Canopy Spectral Measurements Research Articles

The Photochemical Reflectance Index (PRI) Captures the Ecohydrologic Sensitivity of a Semiarid Mixed Conifer Forest

AbstractAt the seasonal time scale, daily photochemical reflectance index (PRI) measurements track changes in photoprotective pigment pools as plants respond to seasonally variable environmental conditions. As such, remotely sensed PRI products present opportunities to study seasonal processes in evergreen conifer forests, where complex vegetation dynamics are difficult to capture due to small annual changes in chlorophyll content or leaf structure. Because PRI is tied explicitly to short‐ and long‐term changes in photoprotective pigments that are responsible for regulating stress, we hypothesize that PRI by extension could serve as a proxy for stomatal response to seasonally changing hydroclimate, assuming plant functional responses to stress covary in space and time. To test this, we characterized PRI in a semiarid, montane mixed conifer forest in the Madrean sky islands of Arizona, USA, during the monsoon growing season subject to precipitation pulse dynamics. To determine the sensitivity of PRI to ecohydrologic variability and associated changes in gross primary productivity (GPP), canopy spectral measurements were coupled with eddy covariance CO2 flux and sap flow measurements. Seasonally, there was a significant relationship between PRI and sap flow velocity (R2 = 0.56), and multiple linear regression analysis demonstrated a PRI response to dynamic water and energy limitations in this system. We conclude that PRI has potential to serve as a proxy for forest functional response to seasonal ecohydrologic forcing. The coordination between photoprotective pigments and seasonal stomatal regulation demonstrated here could aid characterization of vegetation response to future changes in hydroclimate at increasing spatial scales.

Remote Sensing Indices to Measure the Seasonal Dynamics of Photosynthesis in a Southern China Subtropical Evergreen Forest

The accurate measurement of the dynamics of photosynthesis in China's subtropical evergreen forest ecosystems is an important contribution to carbon (C) sink estimates in global terrestrial ecosystems and their responses to climate change. Eddy covariance has historically been the only direct method to assess C flux of whole ecosystems with high temporal resolution, but it suffers from limited spatial resolution. During the last decade, continuous global monitoring of plant primary productivity from spectroradiometer sensors on flux towers and satellites has extended the temporal and spatial coverage of C flux observations. In this study, we evaluated the performance of two physiological remote sensing indices, fluorescence reflectance index (FRI) and photochemical reflectance index (PRI), to measure the seasonal variations of photosynthesis in a subtropical evergreen forest ecosystem using continuous canopy spectral and flux measurements in the Dinghushan Nature Reserve in southern China. The more commonly used NDVI has been shown to be saturated and mainly affected by illumination (R2=0.88, p < 0.001), but FRI and PRI could better track the seasonal dynamics of plant photosynthetic functioning by comparison and are less affected by illumination (R2=0.13 and R2=0.51, respectively) at the seasonal scale. FRI correlated better with daily gross primary production (GPP) in the morning hours than in the afternoon hours, in contrast to PRI which correlated better with light-use efficiency (LUE) in the afternoon hours. Both FRI and PRI could show greater correlations with GPP and LUE respectively in the senescence season than in the recovery-growth season. When incident PAR was taken into account, the relationship between GPP and FRI was improved and the correlation coefficient increased from 0.22 to 0.69 (p < 0.001). The strength of the correlation increased significantly in the senescence season (R2=0.79, p < 0.001). Our results demonstrate the application of FRI and PRI as physiological indices for the accurate measurement of the seasonal dynamics of plant community photosynthesis in a subtropical evergreen forest, and suggest these indices may be applied to carbon cycle models to improve the estimation of regional carbon budgets.

