Effects of low concentrations of o(3) on net photosynthesis, dark respiration, and chlorophyll contents in aging hybrid poplar leaves.
Chronic exposure of hybrid poplar (Populus deltoides x trichocarpa) plants to low concentrations of ozone had negative impact upon net photosynthetic capacity, dark respiration, and leaf chlorophyll contents. Exposure to as much as 0.20 microliters per liter O(3) had no immediate effect on net photosynthesis (P(n)), but chronic exposure to 0.125 or 0.085 microliters per liter had a number of gradual effects on CO(2) exchange. These included increased dark respiration and consequently increased light compensation points in very young leaves (4-6 days old); and decreased P(n), leaf chlorophyll a and b contents, light saturation points, and apparent quantum yields in leaves 10 to 70 days old. Decreased P(n) was partially due to accelerated aging in leaves exposed to O(3), and lightsaturated P(n) was linearly related to chlorophyll a + b contents. Differences in light-saturated P(n) between control and O(3)-treated leaves of the same age were mostly due to photosaturation in O(3)-treated leaves and to a much lesser extent to lowered apparent quantum yields. Also, since P(n) and dark respiration were most affected by O(3) at different leaf ages, distinct modes of action are suggested. The effects of leaf aging on CO(2) exchange were considerable, but typical of other species. However, careful monitoring of the interacting effects of leaf age and pollutant exposure was needed in order to characterize the impact of chronic O(3) exposure upon CO(2) exchange.
Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery
Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25–4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical–optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%–1.07% and 0.36%–1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R2 = 0.89 to R2 = 0.97 and discrepancies of 0.02%–3.63% and 0.24%–7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R2 = 0.47, RMSE = 4.34 μg/cm2, and jackknifed RMSE of 5.69 μg/cm2 for needle chlorophyll content ranging from 24.9 μg/cm2 to 37.6 μg/cm2. The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R2 = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.
Distortion of the SPAD 502 chlorophyll meter readings by changes in irradiance and leaf water status
- The SPAD 502 Chlorophyll Meter can estimate leaf chlorophyll content as a surrogate measure of the nitrogen (N) status of plants, and therefore, it can be used to assess the N requirements of crops. In this work, we show that irradiance, leaf water status and time of measurement (i.e., morning vs. afternoon), may interfere with the SPAD 502 measurements. SPAD values increased by 2-3 units as relative leaf water content decreased from 94 to 87.5 57492768n wheat leaves. A change in irradiance (from 1100 to 600-650 mmol m-2 s-1) caused an increase of about 2 SPAD units in potted maize plants. Throughout the day, SPAD values varied by up to 4 units in well-watered plants of maize growing under field conditions. The slope of the response of the SPAD meter to chlorophyll content was steeper in the morning than in the afternoon. Since the range of SPAD values that may separate N deficiency from N surplus in a crop are often relatively narrow, time of measurement, irradiance and plant water status must be taken into account to precisely monitor crop N needs with the SPAD 502.
Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance
Different nitrogen (N) treatments of four common green-leafy vegetable varieties with different leaf color: lettuce ( Lactuca sativa L. var. crispa L.) with yellow green leaves, pakchoi ( Brassica chinensis L.) var. aijiaohuang in Chinese (AJH) with middle green leaves, spinach ( Spinacia oleracea L.) with green leaves and pakchoi ( B. chinensis L.) var. shanghaiqing in Chinese (SHQ) with dark green leaves, were carried out to achieve a wide range of chlorophyll content. The relationship of vegetable leaf hyperspectral response to its chlorophyll content was examined in this study. Almost all reported successful leaf chlorophyll indices in the literature were evaluated for their ability to predict the chlorophyll content in vegetable leaves. Some new indices based on the first derivative curve were also developed, and compared with the chlorophyll indices published. The results showed that most of the indices showed a strong relation with leaf chlorophyll content. In general, modified indices with the blue or near red edge wavelength performed better than their simple counterpart without modification, ratio indices performed a little better than normalized indices when chlorophyll expressed on area basis and reversed when chlorophyll expressed on fresh weight basis. A normalized derivative difference ratio (BND: ( D 722 − D 700 ) / ( D 722 + D 700 ) calibrated by Maire et al. [Maire, G., Francois, C., Dufrene, E., 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sensing of Environment 89 (1), 1–28]) gave the best results among all published indices in this study (RMSE=22.1 mg m −2 ), then the mSR-like indices with the RMSE between 22.6 and 23.0 mg m −2 . The new indices EBAR (ratio of the area of red and blue, ∑ dRE/∑ dB), EBFN (normalized difference of the amplitude of red and blue, (dRE−dB)/(dRE+dB)) and EBAN (normalized difference of the area of red and blue, (∑ dRE−∑ dB)/(∑ dRE+∑ dB)) calculated with the derivatives also showed a good performance with the RMSE of 23.3, 24.15 and 24.33 mg m −2 , respectively. The study suggests that spectral reflectance measurements hold promise for the assessment of chlorophyll content at the leaf level for green-leafy vegetables. Further investigation is needed to evaluate the effectiveness of such techniques on other vegetable varieties or at the canopy level.
