Canopy Reflectance Spectra Research Articles

Global Sensitivity Analysis for Canopy Reflectance and Vegetation Indices of Mangroves

Remote sensing has been applied to map the extent and biophysical properties of mangroves. However, the impact of several critical factors, such as the fractional cover and leaf-to-total area ratio of mangroves, on their canopy reflectance have rarely been reported. In this study, a systematic global sensitivity analysis was performed for mangroves based on a one-dimensional canopy reflectance model. Different scenarios such as sparse or dense canopies were set up to evaluate the impact of various biophysical and environmental factors, together with their ranges and probability distributions, on simulated canopy reflectance spectra and selected Sentinel-2A vegetation indices of mangroves. A variance-based method and a density-based method were adopted to compare the computed sensitivity indices. Our results showed that the fractional cover and leaf-to-total area ratio of mangrove crowns were among the most influential factors for all examined scenarios. As for other factors, plant area index and water depth were influential for sparse canopies while leaf biochemical properties and inclination angles were more influential for dense canopies. Therefore, these influential factors may need attention when mapping the biophysical properties of mangroves such as leaf area index. Moreover, a tailored sensitivity analysis is recommended for a specific mapping application as the computed sensitivity indices may be different if a specific input configuration and sensitivity analysis method are adopted.

Hyperspectral Identification of Chlorophyll Fluorescence Parameters of Suaeda salsa in Coastal Wetlands

The stomata of Suaeda salsa are closed and the photosynthetic efficiency is decreased under conditions of water–salt imbalance, with the change to photosynthesis closely related to the chlorophyll fluorescence parameters of the photosystem PSII. Accordingly, chlorophyll fluorescence parameters were selected to monitor the growth status of Suaeda salsa in coastal wetlands under conditions of water and salt. Taking Suaeda salsa in coastal wetlands as the research object, we set up five groundwater levels (0 cm, −5 cm, −10 cm, −20 cm, and −30 cm) and six NaCl salt concentrations (0%, 0.5%, 1%, 1.5%, 2%, and 2.5%) to carry out independent tests of Suaeda salsa potted plants and measured the canopy reflectance spectrum and chlorophyll fluorescence parameters of Suaeda salsa. A polynomial regression method was used to carry out hyperspectral identification of Suaeda salsa chlorophyll fluorescence parameters under water and salt stress. The results indicated that the chlorophyll fluorescence parameters Fv/Fm, Fm′, and ΦPSII of Suaeda salsa showed significant relationships with vegetation index under water and salt conditions. The sensitive canopy band ranges of Suaeda salsa under water and salt conditions were 680–750 nm, 480–560 nm, 950–1000 nm, 1800–1850 nm, and 1890–1910 nm. Based on the spectrum and the first-order differential spectrum, the spectral ratio of A/B was constructed to analyze the correlation between it and the chlorophyll fluorescence parameters of Suaeda salsa. We constructed thirteen new vegetation indices. In addition, we discovered that the hyperspectral vegetation index D690/D1320 retrieved Suaeda chlorophyll fluorescence parameter Fv/Fm with the highest accuracy, with a multiple determination coefficient R2 of 0.813 and an RMSE of 0.042, and that D725/D1284 retrieved Suaeda chlorophyll fluorescence parameter ΦPSII model with the highest accuracy, with a multiple determination coefficient R2 of 0.848 and an RMSE of 0.096. The hyperspectral vegetation index can be used to retrieve the chlorophyll fluorescence parameters of Suaeda salsa in coastal wetlands under water and salt conditions, providing theoretical and technical support for future large-scale remote sensing inversion of chlorophyll fluorescence parameters.