Quantitative Inversion of Vegetation Biochemical Components Based on HJ1-A HSI in Coal Mining Area

This study investigates the applicability of estimating chlorophyll and water content at canopy level through empirical models and band combinations. The main goal is to evaluate and compare the accuracy of these two approaches for estimating and mapping canopy chlorophyll and water content through canopy reflectance and spaceborne HJ1-A HSI data acquired over Yanzhou coal mining area. An experiment was carried out. Canopy spectral measurements were acquired in the field using an ASD spectroradiometer along with simultaneous in situ measurements of leaf chlorophyll content. We tested seven variables derived from canopy reflectance for detecting canopy chlorophyll and water content: (1) R, (2) Log(1/R), (3) Log(1/R)′, (4) FDR, (5) SDR, (6) CRR, (7) BD. Stepwise multiple linear regressions were used to select wavelengths from HJ1-A HSI image bands. Correlation analysis was also done between different band combinations and biochemistry. A statistically significant relationship between Log(1/R) and chlorophyll was found at canopy level (R2 = 0.516). SDR had the highest correlation with canopy water content (R2 = 0.490). In addition, relationship between normalized different band combinations and chlorophyll and water content is also significantly obvious (R2 = 0.577 and R2 = 0.615). Canopy chlorophyll content was estimated with the intermediate accuracy (R2 = 0.4144), while water content was estimated with an acceptable accuracy (R2 = 0.4592). Canopy chlorophyll and water content spatial distribution were mapped. Chlorophyll and water stress levels were quantified by comparing different environmental stressors.

Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield

While hyperspectral sensors describe plant canopy reflectance in greater detail than multispectral sensors, they also suffer from issues with data redundancy and spectral autocorrelation. Data mining techniques that extract relevant spectral features from hyperspectral data will aid the development of novel sensors for plant trait estimation. The objectives of this research were to (1) compare broad-band reflectance, narrow-band reflectance, and spectral derivatives for estimation of durum wheat traits in the field and (2) develop a genetic algorithm to identify the most relevant spectral features for durum wheat trait estimation. Experiments at Maricopa, Arizona during the winters of 2010–2011 and 2011–2012 tested six durum wheat cultivars with six split-applied nitrogen (N) fertilization rates. Durum wheat traits, including leaf area index, canopy dry weight, and plant N content, were measured from destructive biomass samples on four occassions in each growing season. Grain yield and grain N content were also measured. Canopy spectral reflectance data in 701 narrow wavebands from 350nm to 1050nm were collected weekly using a field spectroradiometer. First- and second-order spectral derivatives were calculated using Savitzky-Golay filtering. The narrow-band data were also used to estimate reflectance in broad wavebands, as typically collected by two commercial multispectral instruments. Partial least squares regression (PLSR) compared the ability of each spectral data set to estimate each measured durum wheat trait. A genetic algorithm was developed to mine narrow-band canopy reflectance and spectral derivative data for spectral features that improved estimates of durum wheat traits. Multispectral data in 4 broad bands estimated leaf area index, canopy dry weight, and plant N content with root mean squared errors of cross validation (RMSECV) between 33.0% and 67.6%, while hyperspectral data in 701 narrow bands reduced RMSECV to values between 19.3% and 36.3%. Use of the genetic algorithm to identify less than 25 relevant spectral features further reduced RMSECV to values between 15.1% and 30.7%. Grain yield was optimally estimated from canopy spectral measurements between 110 and 130days after planting with RMSECV less than 7.6% using the genetic algorithm approach. The timing corresponded to anthesis and early grain fill when presence of wheat heads likely affected canopy spectral reflectance. By using a genetic algorithm to mine hyperspectral reflectance and spectral derivative data, durum wheat traits were optimally estimated from a subset of relevant canopy spectral features.