Leaf chlorophyll content and its relation to the intracellular localization of iron
Chlorophyll contents of leaves of sugar beets undergoing Fe stress have been shown to be correlated positively with leaf Fe concentration (Plant Physiol. 1980 65:114–120). In the present work, the nature of this quantitative relationship was explored by determining the amounts of leaf Fe associated with whole chloroplasts (isolated nonaqueously) and with EDTA‐washed chloroplast lamellae. The results show that leaf chlorophyll content was quantitatively related to the leaf content of chloroplast Fe and to the leaf content of chloroplast lamellar Fe. Nonaqueously isolated chloroplasts accounted for 79 and 73 percent of the Fe of leaves of Fe sufficient and Fe deficient plants, respectively. The Fe content of EDTA‐washed chloroplast lamellae constituted 58 percent of leaf Fe in control plants; this increased to 75 percent in Fe deficient plants. In contrast with the view that chlorophyll contents may be correlated with a “biochemically active fraction” of leaf Fe (e.g. water or acid soluble fraction...
The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures
Retrieving leaf chlorophyll content at a range of spatio-temporal scales is central to monitoring vegetation productivity, identifying physiological stress and managing biological resources. However, estimating leaf chlorophyll over broad spatial extents using ground-based traditional methods is time and resource heavy. Satellite-derived spectral vegetation indices (VIs) are commonly used to estimate leaf chlorophyll content, however they are often developed and tested on broadleaf species. Relatively little research has assessed VIs for different leaf structures, particularly needle leaves which represent a large component of boreal forest and significant global ecosystems. This study tested the performance of 47 published VIs for estimating foliar chlorophyll content from different leaf and canopy structures (broadleaf and needle). Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species (Picea mariana and Acer saccharum) and a variety of canopy structures. Leaf reflectance data was collected using an ASD Fieldspec Pro spectroradiometer (400–2500 nm) for over 300 leaf samples. Canopy reflectance data was acquired from the medium resolution imaging spectrometer (MERIS). At the canopy level, with both leaf types combined, the DD-index showed the strongest relationship with leaf chlorophyll (R2 = 0.78; RMSE = 3.56 μg/cm2), despite differences in leaf structure. For needleleaf trees alone the relationship with the top VI was weaker (D[red], R2 = 0.71; RMSE = 2.32 μg/cm2). A sensitivity study using simulated VIs from physically-modelled leaf (PROSPECT) and canopy (4-Scale) reflectance was performed in order to further investigate these results and assess the impacts of different background types and leaf area index on the VIs’ performance. At the leaf level, the MNDVI8 index showed a strong linearity to changing chlorophyll and negligible difference to leaf structure/type. At canopy level, the best performing VIs were relatively consistent where LAI ≥ 4, but responded strongly to differences in background at low canopy coverage (LAI = 2). This research provides comprehensive assessments for the use of spectral indices in retrieval of spatially-continuous leaf chlorophyll content at the leaf (MTCI: R2 = 0.72; p < 0.001) and canopy (DD: R2 = 0.78; p < 0.001) level for resource management over different spatial and temporal scales.
