top of the canopy, over a period of eight years from 2001 through 2008. Fifteen widely used chlorophyll-related vegetation indices were employed for GPP estimation in irrigated and rainfed maize, and accuracy and uncertainties of the models were compared. We also explored the possibility of using a unified algorithm in estimating maize GPP in fields that are different in irrigation, field history and climatic conditions. The results showed that vegetation indices that closely relate to total canopy chlorophyll content and/or green leaf area index were accurate in GPP estimation. Both green and red edge Chlorophyll Indices, MERIS Terrestrial Chlorophyll Index as well as Simple Ratio were the best approximations of the widely variable GPP in maize under different crop managements and climatic conditions. They were able to predict daily GPP reaching 30 gC/m 2/d with RMSE below 2.75 gC/m 2/d.

Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments

For this study we hypothesise that the use of canopy chlorophyll content index (CCCI) and crop greenness will be useful in assessing crop nutritional status and provide a robust management tool by growth stage DC30 for fertiliser application across multiple sites without being confounded by soil and biomass differences. The objectives of this study were: (i) to study the robustness of the CCCI and greenness as a measure of crop N content at two different locations, and (ii) to validate the model developed for crop nitrogen (N) determination. Data were collected from two rain-fed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south-eastern wheat belt of Australia (Horsham). Data collection was conducted during the growing season in 2006–07 (December–June) for the Italian site and during the 2006 and 2007 (June–December) growing seasons for the Australian site. Measurements included crop biophysical properties (leaf area index (LAI), biomass, crop N concentration), hyperspectral remote sensing data, and SPAD (chlorophyll meter) determination. An independent dataset including SPAD, biomass, and remotely sensed data from Horsham (Australia) was used to test the validity of the model developed. Results showed that there is good correlation between SPAD and crop N content. The relationship between greenness (measured as LAI*SPAD) and CCCI was fitted with an exponential model and was not affected by biomass accumulation or soil reflectance (r2 = 0.85; y = 15.1e4.5424x; P &lt; 0.001). When this model was tested on the independent dataset it yielded good results for the estimation of greenness (y = 1.22x − 54.87; r2 = 0.90; P &lt; 0.001; root mean square error 32.2; relative error 15%). In conclusion, SPAD measurements combined with LAI could be used as a crop nutritional management tool by DC30 for fertiliser application across multiple sites.

Spectral reflectance from a soybean canopy exposed to elevated CO2 and O3.

By affecting the physiology and structure of plant canopies, increasing atmospheric CO2 and O3 influence the capacity of agroecosystems to capture light and convert that light energy into biomass, ultimately affecting productivity and yield. The objective of this study was to determine if established remote sensing indices could detect the direct and interactive effects of elevated CO2 and elevated O3 on the leaf area, chlorophyll content, and photosynthetic capacity of a soybean canopy growing under field conditions. Large plots of soybean (Glycine max) were exposed to ambient air (∼380 μmol CO2 mol−1), elevated CO2 (∼550 μmol mol−1), elevated O3 (1.2× ambient), and combined elevated CO2 plus elevated O3 at the soybean free air gas concentration enrichment (SoyFACE) experiment. Canopy reflectance was measured weekly and the following indices were calculated from reflectance data: near infrared/red (NIR/red), normalized difference vegetation index (NDVI), canopy chlorophyll content index (chl. index), and photochemical reflectance index (PRI). Leaf area index (LAI) also was measured weekly. NIR/red and LAI were linearly correlated throughout the growing season; however, NDVI and LAI were correlated only up to LAI values of ∼3. Season-wide analysis demonstrated that elevated CO2 significantly increased NIR/red, PRI, and chl. index, indicating a stimulation of LAI and photosynthetic carbon assimilation, as well as delayed senescence; however, analysis of individual dates resolved fewer statistically significant effects of elevated CO2. Exposure to elevated O3 decreased LAI throughout the growing season. Although NIR/red showed the same trend, the effect of O3 on NIR/red was not statistically significant. Season-wide analysis showed significant effects of O3 on PRI; however, analysis of individual dates revealed that this effect was only statistically significant on two dates. Elevated O3 had minimal effects on the total canopy chlorophyll index. PRI appeared to be more sensitive to decreased photosynthetic capacity of the canopy as a whole compared with previously published single leaf gas exchange measurements at SoyFACE, possibly because PRI integrates the reflectance signal of older leaves with accumulated O3 damage and healthy young, upper canopy leaves, enabling detection of significant decreases in photosynthetic carbon assimilation which have not been detected in previous studies which measured gas exchange of upper canopy leaves. When the canopy was exposed to elevated CO2 and O3 simultaneously, the deleterious effects of elevated O3 were diminished. Reflectance data, while less sensitive than direct measurements of physiological/structural parameters, corroborate direct measurements of LAI and photosynthetic gas exchange made during the same season, as well as results from previous years at SoyFACE, demonstrating that these indices accurately represent structural and physiological effects of changing tropospheric chemistry on soybean growing in a field setting.

