Estimating leaf nitrogen concentration in heterogeneous crop plants from hyperspectral reflectance

The estimation of leaf nitrogen concentration (LNC) in crop plants is an effective way to optimize nitrogen fertilizer management and to improve crop yield. The objectives of this study were to (1) analyse the spectral features, (2) explore the spectral indices, and (3) investigate a suitable modelling strategy for estimating the LNC of five species of crop plants (rice (Oryza sativa L.), corn (Zea mays L.), tea (Camellia sinensis), gingili (Sesamum indicum), and soybean (Glycine max)) with laboratory-based visible and near-infrared reflectance spectra (300–2500 nm). A total of 61 leaf samples were collected from five species of crop plant, and their LNC and reflectance spectra were measured in laboratories. The reflectance spectra of plants were reduced to 400–2400 and smoothed using the Savitzky–Golay (SG) smoothing method. The normalized band depth (NBD) values of all bands were calculated from SG-smoothed reflectance spectra, and a successive projections algorithm-based multiple linear regression (SPA-MLR) method was then employed to select the spectral features for five species. The SG-smoothed reflectance spectra were resampled using a spacing interval of 10 nm, and normalized difference spectral index (NDSI) and three-band spectral index (TBSI) were calculated for all wavelength combinations between 400 and 2400 nm. The NDSI and TBSI values were employed to calibrate univariate regression models for each crop species. The leave-one-out cross-validation procedure was used to validate the calibrated regression models. Study results showed that the spectral features for LNC estimation varied among different crop species. TBSI performed better than NDSI in estimating LNC in crop plants. The study results indicated that there was no common optimal TBSI and NDSI for different crop species. Therefore, we suggest that, when monitoring LNC in heterogeneous crop plants with hyperspectral reflectance, it might be appropriate to first classify the data set considering different crop species and then calibrate the model for each species. The method proposed in this study requires further testing with the canopy reflectance and hyperspectral images of heterogeneous crop plants.     

Estimating the irradiance spectrum from measurements in a limited number of spectral bands

Accurate measurement and characterisation of fluctuations in the irradiance environment is important for many areas of optical remote sensing. This paper describes a method of estimating spectral irradiance over the region 400-1000 nm from the radiance of a calibrated reference panel, measured in four narrow spectral bands (FWHM approx. 10 nm). The reproducibility of the method was found to have an average root-mean-squared error of approximately 30 mW m^− 2 nm^− 1 over the region 400-1000 nm when applied to spectra covering a range of clear-sky conditions typical of mid-latitude temperate regions. This was approximately twice as precise as the sequential method, even when the interval between target and reference panel measurements was very short (median interval 23 s). The method provides an alternative to linear interpolation between successive reference panel measurements and is particularly appropriate for conditions when irradiance is varying in a non-systematic way, for example, during the passage of sub-visual clouds.     

Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data

Nitrogen (N) is one of the most important limiting nutrients for sugarcane production. Conventionally, sugarcane N concentration is examined using direct methods such as collecting leaf samples from the field followed by analytical assays in the laboratory. These methods do not offer real-time, quick, and non-destructive strategies for estimating sugarcane N concentration. Methods that take advantage of remote sensing, particularly hyperspectral data, can present reliable techniques for predicting sugarcane leaf N concentration. Hyperspectral data are extremely large and of high dimensionality. Many hyperspectral features are redundant due to the strong correlation between wavebands that are adjacent. Hence, the analysis of hyperspectral data is complex and needs to be simplified by selecting the most relevant spectral features. The aim of this study was to explore the potential of a random forest (RF) regression algorithm for selecting spectral features in hyperspectral data necessary for predicting sugarcane leaf N concentration. To achieve this, two Hyperion images were captured from fields of 6–7 month-old sugarcane, variety N19. The machine-learning RF algorithm was used as a feature-selection and regression method to analyse the spectral data. Stepwise multiple linear (SML) regression was also examined to predict the concentration of sugarcane leaf N after the reduction of the redundancy in hyperspectral data. The results showed that sugarcane leaf N concentration can be predicted using both non-linear RF regression (coefficient of determination, R ²?=?0.67; root mean square error of validation (RMSEV)?=?0.15%; 8.44% of the mean) and SML regression models (R ²?=?0.71; RMSEV?=?0.19%; 10.39% of the mean) derived from the first-order derivative of reflectance. It was concluded that the RF regression algorithm has potential for predicting sugarcane leaf N concentration using hyperspectral data.     

Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis

The dynamics of foliar chlorophyll concentrations have considerable significance for plant-environment interactions, ecosystem functioning and crop growth. Hyperspectral remote sensing has a valuable role in the monitoring of such dynamics. This study focussed upon improving the accuracy of chlorophyll quantification by applying wavelet analysis to reflectance spectra. Leaf-scale radiative transfer models were used to generate very large spectral data sets with which to develop and rigorously test refinements to the approach and compare it with existing spectral indices. The results demonstrated that by decomposing leaf spectra, the resultant wavelet coefficients can be used to generate accurate predictions of chlorophyll concentration, despite wide variations in the range of other biochemical and biophysical factors that influence leaf reflectance. Wavelet analysis outperformed predictive models based on untransformed spectra and a range of spectral indices. The paper discusses the possibilities for further refining the wavelet approach and for extending the technique to the sensing of a variety of vegetation properties at a range of spatial scales.     

Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra

Remote sensing is a promising tool that provides quantitative and timely information for crop stress detection over large areas. Nitrogen (N) is one of the important nutrient elements influencing grain yield and quality of winter wheat (Triticum aestivum L.). In this study, canopy spectral parameters were evaluated for N status assessment in winter wheat. A winter wheat field experiment with 25 different cultivars was conducted at the China National Experimental Station for Precision Agriculture, Beijing, China. Wheat canopy spectral reflectance over 350–2500 nm at different stages was measured with an ASD FieldSpec Pro 2500 spectrometer (Analytical Spectral Devices, Boulder, CO, USA) fitted with a 25° field of view (FOV) fibre optic adaptor. Thirteen narrow-band spectral indices, three spectral features parameters associated with the absorption bands centred at 670 and 980 nm and another three related to reflectance maximum values located at 560, 920, 1690 and 2230 nm were calculated and correlated with leaf N concentration (LNC) and canopy N density (CND). The results showed that CND was a more sensitive parameter than LNC in response to the variation of canopy-level spectral parameters. The correlation coefficient values between LNC and CND, on the one hand, and narrow-band spectral indices and spectral features parameters, on the other hand, varied with the growth stages of winter wheat, with no predominance of a single spectral parameter as the best variable. The differences in correlation results for the relationships of CND and LNC with narrow-band spectral indices and spectral features parameters decreased with wheat plant developing from Feekes 4.0 to Feekes 11.1. The red edge position (REP) was demonstrated to be a good indicator for winter wheat LNC estimation. The absorption band depth (ABD) normalized to the area of absorption feature (NBD) at 670 nm (NBD670) was the most reliable indicator for winter wheat canopy N status assessment.     

Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shortwave infrared subsystem can acquire images of active fires during daytime and night-time from a polar orbit, providing useful data on fire properties at a nominal spatial resolution of 30 m. Binary fire/no-fire counts of ASTER pixels have also been useful in evaluating the performance of widely-used fire products from the Moderate-Resolution Imaging Spectroradiometer (MODIS), which have a nominal spatial resolution of 1 km. However, the ASTER fire pixels are actually mixed pixels that can contain flaming, smouldering and non-burning components, and ASTER fire pixel counts provide no information about the sizes or temperatures of these subpixel components. This paper uses multiple endmember spectral mixture analysis (MESMA) to estimate subpixel fire sizes and temperatures from a night-time ASTER image of a fire in California, USA, demonstrating new methods that can provide information on fires not available from other sources. As a fire's size and its temperature exert strong influences on its gas and aerosol emissions, ecological impact and spreading rates, these MESMA estimates from ASTER imagery could contribute valuable new information towards monitoring, forecasting and understanding the behaviour and impacts of many fires worldwide.     

Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis

Extensive, in situ, reflectance spectra (i.e., 252 bands) were acquired for the dominant botanical and substrate classes within Prentiss Bay and Horseshoe Bay, Lake Huron. These spectral radiance measurements were transformed into relative percent reflectance and then resampled to emulate the band configurations of the airborne, hyperspectral imagery that was also acquired of the sites. Second-derivative analysis was applied to these transformed spectra in order to identify which spectral bands were the most botanically explanative (i.e., optimal) for the differentiation of coastal wetland vegetation in the Great Lakes.This research identified 8 optimal bands in the visible-NIR wavelength region (in order of decreasing importance: 685.5, 731.5, 939.9, 514.9, 812.3, 835.5, 823.9 and 560.1 nm) that appear to contain the majority of the coastal wetland information content of the full spectral resolution, 48-band, hyperspectral signatures. A reduction of band number without significant information loss is important because it makes it practical to utilize small pixels without fear of sacrificing the ability to differentiate the botanical communities.     

Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model

In this paper, we present a theoretical and modeling framework to estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR_canopy), leaf (FAPAR_leaf ), and chlorophyll (FAPAR_chl), respectively. FAPAR_canopy is an important biophysical variable and has been used to estimate gross and net primary production. However, only PAR absorbed by chlorophyll is used for photosynthesis, and therefore there is a need to quantify FAPAR_chl. We modified and coupled a leaf radiative transfer model (PROSPECT) and a canopy radiative transfer model (SAIL-2), and incorporated a Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) for model inversion, which provides probability distributions of the retrieved variables. Our two-step procedure is: (1) to retrieve biophysical and biochemical variables using coupled PROSPECT + SAIL-2 model (PROSAIL-2), combined with multiple daily images (five spectral bands) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor; and (2) to calculate FAPAR_canopy, FAPAR_leaf and FAPAR_chl with the estimated model variables from the first step. We evaluated our approach for a temperate forest area in the Northeastern US, using MODIS data from 2001 to 2003. The inverted PROSAIL-2 fit the observed MODIS reflectance data well for the five MODIS spectral bands. The estimated leaf area index (LAI) values are within the range of field measured data. Significant differences between FAPAR_canopy and FAPAR_chl are found for this test case. Our study demonstrates the potential for using a model such as PROSAIL-2, combined with an inverse approach, for quantifying FAPAR_chl, FAPAR_leaf, FAPAR_canopy, biophysical variables, and biochemical variables for deciduous broadleaf forests at leaf- and canopy-levels over time.     

Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.     

Action recognition from only somatosensory information using spectral learning in a hidden Markov model

Human action classification is fundamental technology for robots that have to interpret a human’s intended actions and make appropriate responses, as they will have to do if they are to be integrated into our daily lives. Improved measurement of human motion, using an optical motion capture system or a depth sensor, allows robots to recognize human actions from superficial motion data, such as camera images containing human actions or positions of human bodies. But existing technology for motion recognition does not handle the contact force that always exists between the human and the environment that the human is acting upon. More specifically, humans perform feasible actions by controlling not only their posture but also the contact forces. Furthermore these contact forces require appropriate muscle tensions in the full body. These muscle tensions or activities are expected to be useful for robots observing human actions to estimate the human’s somatosensory states and consequently understand the intended action. This paper proposes a novel approach to classifying human actions using only the activities of all the muscles in the human body. Continuous spatio-temporal data of the activity of an individual muscle is encoded into a discrete hidden Markov model (HMM), and the set of HMMs for all the muscles forms a classifier for the specific action. Our classifiers were tested on muscle activities estimated from captured human motions, electromyography data, and reaction forces. The results demonstrate their superiority over commonly used HMM-based classifiers.     

An analysis of spectral discrimination between corn and soybeans using a row crop reflectance model

MSS and TM band reflectance calculations of soybean and corn were made using a row crop reflectance model. Crop geometries representing conditions at two different times during the growing season—mid-July and mid-August—were used in order to compare the soybean-corn discrimination potential of MSS bands and TM bands at these two times. The model results confirm experimental evidence that the TM bands can be used for soybean-corn discrimination earlier in the season than can the MSS bands. The reflectance model results suggest that the sensitivity of the TM mid-IR bands to exposed soil between rows of the early soybean canopy is responsible for the early discrimination capability.     

Capturing asymmetrical spectral regrowth in RF systems using a multislice behavioral model and enhanced envelop transient analysis

Nonlinear distortion in RF and microwave systems results in spectral regrowth of digitally modulated signals. The distortion above and below the main channel can be at different levels and this is attributed to baseband effects. This article presents a new multislice behavioral‐model architecture that captures this asymmetry and can be implemented in a variety of circuit simulators, including SPICE, harmonic balance (HB), envelope transient (ET), and system simulators. The work is experimentally validated using an HBT power amplifier at 2.5 GHz driven by a WCDMA signal. The model is used with envelope transient circuit simulation which is enhanced to accommodate an arbitrary baseband transfer function. © 2006 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2006.     