Non-destructive determination of leaf chlorophyll content in four crop species
A recent non-destructive technique allows estimation of leaf chlorophyll content using the portable SPAD-502 chlorophyll meter. Measurements were taken on four species (winter wheat, maize, soyabean and tobacco) subjected to different nitrogen regimes or senescence status and the: non-destructive readings were compared with analytical results obtained by solvent extraction. In general, the relationship between the SPAD measurement and the analytical result was not linear and species was a factor in three out of four crops. Linear, quadratic and exponential curve fitting are presented; only die interpolation with a polynomial exponential function adequately descries the whole data set. The presence of statistically non-significant differences between the estimated values of wheat and maize on the one hand and significant: points of difference between those of tobacco and soyabean on the other suggests distinct behaviour patterns far monocots and cicots. This type of response maybe explained by differences, in the optical properties of pigments with differing sparial distributions (sieve effect) and therefore by in vivo and in vitro different procedures and the structural diversity of leaves belonging to the two subclasses.
Multiple Cost Functions and Regularization Options for Improved Retrieval of Leaf Chlorophyll Content and LAI through Inversion of the PROSAIL Model
Lookup-table (LUT)-based radiative transfer model inversion is considered a physically-sound and robust method to retrieve biophysical parameters from Earth observation data but regularization strategies are needed to mitigate the drawback of ill-posedness. We systematically evaluated various regularization options to improve leaf chlorophyll content (LCC) and leaf area index (LAI) retrievals over agricultural lands, including the role of (1) cost functions (CFs); (2) added noise; and (3) multiple solutions in LUT-based inversion. Three families of CFs were compared: information measures, M-estimates and minimum contrast methods. We have only selected CFs without additional parameters to be tuned, and thus they can be immediately implemented in processing chains. The coupled leaf/canopy model PROSAIL was inverted against simulated Sentinel-2 imagery at 20 m spatial resolution (8 bands) and validated against field data from the ESA-led SPARC (Barrax, Spain) campaign. For all 18 considered CFs with noise introduction and opting for the mean of multiple best solutions considerably improved retrievals; relative errors can be twice reduced as opposed to those without these regularization options. M-estimates were found most successful, but also data normalization influences the accuracy of the retrievals. Here, best LCC retrievals were obtained using a normalized “L1 -estimate” function with a relative error of 17.6% (r2 : 0.73), while best LAI retrievals were obtained through non-normalized “least-squares estimator” (LSE) with a relative error of 15.3% (r2 : 0.74).
Nondestructive and Rapid Estimation of Leaf Chlorophyll and Nitrogen Status of Peace Lily Using a Chlorophyll Meter
Timely and nondestructive detection of leaf nitrogen (N) status could greatly improve nutrient management practices for greenhouse ornamental production. This study evaluated the feasibility of using a SPAD-502 chlorophyll meter for estimating leaf chlorophyll and nitrogen contents of the peace lily (Spathiphyllum Schott). Three cultivars ‘Claudia’, ‘Double Take’, and ‘Petite’ were grown in a soilless substrate and sub-irrigated with solutions containing N at 50, 100, 200, or 400 mg L−1 through an ebb-and-flow system. The SPAD-502 chlorophyll meter was used to measure leaf greenness. Chlorophyll contents of leaves were also analyzed using the dimethyl sulphoxide extraction method. Total N was determined by a CNS Auto-Analyzer. Correlation analyses showed that coefficients (r 2) between SPAD values and leaf chlorophyll contents were 0.83, 0.77, and 0.73 for ‘Claudia’, ‘Double Take’, and ‘Petite’, respectively, and coefficients (r 2) between SPAD values and leaf N contents of ‘Claudia’, ‘Double Take’, and ‘Petite’ were 0.84, 0.82, and 0.91, respectively. These results suggest that the readings from a SPAD meter can be used for rapidly and nondestructively estimating leaf chlorophyll and N status of the peace lily. As high quality Spathiphyllum were produced at the N concentration of 200 mg L−1, the corresponding SPAD values established in this study could facilitate in situ decision-making on N application for the production of the peace lily. #This research was supported by the Florida Agricultural Experiment Station and approved for publication as Journal Series No. R-09531.
