Canopy Spectral Reflectance Measurements Research Articles

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND705 (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage.

Applicability of optimized hyperspectral indices for estimating Betalain content in Suaeda salsa

ABSTRACT Betalains (Bt) are a collective group of natural, edible, red-yellow plant pigments, and are an important biochemical parameter in vegetable growth. Betalains are believed to have fungicidal activity and their presence in plants is a key to explaining the physiological response and resistance of plants caused by different environmental stress factors or seasonal fluctuations. Hyperspectral remote sensing has proven potential to understand many biochemical processes widely used in investigating vegetation growth condition. To better understand vegetation containing betalain pigments, for growth monitoring and agronomic decision-making, a novel betalain source, Suaeda salsa (S. salsa) was studied by extracting betalain at different phenological periods, and registering the results with time-course measurements of canopy spectral reflectance via hyperspectral remote imaging. Partial least squares regression (PLSR) analysis was undertaken with three spectral transformation methods (the raw hyperspectral reflectance (R), first derivative reflectance (FDR) and second derivative reflectance (SDR). The variable importance in projection (VIP) score resulting from PLSR model was used to determine the key spectral wavelengths and reduce the dimensionality of the hyperspectral reflectance data matrix. The study results demonstrated that Bt content had a significant correlation with the effective wavelength (FD-R 436nm, R 847nm, R 848nm, R 860nm, R 871nm, R 971nm and SD-R 437nm, R 496nm, R 499nm, R 508nm, R 740nm, R 772nm, R 774nm, R 780nm) and optimized spectral indices (simple normalized difference spectral indices (NDSI(802nm,408nm), NDSI(781nm,776nm)), normalized polarization indices (NPDI(781nm,775nm), NPDI(781nm,776nm)), ratio spectral indices (RSI(781nm,776nm), RSI(781nm,775nm)), chlorophyll indices(CI(781nm,776nm), CI(781nm, 775nm)) derived from first derivative spectral reflectance. The PLSR-5 (constructed with the spectral indices) model resulted in an R 2 val (determination coefficient) of 0.79 and root mean square error (RMSEval) was 0.81 μg cm−2. Compared to PLSR-3 model built by sensitive bands, the PLSR-5 model estimation accuracy increased 15%. These results suggest the possibility of estimating Bt content using hyperspectral indices, which contain sufficient information for a successful estimation. Study findings will help researchers in deciding suitable Bt indices for S. salsa growth status and stress assessment, and will facilitate detection growth condition of the vegetables with betalain pigments.

Predicting grain yield and protein content using canopy reflectance in maize grown under different water and nitrogen levels

Predicting grain yield and protein content of maize using spectral reflectance data is very important for improved agricultural production. In this study, we predicted the grain yield and protein content of maize grown under different irrigation and nitrogen levels in 2018 and 2019 based on canopy spectral reflectance measurements at the V6 (sixth leaf), VT (tassel), and R2 (blister) stages. We developed a predictive approach, namely, spectral reflectance–physiological parameters–productivity, to predict grain yield and protein content on maize crop. First, the quantitative relationships between grain yield and protein content and physiological parameters (canopy chlorophyll content [CCC], leaf carbon accumulation [LCA], leaf nitrogen content [LNC], and leaf nitrogen accumulation [LNA]) were analysed. Then, vegetation indices (VIs) and wavelet features based on spectral reflectance were used to establish estimation models for physiological parameters. The physiological parameters were used as a bridge to connect the spectral reflectance data with grain yield and protein content. The purpose was to establish spectral inversion models to indirectly estimate grain yield and protein content. Results showed that grain yield had a significant linear relationship with CCC and LCA. In addition a grain protein content and LNC and LNA were also significantly related under different water, and nitrogen availability. The physiological parameter models with ratio vegetation indices (RVI), biorthogonal 3.3 (bior3.3), and reverse biorthogonal 1.5 (rbio1.5) were reliable in terms of predictability and applicability. Independent data verification suggested that grain yield was predicted using RVI769,758 (R2 = 0.773, RMSE = 2.509) in water availability, rbio1.5781,29 (R2 = 0.744, RMSE = 2.850) in nitrogen availability, and rbio1.5772,11 (R2 = 0.506, RMSE = 2.297) in water–nitrogen availability. In addition, the grain protein content was predicted using RVI793,757 (R2 = 0.704, RMSE = 0.744) in water availability, bior3.3743,19 (R2 = 0.717, RMSE = 0.957) in nitrogen availability, and RVI492,418 (R2 = 0.715, RMSE = 1.224) in water–nitrogen availability. Therefore, maize grain yield and protein content can be accurately predicted using our modelling approach.

Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat

Wheat nitrogen use efficiency must be improved to reduce the need for nitrogen (N) fertilizers. This study was conducted to determine if measurement of canopy spectral reflectance (CSR) could be used to non-destructively and indirectly select wheat genotypes with improved N use traits. Canopy spectral reflectance measurements were collected during grain fill in a 299-genotype trial planted near Ithaca, NE in 2012 and 2013, and 28 vegetation indices were calculated from the data. The relationship between vegetation indices (VIs) and nitrogen use (NU) traits was investigated. Vegetation indices were highly heritable in both years and predicted these NU traits: anthesis biomass, mature biomass, grain N yield, grain yield, N harvest index, N utilization efficiency, N uptake efficiency and post anthesis N uptake. One VI, Maccioni, performed most consistently and was significantly related to several NU traits. The results of this study indicate that VIs, particularly the Maccioni index, could be used in wheat breeding programs to non-destructively phenotype for NU traits.

Genome-wide association mapping of soybean chlorophyll traits based on canopy spectral reflectance and leaf extracts.

BackgroundChlorophyll is a major component of chloroplasts and a better understanding of the genetic basis of chlorophyll in soybean [Glycine max (L.) Merr.] might contribute to improving photosynthetic capacity and yield in regions with adverse environmental conditions. A collection of 332 diverse soybean genotypes were grown in 2 years (2009 and 2010) and chlorophyll a (eChl_A), chlorophyll b (eChl_B), and total chlorophyll (eChl_T) content as well as chlorophyll a/b ratio (eChl_R) in leaf tissues were determined by extraction and spectrometric determination. Total chlorophyll was also derived from canopy spectral reflectance measurements using a model of wavelet transformed spectra (tChl_T) as well as with a spectral reflectance index (iChl_T).ResultsA genome-wide associating mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify loci associated with the extract based eChl_A, eChl_B, eChl_R and eChl_T measurements and the two canopy spectral reflectance-based methods (tChl_T and iChl_T). A total of 23 (14 loci), 15 (7 loci) and 14 SNPs (10 loci) showed significant association with eChl_A, eChl_B and eChl_R respectively. A total of 52 unique SNPs were significantly associated with total chlorophyll content based on at least one of the three approaches (eChl_T, tChl_T and iChl_T) and likely tagged 27 putative loci for total chlorophyll content, four of which were indicated by all three approaches.ConclusionsResults presented here show that markers for chlorophyll traits can be identified in soybean using both extract-based and canopy spectral reflectance-based phenotypes, and confirm that high-throughput phenotyping-amenable canopy spectral reflectance measurements can be used for association mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0861-x) contains supplementary material, which is available to authorized users.

Identification of Genomic Loci Associated with the Photochemical Reflectance Index by Genome-Wide Association Study in Soybean.

The photochemical reflectance index (PRI) is determined from canopy spectral reflectance measurements and can provide important information about photosynthesis. The PRI can be used to assess the epoxidation state of xanthophyll pigments, which provides information on nonphotochemical quenching (NPQ) and the amount of energy used for photosynthesis. Genome-wide association analyses were conducted to identify single-nucleotide polymorphisms (SNPs) and genomic loci associated with PRI using data from a soybean [ (L.) Merr.] diversity panel grown under field conditions over 2 yr. Based on a mixed linear model (MLM), 31 unique candidate SNPs that identify 15 putative loci on 11 chromosomes were identified. Several candidate genes known to be associated with NPQ, photosynthesis, and sugar transport processes were identified in the proximity of 10 putative loci. Violaxanthin de-epoxidase, one of the identified genes, is directly involved in the xanthophyll cycle, which plays a major role in NPQ. This study is the first to identify genomic loci for PRI and illustrates the potential of canopy spectral reflectance measurements for high-throughput phenotyping of a photosynthesis related trait. Significant SNPs, candidate genes, and genotypes contrasting for PRI identified in this study may prove useful for crop improvement efforts.