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 r 2 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.

Assessing Nitrogen Status of Dryland Wheat Using the Canopy Chlorophyll Content Index

Ground-based, active light sensing relies upon the Normalized Difference Vegetation Index (NDVI) for assessing crop N response and applying N fertilizer. However, NDVI may not work well in semiarid environments where biomass and yields depend upon plant available water. This study evaluated the Canopy Chlorophyll Content Index (CCCI) for predicting leaf chlorophyll and N contents while minimizing non-N related crop variation. Ground reflectance was measured on wheat (Triticium aestivumL.) in a small plot experiment using an active sensor with sensitivity in red, red edge, and near infrared wavebands. Relative chlorophyll and N were measured in flag leaf samples. The CCCI was calculated from the Normalized Difference Red Edge (NDRE) index and NDVI in ratio. Index CCCI was more highly correlated with chlorophyll (r2 = 0.46) and leaf N (r2 = 0.31) than NDRE (r2 ≤ 0.16) or NDVI (r2 ≤ 0.09). Chlorophyll and leaf N were well described by CCCI in two farm fields (r2 ≤ 0.79), but NDRE or NDVI performed well in only one field. The CCCI shows promise over NDVI for predicting N status in dryland fields.

Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI)

Various remote sensing indices have been used to infer crop nitrogen (N) status for field-scale nutrient management. However, such indices may indicate erroneous N status if there is a decrease in crop canopy density influenced by other factors, such as water stress. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that has been proposed for inferring cotton N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content. The primary objective of this study was to evaluate the CCCI and several other remote sensing indices for detecting the N status for cotton during the growing season. A secondary objective was to evaluate the ability of the indices to appropriately detect N in the presence of variable water status. Remote sensing data were collected during the 1998 (day of year [DOY] 114 to 310) and 1999 (DOY 106 to 316) cotton seasons in Arizona, in which treatments of optimal and low levels of N and water were imposed. In the 1998 season, water treatments were not imposed until late in the season (DOY 261), well after full cover. Following an early season N application in 1998 for the optimal (DOY 154) but not the low N treatment, the CCCI detected significant differences in crop N status between the N treatments starting on DOY 173, when canopy cover was about 30%. A common vegetation index, the ratio of NIR to red (RVI), also detected significant separation between N treatments, but RVI detection occurred 16 days after the CCCI response. After an equal amount of N was applied to both optimal and low N treatments on DOY 190 in 1998, the CCCI indicated comparable N status for the N treatments on DOY 198, a trend not detected by RVI. In the 1999 season, both N and water treatments were imposed early and frequently during the season. The N status was poorly described by both the CCCI and RVI under partial canopy conditions when water status differed among treatments. However, once full canopy was obtained in 1999, the CCCI provided reliable N status information regardless of water status. At full cotton cover, the CCCI was significantly correlated with measured parameters of N status, including petiole NO3-N (r = 0.74), SPAD chlorophyll (r = 0.65), and total leaf N contents (r = 0.86). For well-watered cotton, the CCCI shows promise as a useful indicator of cotton N status after the canopy reaches about 30% cover. However, further study is needed to develop the CCCI as a robust N detection tool independent of water stress.