Factors Influencing Leaf Chlorophyll Content in Natural Forests at the Biome Scale
Chlorophyll (Chl) is an important photosynthetic pigment to the plant, largely determining photosynthetic capacity and hence plant growth. However, this concept has not been verified in natural forests, especially at a large scale. Furthermore, how Chl varies in natural forests remains unclear. In this study, the leaves of 823 plant species were collected from nine typical forest communities, extending from cold-temperate to tropical zones in China, to determine the main factors influencing leaf chlorophyll content in different regions and at different scales. We measured chlorophyll a (Chl a), chlorophyll b (Chl b), Chl (Chl a+ Chl b), and the ratio of Chl a and Chl b (Chl a/b). The results showed that Chl a, Chl b, and Chl a/b values were in the range of 0.87–15.92 mg g–1 (mean: 4.18 mg g–1), 0.32–6.42 mg g–1 (mean 1.72 mg g–1), and 1.43–7.07 (mean: 2.47). The values of these three Chl parameters significantly differed among plant functional groups (trees < shrubs < herbs, coniferous trees < broadleaved trees, and evergreens < deciduous trees). Unexpectedly, Chl a, Chl b, and Chl a+b increased slightly with increasing latitude. Climate, soil, and phylogeny exert only a small effect on the spatial variation of Chl in natural forests, with large variation in the Chl of coexisting species masking the spatial patterns. This study is the first to report variations in Chl among different types of natural forests at a large scale, demonstrating that the fuzzy regulation of Chl makes it very difficult to take Chl as the main input parameter to the models of natural forests.
Chlorophyll content in eucalypt vegetation at the leaf and canopy scales as derived from high resolution spectral data.
The physiological status of forest canopy foliage is influenced by a range of factors that affect leaf pigment content and function. Recently, several indices have been developed from remotely sensed data that attempt to provide robust estimates of leaf chlorophyll content. These indices have been developed from either hand-held spectroradiometer spectra or high spectral resolution (or hyperspectral) imagery. We determined if two previously published indices (Datt 1999), which were specifically developed to predict chlorophyll content in eucalypt vegetation by remote sensing at the leaf scale, can be extrapolated accurately to the canopy. We derived the two indices from hand-held spectroradiometer data of eucalypt leaves exhibiting a range of insect damage symptoms. We also derived the indices from spectra obtained from high spectral and spatial resolution Compact Airborne Spectrographic Imager 2 (CASI-2) imagery to determine if reasonable estimates at a scale of < 1 m can be achieved. One of the indices (R 850/R 710 index, where R is reflectance) derived from hand-held spectroradiometer data showed a moderate correlation with relative leaf chlorophyll content (r = 0.59, P < 0.05) for all dominant eucalypt species in the study area. The R (850)/R (710) index derived from CASI-2 imagery yielded slightly lower correlations over the entire data set (r = 0.42, P < 0.05), but correlations for individual species were high (r = 0.77, P < 0.05). A scaling analysis indicated that the R (850)/R (710) index was strongly affected by soil and water cover types when pixels were mixed, but appeared to be invariant to changes in proportions of understory, which may limit its application.