Association Mapping of Total Carotenoids in Diverse Soybean Genotypes Based on Leaf Extracts and High-Throughput Canopy Spectral Reflectance Measurements.

Carotenoids are organic pigments that are produced predominantly by photosynthetic organisms and provide antioxidant activity to a wide variety of plants, animals, bacteria, and fungi. The carotenoid biosynthetic pathway is highly conserved in plants and occurs mostly in chromoplasts and chloroplasts. Leaf carotenoids play important photoprotective roles and targeted selection for leaf carotenoids may offer avenues to improve abiotic stress tolerance. A collection of 332 soybean [Glycine max (L.) Merr.] genotypes was grown in two years and total leaf carotenoid content was determined using three different methods. The first method was based on extraction and spectrophotometric determination of carotenoid content (eCaro) in leaf tissue, whereas the other two methods were derived from high-throughput canopy spectral reflectance measurements using wavelet transformed reflectance spectra (tCaro) and a spectral reflectance index (iCaro). An association mapping approach was employed using 31,253 single nucleotide polymorphisms (SNPs) to identify SNPs associated with total carotenoid content using a mixed linear model based on data from two growing seasons. A total of 28 SNPs showed a significant association with total carotenoid content in at least one of the three approaches. These 28 SNPs likely tagged 14 putative loci for carotenoid content. Six putative loci were identified using eCaro, five loci with tCaro, and nine loci with iCaro. Three of these putative loci were detected by all three carotenoid determination methods. All but four putative loci were located near a known carotenoid-related gene. These results showed that carotenoid markers can be identified in soybean using extract-based as well as by high-throughput canopy spectral reflectance-based approaches, demonstrating the utility of field-based canopy spectral reflectance phenotypes for association mapping.

Detecção do mofo-branco no feijoeiro, utilizando características espectrais

Neste trabalho, objetivou-se estudar a resposta espectral de plantas de feijoeiro à infecção por Sclerotinia sclerotiorum, fungo causador do mofo-branco. Foram conduzidos dois ensaios, no delineamento em blocos ao acaso, com cinco tratamentos e quatro repetições, no primeiro ensaio, e seis tratamentos e quatro repetições, no segundo. Medidas de reflectância espectral do dossel do feijoeiro, entre os comprimentos de ondas de 400 a 850 nm, foram tomadas aos 60, 71, 91 e 102 dias após a emergência (DAE), no primeiro ensaio, e aos 13, 27, 34, 56, 71 e 91 DAE, no segundo. Os dados espectrais foram transformados em índices de vegetação: da diferença normalizada (NDVI), da diferença de verde normalizado (GNDVI) e ajustado para o solo (SAVI). A severidade do mofo-branco foi avaliada, visualmente, aos 102 DAE, no primeiro ensaio, e aos 92 DAE, no segundo. Houve correlação significativa apenas no segundo ensaio, entre a severidade da doença e o NDVI, aos 71 DAE. A resposta espectral do feijoeiro pode ser utilizada para a detecção da ocorrência do mofo-branco e, consequentemente, como ferramenta no manejo integrado.

Characterizing Changes in Soybean Spectral Response Curves with Breeding Advancements