Discriminant analysis of nitrogen treatments in switchgrass and high biomass sorghum using leaf and canopy-scale reflectance spectroscopy

Recent advances in imaging, laboratory, and field spectroscopy (sometimes referred to as hyperspectral remote sensing) provide a unique opportunity to obtain critical information needed for understanding nitrogen (N) management in crop production systems. Therefore, the objective of this study was to identify wavelength regions and phenological timing useful for the prediction of N status from canopy and leaf spectra. Leaf and canopy spectral data were collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals during 2011 and 2012. The crops evaluated in the study were switchgrass ‘Alamo’ (Panicum virgatum L.) and high biomass sorghum ‘Blade 5200’ (Sorghum bicolor) grown to evaluate N applications rates on biomass yield and quality. The optimal wavelengths were determined based on principal component analysis (PCA) and the separation of the N treatments using stepwise discriminant analysis (SDA). The results showed similar canopy and leaf-scale reflectance for high biomass sorghum but not for switchgrass. The wavelengths found to be most important for separating the N treatments were 520–560, 650–690 nm (visible region), and 710–730 nm (red-edge region). Triangular greenness index (TGI) was the most useful index for discriminating the N application rates. The best time for differentiating the different N treatments was 4–6 weeks after planting or 2–4 weeks after N fertilization in high biomass sorghum and within 4 weeks after green-up in switchgrass. In general, the results indicate that spectroscopy is a viable tool that could be used to estimate the biochemical and biophysical characteristics in bioenergy crop production systems.     

Estimating the nitrogen nutrition index in grass seed crops using a UAV-mounted multispectral camera

The application of adequate nitrogen (N) fertilizers to grass seed crops is important to achieve high seed yield. Application of N will inevitably result in over-fertilization on some fields and, concomitantly, an increased risk of adverse environmental impacts, such as ground- and/or surface-water contamination. This study was designed to estimate the N status of two grass seed crops: red fescue (Festuca rubra L.) and perennial ryegrass (Lolium perenne L.) using images captured with an unmanned aerial vehicle (UAV) mounted multispectral camera. Two types of UAV, a fixed-wing UAV and a multi-rotor UAV, operating at two different heights and mounted with the same multispectral camera, were used in different field experiments at the same location in Denmark in the period from 432 to 861 growing degree-days. Seven vegetation indices, calculated from multispectral images with four bands: red, green, red edge and near infrared (NIR), were evaluated for their relationship to dry matter (DM), N concentration, N uptake and N nutrition index (NNI). The results showed a better prediction of N concentration, N uptake and NNI, than DM using vegetation indices. Furthermore, among all vegetation indices, two red-edge-based indices, normalized difference red edge (NDRE) and red edge chlorophyll index (CI_RE), performed best in estimating N concentration (R² = 0.69–0.88), N uptake (R² = 0.41–0.84) and NNI (R² = 0.47–0.86). In addition, there was no effect from the choice of UAV, and thereby flight height, on the estimation of NNI. The choice of UAV type therefore seems not to influence the possibility of diagnosing N status in grass seed crops. We conclude that it is possible to estimate NNI based on multispectral images from drone-mounted cameras, and the method could guide farmers as to whether they should apply additional N to the field. We also conclude that further research should focus on estimating the quantity of N to apply and on further developing the method to include more grass species.     

Estimating crop and waveband specific water cloud model parameters using a theoretical backscatter model

Synthetic aperture radar (SAR) data are useful for monitoring various biophysical properties, and backscatter models are required to extract such information. The semi-empirical water cloud model is traditionally formulated using four parameters fitted using in situ data. The model becomes more accurate through the use of crop and soil specific values of these parameters, estimated using a robust theoretical second-order backscatter model. Methods are introduced in this letter for generating two of the four parameters specific to C-band data to enable greater transportability of the model.     

Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data

Biomass and leaf area index (LAI) are important variables in many ecological and environmental applications. In this study, the suitability of visible to shortwave infrared advanced spaceborne thermal emission and reflection radiometer (ASTER) data for estimating aboveground tree and LAI in the treeline mountain birch forests was tested in northernmost Finland. The biomass and LAI of the 128 plots were surveyed, and the empirical relationships between forest variables and ASTER data were studied using correlation analysis and linear and non‐linear regression analysis. The studied spectral features also included several spectral vegetation indices (SVI) and canonical correlation analysis (CCA) transformed reflectances. The results indicate significant relationships between the biomass, LAI and ASTER data. The variables were predicted most accurately by CCA transformed reflectances, the approach corresponding to the multiple regression analysis. The lowest RMSEs were 3.45 t ha^?1 (41.0%) and 0.28 m²m^?2 (37.0%) for biomass and LAI respectively. The red band was the band with the strongest correlation against the biomass and LAI. SR and NDVI were the SVIs with the strongest linear and non‐linear relationships. Although the best models explained about 85% of the variation in biomass and LAI, the undergrowth vegetation and background reflectance are likely to affect the observed relationships.     

Discrete-time average positivity and spectral factorization in a behavioral framework

Quite recently, Willems and Trentelman (SIAM J. Control Opt. 36 (1998) 1703–1749) have proposed quadratic differential forms for system analysis and synthesis from the behavioral point of view. In this paper, we develop their discrete-time version, called quadratic difference forms, and some related fundamental concepts with respect to dissipativeness for a dynamical system which interacts with its environment along square summable trajectories. Moreover, we provide an algorithm for spectral factorization of polynomial matrices as one example of quadratic difference forms. A numerical example will be given to show the validity of our result.     

Estimation of LAI and FAPAR by constraining the leaf and soil spectral characteristics in a radiative transfer model

We evaluated the estimation of the leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) of green plant canopies from top-of-the-canopy (TOC) spectral indices by using the PROSAIL model under the possible constraints of leaf and soil spectra. For the LAI estimation, the ratios (B1 – B3)/(B1?+?B3) and B1/B3 provided fewer estimation errors than (B4 – B3)/(B4?+?B3) or B4/B3 when the variation in the soil spectral reflectance was small or LAI was large. Here, B1, B3 and B4 denote the blue, red and near-infrared bands of Landsat ETM+, respectively. For the FAPAR estimation, the ratios (B5 – B7)/(B5?+?B7) and B5/B7 provided fewer estimation errors than (NIR – R)/(NIR?+?R) or NIR/R for a FAPAR value of 0.3–0.7 when the variation in the soil spectral reflectance was large. Here, B5 and B7 denote the bands with wavelengths 1.55–1.75 and 2.09–2.35 μm, respectively. These were maintained for various conditions of the solar incident zenith angle (θ_s), leaf angle distribution (LAD), canopy hotspot parameter (s ₁) and clumping index (Ω).     

Retrieval of the canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis

This paper reports on the use of linear spectral mixture analysis for the retrieval of canopy leaf area index (LAI) in three flux tower sites in the Boreal Ecosystem-Atmosphere Study (BOREAS) southern study area: Old Black Spruce, Old Jack Pine, and Young Jack Pine (SOBS, SOJP, and SYJP). The data used were obtained by the Compact Airborne Spectrographic Imager (CASI) with a spatial resolution of 2 m in the winter of 1994. The convex geometry method was used to select the endmembers: sunlit crown, sunlit snow, and shadow. Along transects for these flux tower sites, the fraction of sunlit snow was found to have a higher correlation with the field-measured canopy LAI than the fraction of sunlit crown or the fraction of shadow. An empirical equation was obtained to describe the relation between canopy LAI and the fraction of sunlit snow. There is a strong correlation between the estimated LAI and the field-measured LAI along transects (with R² values of 0.54, 0.71, and 0.60 obtained for the SOBS, SYJP, and SOJP sites, respectively). The estimated LAI for the whole tower site is consistent with that obtained by the inversion of a canopy model in our previous study where values of 0.94, 0.92, and 0.63 were obtained for R² for the SOBS, SYJP and SOJP sites, respectively.The CASI 2-m summer data over the SOBS site was also employed to investigate the possibility of deriving canopy LAI from the summer data using linear mixture analysis. At a spatial resolution of 10 m, the correlation between the field-measured LAI and the estimated LAI along transects is small at R² less than 0.3, while R² increases to 0.6 at a spatial resolution of 30 m. The difficulty in canopy LAI retrieval from the summer data at a spatial resolution of 10 m is likely due to the variation of the understory reflectance across the scene, although spatial misregistration of the CASI data used may also be a possible contributing factor.