Specific leaf area estimation from leaf and canopy reflectance through optimization and validation of vegetation indices

Specific leaf area (SLA), which is defined as the leaf area per unit of dry leaf mass is an important component when assessing functional diversity and plays a key role in ecosystem modeling, linking plant carbon and water cycles as well as quantifying plant physiological processes. However, studies of SLA variation across relevant spatial and temporal scales are lacking. While remote sensing is a fast and efficient approach to quantify vegetation parameters, there are insufficient studies estimating SLA from remotely sensed data. This article aims at finding efficient hyperspectral indices for fast and accurate estimation of SLA from leaf and canopy spectral measurements. Validation of our results with data measured at leaf and canopy scale as well as with experimental datasets simulated using PROSPECT (at leaf level) and INFORM (at canopy level) revealed SLA was predicted accurately by several indices, such as simple ratio and normalized index types. Most of the bands sensitive to SLA selected using these indices were in the 1300–1800nm spectral region. At leaf level, a ratio index at bands1370nm, 1615nm performed strongly (R2=0.93 and RMSE=13.66cm2/g) during cross-validation. The multiband indices with 920nm, 1675nm, 1335nm and 1345nm, 1675nm, 1850nm central wavelengths were also among the top performing indices, with R2>0.90 for both measured and simulated leaf level data. At canopy level, the soil adjusted ratio vegetation index (with a band setting of 1537nm, 1543nm) showed that the hyperspectral data from HySpex airborne imagery accurately estimated SLA (R2=0.88 and RMSE=13.30cm2/g). Generally at canopy level the potential indices for accurate retrieval of SLA from HySpex imagery were two-band indices with soil line information. The discrepancy observed in the performance and band combinations of vegetation indices at leaf and canopy scales can be explained by external factors such as canopy structure, soil background, illumination and sensor configuration which affect the signal when moving from leaf to canopy level. Our findings suggest the availability and suitability of a wide range of existing vegetation indices for assessing SLA accurately and rapidly from remotely sensed data.

In-situ plant hyperspectral sensing for early detection of soybean injury from dicamba

Drift of dicamba onto non-target crops is a major concern because it is highly active on susceptible crops even at low doses. Early detection of crop injury is critical in crop management. A field study was conducted to determine spectral characteristics of soybean (Progeny P4819LL) treated with dicamba. Drift deposition of dicamba was simulated by direct application at 0.05 to 1.0 times of the recommended label rate (0.56 kg [ai] ha−1) to soybean at the 5- to 6-trifloliolate leaf stage, approximately 6 weeks after planting. The canopy spectral measurements were taken at 24, 48, and 72 h after treatment (HAT) using a portable spectroradiometer in the 325–1075 nm spectral range on 3 randomly selected plants within each plot with device optimisation and data calibration. The results indicated that it was difficult to clearly differentiate the dose response of soybean to different dicamba spray rates within 72 HAT. Regardless of spray rates the soybean treated with dicamba could be clearly differentiated from untreated soybean from 24 to 72 HAT through spectral vegetation index analysis with anthocyanin reflectance and photochemical reflectance indices with accuracies at 24, 48, and 72 HAT ranging from 76 to 86%. Simulated dicamba drift injured soybean and reduced its yield by 71 and 90% at 0.05 and 0.1 times recommended rate, respectively. This study demonstrated that hyperspectral remote sensing has a potential in early detection of soybean injury from exposure to off-target dicamba drift at sub lethal rates in the field.

Response of spectral vegetation indices to a stocking rate experiment in Inner Mongolia, China

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales.

Spectral Discrimination of Insect Defoliation Levels in Mopane Woodland Using Hyperspectral Data