Modelling leaf chlorophyll content in broadleaf and needle leaf canopies from ground, CASI, Landsat TM 5 and MERIS reflectance data
Foliar chlorophyll content in forested ecosystems plays a fundamental role in plant photosynthesis and can indicate vegetation stress and disturbance. However, leaf chlorophyll retrieval is complicated as canopy reflectance in the visible and near-infrared wavelengths is affected by confounding effects not only from leaf pigment concentration but also leaf area index (LAI), canopy architecture, illumination and viewing geometry and understory vegetation. Unlike empirical indices, which are often developed at leaf-level and can be species, site and time specific, a process modelling approach can account for the variation of other variables affecting canopy reflectance; therefore providing a more accurate estimate of chlorophyll content over multiple vegetation species, time-frames and across broader spatial extents. This study used a linked canopy (4-Scale) and leaf (PROSPECT) modelling approach to investigate the ability of radiative transfer models to estimate foliar chemistry for different vegetation types (broadleaf and needle leaf) from optical remote sensing data. Coniferous and deciduous sites were selected in Ontario, Canada, representing different dominant vegetation species, including black spruce (Picea mariana), sugar maple (Acer saccharum) and trembling aspen (Populus tremuloides), and a variety of canopy closures and structures. These sites were sampled over multiple time-frames to collect ground data including leaf area index, leaf reflectance spectra (400–2500 nm) and laboratory leaf chlorophyll content. Canopy reflectance data were acquired from the Compact Airborne Spectrographic Imager (CASI), Landsat 5 TM and Medium Resolution Imaging Spectrometer (MERIS). The model results show that leaf chlorophyll content derived from satellite images demonstrates a good relationship with measured leaf chlorophyll content, with validation results of R2 = 0.62; p < 0.001 (MERIS) and R2 = 0.65; p < 0.001 (Landsat 5 TM), and a strong linearity with negligible systematic bias. CASI data gave a regression coefficient of R2 = 0.41 (p < 0.05) on a reduced dataset. This research provides theoretical and operational bases for the future retrieval of leaf chlorophyll content across different vegetation species, canopy structures and over broad spatial extents; crucial characteristics for inclusion in photosynthesis and carbon cycle models.
Gaussian Process Retrieval of Chlorophyll Content From Imaging Spectroscopy Data
Precise and spatially-explicit knowledge of leaf chlorophyll content (Chl) is crucial to adequately interpret the chlorophyll fluorescence (ChF) signal from space. Accompanying information about the reliability of the Chl estimation becomes more important than ever. Recently, a new statistical method was proposed within the family of nonparametric Bayesian statistics, namely Gaussian Processes regression (GPR). GPR is simpler and more robust than their machine learning family members while maintaining very good numerical performance and stability. Other features include: (i) GPR requires a relatively small training data set and can adopt very flexible kernels, (ii) GPR identifies the relevant bands and observations in establishing relationships with a variable, and finally (iii) along with pixelwise estimations GPR provides accompanying confidence intervals. We used GPR to retrieve Chl from hyperspectral reflectance data and evaluated the portability of the regression model to other images. Based on field Chl measurements from the SPARC dataset and corresponding spaceborne CHRIS spectra (acquired in 2003, Barrax, Spain), GPR developed a regression model that was excellently validated (r2: 0.96, RMSE: 3.82 μg/cm2). The SPARC-trained GPR model was subsequently applied to CHRIS images (Barrax, 2003, 2009) and airborne CASI flightlines (Barrax 2009) to generate Chl maps. The accompanying confidence maps provided insight in the robustness of the retrievals. Similar confidences were achieved by both sensors, which is encouraging for upscaling Chl estimates from field to landscape scale. Because of its robustness and ability to deliver confidence intervals, GPR is evaluated as a promising candidate for implementation into ChF processing chains.
Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data
Leaf chlorophyll content is a useful parameter for assessing vegetation physiological status and dominates the spectral signal of leaf and canopy reflectance at visible wavelengths. Using hyperspectral instruments, we quantified leaf chlorophyll content and optical properties for 255 overstory and understory leaf samples through the growing season in a mature sugar maple (Acer saccharum) stand. Strong seasonal and canopy-height-related differences were observed in both leaf chlorophyll content and leaf reflectance and transmittance spectra. Seasonal and canopy-height-related variation in leaf spectra were closely related to leaf chlorophyll content. We estimated leaf chlorophyll content using two approaches, namely empirical spectral indices, and a mathematical inversion of the leaf optical model PROSPECT. Both estimates were highly correlated with the measured leaf chlorophyll content; however, the spectral indices resulted in greater accuracy, with the best-performing index (modified simple ratio) showing an accuracy of R2 = 0.88 and RMSE = 3.94 µg/cm2. A leaf thickness factor was introduced in the PROSPECT model to take into account the effects of changes in leaf structure on light absorption. The model inversion was improved after incorporating leaf thickness factors based on observed seasonal and canopy-height-related variation in leaf thickness. The improved model had the best performance, with an accuracy of R2 = 0.93 and RMSE = 3.09 µg/cm2 in retrieved leaf chlorophyll concentration in comparison with laboratory measurements.