ABSTRACTSoybean (Glycine max (L.) Merr.) crop yield has steadily increased in the past 60 yr due in part to breeding advances. Relations between canopy spectral reflectance to specific plant functions may help characterize the impact of breeding on soybean cultivar development. The objectives of this study were to: 1) find specific regions of the soybean canopy spectral reflectance response curves that show genotypic differences; and 2) determine the effect of the breeding process on spectral reflectance response curves of soybean cultivars. Canopy spectral reflectance measurements were taken on 20 maturity group III (MGIII) and 20 maturity group IV (MGIV) soybean cultivars ranging in release year from 1923 to 2010 (arranged in a randomized complete block design) in 2011 and 2012 in Manhattan, KS. Large genotypic differences were found among cultivars, especially in the green (500 nm–600 nm), red (600 nm–700 nm), and red‐edge (700nm–730 nm) portions of the spectra. Reflectance in the visible (VIS) (400–700 nm), red‐edge (700–730 nm), and near‐infrared (NIR) (730–1305) portions of the spectra varied with year of release (YOR) among cultivars. The more recently released cultivars tended to have lower reflectance values in the VIS and red‐edge spectra portions and higher values in the NIR portion of the spectra than earlier‐released cultivars. Results also indicate that spectral reflectance in the NIR portion of the spectra are highly confounded with maturity and other agronomic traits. These results indicate that breeding advancement has had an impact on canopy spectral reflectance curves and the VIS and red‐edge portions of the spectra may be a source of variation for further cultivar development and advancement.

Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest

Aims Understanding of the ecophysiological dynamics of forest canopy photosynthesis and its spatial and temporal scaling is crucial for revealing ecological response to climate change. Combined observations and analyses of plant ecophysiology and optical remote sensing would enable us to achieve these studies. In order to examine the utility of spectral vegetation indices (VIs) for assessing ecosystem-level photosynthesis, we investigated the relationships between canopy-scale photosynthetic productivity and canopy spectral reflectance over seasons for 5 years in a cool, temperate deciduous broadleaf forest at ‘Takayama’ super site in central Japan. Methods Daily photosynthetic capacity was assessed by in situ canopy leaf area index (LAI), (LAI × Vcmax [single-leaf photosynthetic capacity]), and the daily maximum rate of gross primary production (GPPmax) was estimated by an ecosystem carbon cycle model. We examined five VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red vegetation index (GRVI), chlorophyll index (CI) and canopy chlorophyll index (CCI), which were obtained by the in situ measurements of canopy spectral reflectance. Important Findings Our in situ observation of leaf and canopy characteristics, which were analyzed by an ecosystem carbon cycling model, revealed that their phenological changes are responsible for seasonal and interannual variations in canopy photosynthesis. Significant correlations were found between the five VIs and canopy photosynthetic capacity over the seasons and years; four of the VIs showed hysteresis-type relationships and only CCI showed rather linear relationship. Among the VIs examined, we applied EVI–GPPmax relationship to EVI data obtained by Moderate Resolution Imaging Spectroradiometer to estimate the temporal and spatial variation in GPPmax over central Japan. Our findings would improve the accuracy of satellite-based estimate of forest photosynthetic productivity in fine spatial and temporal resolutions, which are necessary for detecting any response of terrestrial ecosystem to meteorological fluctuations.

Effect of Nitrogen and Endophytic Bacteria on Biophysical and Spectral Parameters of Wheat Canopy

Nitrogen (N) is the primary limiting factor to increase wheat (Triticum aestivum L.) yield in Brazil, and the use of N‐fixing agents might be an alternative to increase wheat production. A field experiment was conducted in 2006 in São Paulo State, Brazil, with three N rates (0, 60, and 120 kg ha−1) and three endophytic bacteria (IAC‐HT‐11, IAC‐HT‐12, and IAC‐AT‐8) to evaluate their combined impact on biophysical (leaf area index [LAI] and grain yield) and spectral parameters (normalized difference vegetation index [NDVI], absorption band parameters) of wheat and to determine the spectral parameters that best correlated with LAI and grain yield. Leaf area index and canopy spectral reflectance measurements were made at jointing, heading, and ripening growth stages for this purpose. The statistical relationships between biophysical and spectral parameters were evaluated within and among the treatments. Statistical results showed that the N rates significantly affected biophysical and spectral parameters. Bacteria inoculation presented a significant effect only on grain yield, which is consistent with results reported in the literature. The NDVI, followed by absorption band parameters PB and AB at 1205 nm, provided the best estimates of LAI and grain yield during the heading stage, indicating that it may be the best stage for remote sensing–based yield mapping.

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.