Mopane woodland are a source of valuable resources that contribute substantially to rural economies and nutrition across Southern Africa. However, a number of factors have, of late, brought the sustainability of the mopane woodland resources into question. One of such factors is the difficulty in monitoring of defoliation process within the woodland. In this study we set out to discriminate the levels of change in forest canopy cover detectable after insect defoliation using ground based hyperspectral measurements in mopane woodland. Canopy spectral measurements were taken from three levels of defoliation: Undefoliated (UD), Partly defoliated (PD) and Refoliating plants (R) using ASD FieldSpec HandHeld 2. A pre-filtering approach (ANOVA) was compared with random forest independent variable selector in selecting the significant wavelengths for classification. Furthermore, a backward feature elimination method was used to select optimal wavelengths for discriminating the different levels of defoliation in mopane woodland. Results show that optimal wavelengths located at 707 nm, 710 nm, 711 nm, 712 nm, 713 nm, 714 nm, 727 nm, and 1066 nm were able to discriminate between the three levels of defoliation. The results further show that there was no significant difference in the overall accuracy of classification when random forest variable selector was used 82.42% (Kappa = 0.64) and the pre-filtering approach (ANOVA) 81.21% (Kappa = 0.68) used before building the classification. Overall, the study clearly demonstrated that the dynamic process of defoliation in mopane woodland can be assessed and detected using hyperspectral dataset and effective algorithm for discrimination.

Discriminating indicator grass species for rangeland degradation assessment using hyperspectral data resampled to AISA Eagle resolution

The development of techniques to estimate and map increaser grass species is critical for better understanding the condition of the rangeland and levels of rangeland degradation. This paper investigates whether canopy reflectance spectra, resampled to AISA Eagle resolution can discriminate among four increaser species representing different levels of rangeland degradation. Canopy spectral measurements were taken from the four indicator species: Hyparrhenia hirta (HH), Eragrostis curvula (EC), Sporobolus africanus (SA), and Aristida diffusa (AD). The random forest algorithm and a forward variable selection technique were used to identify optimal wavelengths for discriminating the species. Results revealed that the optimal number of wavelengths (n=8) that yielded the lowest OOB error (11.36%) in discriminating among the four increaser species are located in 966.7, 877.6, 691.9, 718.7, 902.7, 854.8, 674.1 and 703nm. These wavelengths are located in the visible, red-edge and near-infrared regions of the electromagnetic spectrum. The random forest algorithm can accurately discriminate species with an overall accuracy of 88.64% and a KHAT value of 0.85. The study demonstrated the possibility to upscale the method to airborne sensors such as AISA Eagle for mapping indicator species of rangeland degradation. A rotational grazing management plan should be considered as a way to create sustainable rangeland management in degraded areas.

Discriminating the papyrus vegetation (Cyperus papyrus L.) and its co-existent species using random forest and hyperspectral data resampled to HYMAP

Techniques for discriminating swamp wetland species are critical for the rapid assessment and proactive management of wetlands. In this study, we tested whether the random forest (RF) algorithm could discriminate between papyrus swamp and its co-existent species (Phragmites australis, Echinochloa pyramidalis and Thelypteris interrupta) using in situ canopy reflectance spectra. Canopy spectral measurements were taken from the species using analytical spectral devices but later resampled to Hyperspectral Mapper (HYMAP) resolution. The RF algorithm and a simple forward variable selection (FVS) technique were used to identify key wavelengths for discriminating papyrus swamp and its co-existence species. The method yielded 10 wavelengths located in the visible and short-wave infrared portions of the electromagnetic spectrum with a lowest out-of-bag (OOB) estimate error rate of 9.5% and .632+ bootstrap error of 8.95%. The use of RF as a classification algorithm resulted in overall accuracy of 90.50% and a kappa value of 0.87, with individual class accuracies ranging from 93.73% to 100%. Additionally, the results from this study indicate that the RF algorithm produces better classification results than conventional classification trees (CTs) when using all HYMAP wavelengths (n = 126) and when using wavelengths selected by the FVS technique.

Spectral Discrimination of Opium Poppy Using Field Spectrometry

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-Whitney U-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.