Chlorophyll Dynamics in Rice (Oryza sativa) Before and After Flowering Based on SPAD (Chlorophyll) Meter Monitoring and its Relation with Grain Yield
The rice plant at any point in time is composed of leaves of physiologically different ages, so it follows that the leaves differ in their contributions to the growth of the whole plant and its grain yield. As the leaf chlorophyll content (Soil Plant Analytical Division value) is the best indicator of photosynthetic activity in rice, the chlorophyll content of rice before and after flowering was determined in a weed management field experiment on direct wet seeded rice. The results indicated that the leaf chlorophyll content at 79 days after sowing correlated well with the grain yield of rice. Multiple regression models also indicated the dependence of rice yield on leaf chlorophyll content before and after flowering.
Estimating Leaf Chlorophyll Content Using Red Edge Parameters
Hyperspectral remote sensing makes it possible to non-destructively monitor leaf chlorophyll content (LCC). This study characterized the geometric patterns of the first derivative reflectance spectra in the red edge region of rapeseed ( Brassica napus L.) and wheat ( Triticum aestivum L.) crops. The ratio of the red edge area less than 718 nm to the entire red edge area was negatively correlated with LCC. This finding allowed the construction of a new red edge parameter, defined as red edge symmetry (RES). Compared to the commonly used red edge parameters (red edge position, red edge amplitude, and red edge area), RES was a better predictor of LCC. Furthermore, RES was easily calculated using the reflectance of red edge boundary wavebands at 675 and 755 nm ( R 675 and R 755 ) and reflectance of red edge center wavelength at 718 nm ( R 718 ), with the equation RES = ( R 718 -R 675 ) / ( R 755 - R 675 ). In addition, RES was simulated effectively with wide wavebands from the airborne hyperspectral sensor AVIRIS and satellite hyperspectral sensor Hyperion. The close relationships between the simulated RES and LCC indicated a high feasibility of estimating LCC with simulated RES from AVIRIS and Hyperion data. This made RES readily applicable to common airborne and satellite hyperspectral data derived from AVIRIS and Hyperion sources, as well as ground-based spectral reflectance data.
A Comparison of Hyperspectral Chlorophyll Indices for Wheat Crop Chlorophyll Content Estimation Using Laboratory Reflectance Measurements
The objective of this paper is to investigate the relationship between a wide range of hyperspectral chlorophyll indices and wheat crop chlorophyll content using laboratory measurements. These measurements included the GER-3700 spectroradiometric data, leaf chlorophyll content using the Soil-Plant Analyses Development (SPAD)-502 meter and leaf chlorophyll content estimated from chemical laboratory analysis. The SPAD-502 readings were correlated with leaf chlorophyll content extracted in the laboratory to establish calibration equations for the computation of chlorophyll-ab (Chl-ab) and chlorophyll-a (Chl-a) content. This resulted in a coefficient of determination (R2) of 0.72 for the Chl-ab content and 0.69 for the Chl-a content and a root mean square error (RMSE) of 3.53 and 1.94 mug/cm2, respectively. These estimates were used to establish relationships against hyperspectral chlorophyll indices calculated from the GER-3700 data. From the investigated indices, the NPCI showed the best results with R2 of 0.84 and RMSE of 11.0. The other indices, such as GNDVI, OSAVI, PSSRa, PSNDa, CAI, HNDVI, and MTCI did not perform satisfactorily. The better ones, but still showing a relatively week relationship with leaf chlorophyll content, are the indices NDPI, SIPI, PRI and SRPI with R2's of 0.56, 0.62, 0.54, and 0.57, respectively and RMSEs of 11.06, 10.27, 11.32, and 10.96 mug/cm2, respectively.