Assessing Canopy Spectral Reflectance of Hybrid Bermudagrass under Various Combinations of Nitrogen and Water Treatments

Remote sensing technology was tested as a non-destructive tool to assess the nitrogen (N) and water status of hybrid bermudagrass 'Tifway' [Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt-Davy]. Ten combinations of water (based on leaching fraction: LF) and N treatments including cyclic and steady treatments were imposed on 20 plots (6.1 × 6.1 m) from October 2001 to October 2002 and January 2003 to June 2003. Each plot contained a non-weighing lysimeter which was used to assess the weekly water balance. Leaf xylem water potential, canopy temperature, tissue moisture content (TM), chlorophyll meter index, visual color ratings, and tissue nitrogen concentration (TN) were assessed twice per month. Canopy spectral reflectance measurements were also acquired twice per month using a portable spectroradiometer mounted on a movable cart. Analysis of variance (ANOVA) showed that spectral reflectance at individual wavelengths, vegetation indices, and soil-plant parameters separated based on N, LF and/or N × LF interaction (P < 0.05). Prediction equations for individual wavelengths and vegetation indices were generated from plant and soil parameters that included TN (Nitrogen Content index (NC): R2 = 0.73), TM (R555 : R2 = 0.66), or TM × TN (Normalized Difference Vegetation Index (NDVI) : R2 = 0.71). Multiple regression analysis also produced significant predictive models for four plant variables based on a selected number of wavelengths (between 3 and 9): canopy color (R2 = 0.48), TN (R2 = 0.56), TM × TN (R2 = 0.43), and chlorophyll meter index (R2 = 0.78). Normalized difference vegetation index (NDVI) showed a strong threshold relationship with canopy color rating (R2 = 0.64, P < 0.001, n = 194) and a consistent positive curvilinear relationship with the chlorophyll meter index (R2 = 0.75, P < 0.001, n = 200). The Water Band Index (WBI) significantly correlated with TM (R2 = 0.76, P < 0.001) and N content index (NC) correlated with TN (R2 = 0.45, P < 0.001) for single day measurements, but not for the larger data sets. Results from this experiment demonstrate that canopy reflectance measured with a ground-based spectroradiometer has potential for improving turfgrass N and irrigation management under field conditions, which could lead to significant N and water savings on large turfgrass areas such as golf courses and sports fields.

Calculations of canopy spectral reflectances using the goudriaan reflectance model and their experimental evaluation

Under consideration are two main problems: 1) the dependences between the coefficients of canopy spectral reflectance (CSRs) for nadir and solar altitudes over 45° and the relative surface of phytoelements, their angular distribution, and coefficients of spectral reflectance of soil and individual phytoelements, as based on the theoretical model of reflection offered by Goudriaan; and 2) the adequacy of this model in terms of the ground-based measurements of CSRs for wheat, ryegrass, corn, and soybean. The use of a wide range of input values of the model permitted us to express the relative changes in the calculated CSRs due to the relative surface of phytoelements and their mean inclination angle as generalized plots in which the coordinates are the coefficients of spectral reflection of phytoelements (ϱ) and of soil ( ϱ s ). The verification in a natural experiment has shown that in the visible and near IR bands the Goudriaan model properly describes the tendencies in the CSRs changes as a function of the relative surface of phytoelements with differently combined values ϱ, ϱ s and of angular orientation. In the case of wheat, the mean divergences between the calculated and measured CSRs were about ± 15%. Maximum divergences were observed in the visible band for highly closed sowings of cereals and for sparse sowings of intertilled crops.

Estimation of Plant Biomass by Spectral Reflectance in an East African Grassland

Estimation of Plant Biomass by Spectral Reflectance in an East African Grassland

Crop identification and leaf area index calculations with Landsat multitemporal data

Formulas are given to convert land-based vegetative canopy spectral reflectance measurements into Landsat digital counts in the four channels for a clear standard atmosphere. These formulas are shown to predict the curves found by plotting multitemporal Landsat trajectories. With correct interpretation, plots of channel 3 versus channel 4 Landsat data for crops are shown to yield information on both crop identification and crop leaf area index.