Comparison of Laboratory and Field Remote Sensing Methods to Measure Forage Quality

Recent research in range ecology has emphasized the importance of forage quality as a key indicator of rangeland condition. However, we lack tools to evaluate forage quality at scales appropriate for management. Using canopy reflectance data to measure forage quality has been conducted at both laboratory and field levels separately, but little work has been conducted to evaluate these methods simultaneously. The objective of this study is to find a reliable way of assessing grassland quality through measuring forage chemistry with reflectance. We studied a mixed grass ecosystem in Grasslands National Park of Canada and surrounding pastures, located in southern Saskatchewan. Spectral reflectance was collected at both in-situ field level and in the laboratory. Vegetation samples were collected at each site, sorted into the green grass portion, and then sent to a chemical company for measuring forage quality variables, including protein, lignin, ash, moisture at 135 °C, Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Total Digestible, Digestible Energy, Net Energy for Lactation, Net Energy for Maintenance, and Net Energy for Gain. Reflectance data were processed with the first derivative transformation and continuum removal method. Correlation analysis was conducted on spectral and forage quality variables. A regression model was further built to investigate the possibility of using canopy spectral measurements to predict the grassland quality. Results indicated that field level prediction of protein of mixed grass species was possible (r2 = 0.63). However, the relationship between canopy reflectance and the other forage quality variables was not strong.

Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data

The dynamics of photosynthetic pigment concentrations and composition provide important information about plant nutritional status. For this reason, hyperspectral remote sensing techniques for quantifying plant pigments on a large scale have received much attention. The spectral absorbance properties of photosynthetic pigments are manifested in the reflectance spectra of the canopy level. This offers the opportunity of using measurements of reflected radiation as a non-destructive method for analyzing nutritional status in grass. The objectives of this study were to analyze the relationships between photosynthetic pigments (chlorophyll, carotenoids, and chlorophyll/carotenoid ratio), canopy spectral characteristics, and vegetation indices (VIs) derived from broadband and narrowband analysis in tall fescue ( Festuca arundinacea). The overall goal was to determine a sensitive indicator for assessing grass nutritional status using hyperspectral remote sensing techniques. Canopy spectral measurements from each treatment (Control, Low fertility and High fertility) were taken in the field using a FieldSpec ® FR spectroradiometer. A large number (i.e. 22,500) of two-band combinations in the Normalized Difference Vegetation Index (NDVI) and the Ratio Vegetation Index (RVI) were then used for a linear regression analysis against photosynthetic pigments. Obvious differences in spectral reflectance existed between treatments within certain wavelength regions (400–1000 nm). In addition, the techniques of derivative analysis increased the separation of grasses with different fertility levels, providing the possibility of monitoring grass nutritional status. Unlike chlorophyll and carotenoid values individually, the ratio of chlorophyll/carotenoid was strongly correlated with canopy spectral reflectance (500–730 nm) ( P < 0.01). Further investigation of the relationships between photosynthetic pigments and traditional broadband vegetation indices suggested that wavelengths of 610–690 and 752–1000 nm, the regions of the red and NIR channels of several multi-spectral sensors in place on the current generation of earth-orbiting satellites, were not the optimum wavelengths for NDVI and RVI analysis. However, since the NDVI was closely related to chlorophyll/carotenoid ratio, using the combinations of λ 1 at 540–560 nm and λ 2 at 750–950 nm produced a coefficient of determination R 2 > 0.75. Thus, it was possible to map variation in grass photosynthetic pigments using hyperspectral remote sensing. This provided an insight for building new models and vegetation indices to monitor grass nutritional status by following the dynamics of the chlorophyll/carotenoid ratio.

Spectral discrimination of papyrus vegetation ( Cyperus papyrus L.) in swamp wetlands using field spectrometry