Non-destructive evaluation of chlorophyll content in quinoa and amaranth leaves by simple and multiple regression analysis of RGB image components
Leaf chlorophyll content provides valuable information about physiological status of plants; it is directly linked to photosynthetic potential and primary production. In vitro assessment by wet chemical extraction is the standard method for leaf chlorophyll determination. This measurement is expensive, laborious, and time consuming. Over the years alternative methods, rapid and non-destructive, have been explored. The aim of this work was to evaluate the applicability of a fast and non-invasive field method for estimation of chlorophyll content in quinoa and amaranth leaves based on RGB components analysis of digital images acquired with a standard SLR camera. Digital images of leaves from different genotypes of quinoa and amaranth were acquired directly in the field. Mean values of each RGB component were evaluated via image analysis software and correlated to leaf chlorophyll provided by standard laboratory procedure. Single and multiple regression models using RGB color components as independent variables have been tested and validated. The performance of the proposed method was compared to that of the widely used non-destructive SPAD method. Sensitivity of the best regression models for different genotypes of quinoa and amaranth was also checked. Color data acquisition of the leaves in the field with a digital camera was quick, more effective, and lower cost than SPAD. The proposed RGB models provided better correlation (highest R2) and prediction (lowest RMSEP) of the true value of foliar chlorophyll content and had a lower amount of noise in the whole range of chlorophyll studied compared with SPAD and other leaf image processing based models when applied to quinoa and amaranth.
The effects of temperature and light integral on early vegetative growth and chlorophyll fluorescence of four contrasting genotypes of cacao (Theobroma cacao)
Summary The effect of temperature on early vegetative growth, leaf chlorophyll fluorescence and chlorophyll content was examined on four genotypes of cacao (Amelonado, AMAZ 15-15, SCA 6 and SPEC 54/1). A controlled environment glasshouse was used to simulate the temperature conditions of three cacao-growing regions (Bahia, Brazil; Tafo, Ghana and Lower Perak, Malaysia) over the course of a year. Base temperatures calculated from increments in main stem growth varied from 18.6°C for AMAZ 15/15 to 20.8°C for SPEC 54/1. Temporal variation in Fv/Fm observed for two of the clones (SCA 6 and SPEC 54/1) in two of the compartments were correlated with temperature differences over time. Significant differences were also recorded between genotypes in leaf chlorophyll content. It was shown that variation over time in leaf chlorophyll content could be quantified accurately as a function of temperature and light integral. The results imply that genetic variability exists in cacao in response to temperature stress.
Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery
Accurate estimation of leaf chlorophyll content (Cab) from remote sensing is of tremendous significance to monitor the physiological status of vegetation or to estimate primary production. Many vegetation indices (Vls) have been developed to retrieve Cab at the canopy level from meter- to decameter-scale reflectance observations. However, most of these Vls may be affected by the possible confounding influence of canopy structure. The objective of this study is to develop methods for Cab estimation using millimeter to centimeter spatial resolution reflectance imagery acquired at the field level. Hyperspectral images were acquired over sugar beet canopies from a ground-based platform in the 400-1000 nm range, concurrently to Cab, green fraction (GF), green area index (GAI) ground measurements. The original image spatial resolution was successively degraded from 1 mm to 35 cm, resulting in eleven sets of hyperspectral images. Vegetation and soil pixels were discriminated, and for each spatial resolution, measured Cab values were related to various Vls computed over four sets of reflectance spectra extracted from the images (soil and vegetation pixels, only vegetation pixels, 50% darkest and brightest vegetation pixels). The selected Vls included some classical Vls from the literature as well as optimal combinations of spectral bands, including simple ratio (SR), modified normalized difference (mND) and structure insensitive pigment index (SIPI). In the case of mND and SIPI, the use of a blue reference band instead of the classical near-infrared one was also investigated. For the eleven spatial resolutions, the four pixel selections and the five VI formats, similar band combinations are obtained when optimizing VI performances: the main bands of interest are generally located in the blue, red, red edge and near-infrared domains. Overall, mND(blue)[728,850] defined as (R-440-R-728)/(R-440+R-850) and computed over the brightest green pixels obtains the best correlations with C-ab for spatial resolutions finer than 8.8 cm with a root mean square error of prediction better than 2.6 mu g/cm(2). Conversely, mND(blue)[728,8501 poorly correlates with variations in GF and GAI, thus reducing the risk of deriving non-causal relationships with Cab that would actually be due to the covariance between C-ab and these canopy structure variables. As mND(blue)[728,8501 can be calculated from most current multispectral sensors, it is therefore a promising VI to retrieve C-ab from millimeter- to centimeter-scale reflectance imagery. (C) 2017 Elsevier Inc. All rights reserved.
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