Techniques for mapping and monitoring wetland species are critical for their sustainable management. Papyrus ( Cyperus papyrus L.) is one of the most important species-rich habitats that characterize the Greater St. Lucia Wetlands Park (GSWP) in South Africa. This paper investigates whether papyrus could be discriminated from its co-existing species using ASD field spectrometer data ranging from 300 nm to 2500 nm, yielding a total of 2151 bands. Canopy spectral measurements from papyrus and three other species were collected in situ in the Greater St. Lucia Wetlands Park, South Africa. A new hierarchical method based on three integrated analysis levels was proposed and implemented to spectrally discriminate papyrus from other species as well as to reduce and subsequently select optimal bands for the potential discrimination of papyrus. In the first level of the analysis using ANOVA, we found that there were statistically significant differences in spectral reflectance between papyrus and other species on 412 wavelengths located in different portions of the electromagnetic spectrum. Using the selected 412 bands, we further investigated the use of classification and regression trees (CART) in the second level of analysis to identify the most sensitive bands for spectral discrimination. This analysis yielded eight bands which are considered to be practical for upscaling to airborne or space borne sensors for mapping papyrus vegetation. The final sensitivity analysis level involved the application of Jeffries-Matusita (JM) distance to assess the relative importance of the selected eight bands in discriminating papyrus from other species. The results indicate that the best discrimination of papyrus from its co-existing species is possible with six bands located in the red-edge and near-infrared regions of the electromagnetic spectrum. Overall, the study concluded that spectral reflectance of papyrus and its co-existing species is statistically different, a promising result for the use of airborne and satellite sensors for mapping papyrus. The three-step hierarchical approach employed in this study could systematically reduce the dimensionality of bands to manageable levels, a move towards operational implementation with band specific sensors.

LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy ( R cv 2 = 0.74 , nRMSE cv = 0.35 ). All methods failed to estimate LCC ( R cv 2 ≤ 0.40 ), while LAI was estimated with intermediate accuracy ( R cv 2 values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics.

Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland

Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R2 between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R2=0.70, NRMSE=0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R2=0.59, NRMSE=0.18). When only sample plots with up to two species were considered (n=107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE=0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements.

Red edge shift and biochemical content in grass canopies

The concentration of foliar nitrogen in tropical grass is one of the factors that explain the distribution of wildlife. Therefore, the remote sensing of foliar nitrogen contributes to a better understanding of wildlife feeding patterns. This study evaluated changes in the red edge position of the 680 nm continuum removed chlorophyll feature in the reflectance spectra of samples of Cenchus ciliaris grass grown in a greenhouse under three levels of nitrogen supply. Canopy spectral measurements from each treatment were recorded under controlled laboratory conditions over a four-week period using a GER 3700 spectroradiometer. Results indicate that the mean wavelength positions of the three fertilization treatments were statistically different. An increase in nitrogen supply yielded a shift in the red edge position to longer wavelengths. The red edge position, amplitude, slope at 713 nm and slope at 725 nm were significantly correlated to measured nitrogen concentration (bootstrapped r = 0.89, − 0.28, 0.63 and 0.75, respectively) even at canopy level. Based on these results, the red edge position is strongly correlated with biochemical concentration in plants compared to the other methods tested. The study provides conclusive evidence that confirms the strength of a red edge-nitrogen relationship that remains underused in remote sensing. This method is promising for estimating nutrient content in grasslands.

Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features

The remote sensing of pasture quality as determined by nitrogen, phosphorous, potassium, calcium and magnesium concentration is critical for a better understanding of wildlife and livestock feeding patterns. Although remote sensing techniques have proved useful for assessing the concentration of foliar biochemicals under controlled laboratory conditions, more investigation is required to assess their capabilities in the field, where inconsistent results have been obtained so far. We investigated the possibility of determining the concentration of in situ biochemicals in a savanna rangeland, using the spectral reflectance of five grass species. Canopy spectral measurements were taken in the field using a GER 3700 spectroradiometer. We tested the utility of using four variables derived from continuum-removed absorption features for predicting canopy nitrogen, phosphorus, potassium, calcium and magnesium concentration: (i) continuum-removed derivative reflectance (CRDR), (ii) band depth (BD), (iii) band depth ratio (BDR) and (iv) normalised band depth index (NBDI). Stepwise linear regression was used to select wavelengths from the absorption-feature-based variables. Univariate correlation analysis was also done between the first derivative reflectance and biochemicals. Using a training data set, the variables derived from continuum-removed absorption features could predict biochemicals with R 2 values ranging from 0.43 to 0.80. Results were highest using CRDR data, which yielded R 2 values of 0.70, 0.80, 0.64, 0.50 and 0.68 with root mean square errors (RMSE) of 0.01, 0.004, 0.03, 0.01 and 0.004 for nitrogen, phosphorous, potassium, calcium and magnesium, respectively. Predicting biochemicals on a test data set, using regression models developed from a training data set, resulted in R 2 values ranging from 0.15 to 0.70. The error of prediction (RSE) in the test data set was 0.08 (±10.25% of mean), 0.05 (±5.2% of mean), 0.02 (±11.11% of mean), 0.05 (±11.6% of mean) and 0.03 (±15% of mean) for nitrogen, potassium, phosphorous, calcium and magnesium, respectively, using CRDR. When data was partitioned into species groups, the R 2 increased significantly to >0.80. With high-quality radiometric and geometric calibration of hyperspectral imagery, the techniques applied in this study (i.e. continuum removal on absorption features) may also be applied on data acquired by airborne and spaceborne imaging spectrometers to predict and ultimately to map the concentration of macronutrients in tropical rangelands.

Discriminating tropical grass ( Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry

Techniques for estimating and mapping pasture quality are critical for a better understanding of wildlife and livestock grazing patterns. Nitrogen is one of the most important elements that determine quality in plants. We assessed the potential to discriminate differences in nitrogen concentration using high-resolution reflectance by growing Cenchrus ciliaris grass with different fertilization treatments in a greenhouse. Canopy spectral measurements from each treatment were taken under controlled laboratory conditions within a period of 4 weeks using a GER 3700 spectroradiometer. Results show that there were statistically significant differences in spectral reflectance between treatments within certain wavelength regions—an encouraging result for classifying and mapping grasslands with different levels of nutrients using hyperspectral remote sensing. We further investigated the effect of varying nitrogen supply to a specific absorption feature in the visible between 550 and 750 nm (R 550– 750) using continuum-removed spectra. Results show that the high nitrogen treatment had deeper and wider absorption pits as compared to the low nitrogen treatment as well as the control (no nitrogen), which is important for the prediction of nitrogen in grass canopies. This is a promising result for the remote sensing of canopy chemistry since emphasis can be shifted from the mid-infrared region (which is highly masked by water absorption) to the visible region. Overall, the results provide the possibility to map variation in pasture quality using hyperspectral remote sensing.

Use of Spectral Radiance to Estimate In-Season Biomass and Grain Yield in Nitrogen- and Water-Stressed Corn

Current technologies for measuring plant water status are limited, while recently remote sensing techniques for estimating N status have increased with limited research on the interaction between the two stresses. Because plant water status methods are time-consuming and require numerous observations to characterize a field, managers could benefit from remote sensing techniques to assist in irrigation and N management decisions. A 2-yr experiment was initiated to determine specific wavelengths and/or combinations of wavelengths indicative of water stress and N deficiencies, and to evaluate these wavelengths for estimating in-season biomass and corn (Zea mays L.) grain yield. The experiment was a split-plot design with three replications. The treatment structure had five N rates (0, 45, 90, 134, and 269 kg N ha−1) and three water treatments [dryland, 0.5 evapotranspiration (ET), and full ET]. Canopy spectral radiance measurements (350–2500 nm) were taken at various growth stages (V6–V7, V13–V16, and V14–R1). Specific wavelengths for estimating crop biomass, N concentration, grain yield, and chlorophyll meter readings changed with growth stage and sampling date. Changes in total N and biomass in the presence of a water stress were estimated using near-infrared (NIR) reflectance and the water absorption bands. Reflectance in the green and NIR regions were used to estimate total N and biomass without water stress. Reflectance at 510, 705, and 1135 nm were found for estimating chlorophyll meter readings regardless of year or sampling date.