Plant ecophysiological processes in spectral profiles: perspective from a deciduous broadleaf forest

The need for progress in satellite remote sensing of terrestrial ecosystems is intensifying under climate change. Further progress in Earth observations of photosynthetic activity and primary production from local to global scales is fundamental to the analysis of the current status and changes in the photosynthetic productivity of terrestrial ecosystems. In this paper, we review plant ecophysiological processes affecting optical properties of the forest canopy which can be measured with optical remote sensing by Earth-observation satellites. Spectral reflectance measured by optical remote sensing is utilized to estimate the temporal and spatial variations in the canopy structure and primary productivity. Optical information reflects the physical characteristics of the targeted vegetation; to use this information efficiently, mechanistic understanding of the basic consequences of plant ecophysiological and optical properties is essential over broad scales, from single leaf to canopy and landscape. In theory, canopy spectral reflectance is regulated by leaf optical properties (reflectance and transmittance spectra) and canopy structure (geometrical distributions of leaf area and angle). In a deciduous broadleaf forest, our measurements and modeling analysis of leaf-level characteristics showed that seasonal changes in chlorophyll content and mesophyll structure of deciduous tree species lead to a seasonal change in leaf optical properties. The canopy reflectance spectrum of the deciduous forest also changes with season. In particular, canopy reflectance in the green region showed a unique pattern in the early growing season: green reflectance increased rapidly after leaf emergence and decreased rapidly after canopy closure. Our model simulation showed that the seasonal change in the leaf optical properties and leaf area index caused this pattern. Based on this understanding we discuss how we can gain ecophysiological information from satellite images at the landscape level. Finally, we discuss the challenges and opportunities of ecophysiological remote sensing by satellites.

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy

Estimating nitrogen (N) concentration in situ is fundamental for managing the fertilization of the sugarcane crop. The purpose of this work was to develop estimation models that explain how N varies over time as a function of three spectral data transformations in two stages (plant cane and first ratoon) under variable rates of N application. A randomized complete-block experimental design was applied, with four levels of N fertilization: 0, 80, 160, and 240 kg N ha−1. Six sampling events were carried out during the rapid growth stage, where the canopy reflectance spectra with a hyperspectral sensor were measured, and tissue samples for N determination in plant cane and first ratoon were taken, from 60 days after emergence (DAE) and 60 days after harvest (DAH), respectively, until days 210 DAE and 210 DAH. To build the models, partial least squares regression analysis was used and was trained by three transformations of the spectral data: (i) average reflectance spectrum (R), (ii) multiple scatter correction and Savitzky-Golay filter MSC-SG) reflectance spectrum, and (iii) calculated vegetation indices (VIs).

Exploring the potential of canopy reflectance spectra for estimating organic carbon content of aboveground vegetation in coastal wetlands

ABSTRACT Accurate estimation of organic carbon content or carbon storage of coastal wetland vegetation is essential for understanding the carbon cycle of coastal wetland ecosystems. This study aimed to explore the potential of canopy spectra of coastal wetland vegetation in estimating the organic carbon content of aboveground vegetation (OCCAV). A total of 54 representative vegetation quadrats were selected from coastal wetlands in Jiangsu Province, China, and their canopy reflectance spectra were measured using an in situ field spectrometer. In particular, two advanced spectral algorithms, fractional-order derivative (FOD) and optimal band combination algorithm, were used for spectral pre-treatments. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to establish the OCCAV estimation models. Accuracies of the models constructed by the processed spectral parameters were compared with that of the models corresponding to five traditional pre-treatment methods and 15 common vegetation indices. Results showed that the FOD method captured subtler spectral characteristics than the first and second derivatives. The optimal estimation accuracies of PLSR and SVM models were obtained based on 0.75 and 1.50 order derivative spectra, respectively, and the ratios of performance to interquartile range (RPIQ) of the models were 2.57 and 2.97, respectively. Moreover, the optimal band combination algorithm effectively extracted the sensitive spectral parameters related to OCCAV, and the accuracy of the model established based on the spectral parameters extracted by this algorithm was generally better than that of the model obtained by the common vegetation indices. The optimal estimation result was achieved by the SVM model based on the optimized ratio vegetation index, with an RPIQ of 3.10. In summary, this research provides a theoretical basis for future studies on estimating the organic carbon and carbon storage of wetland vegetation based on large-scale canopy hyperspectral images and also helps to improve the knowledge of the carbon cycle of wetland ecosystems.

Estimating and mapping of soil organic matter content in a typical river basin of the Qinghai-Tibet Plateau

Spectroscopy is a fast, non-destructive, and cheap method, which has been widely used in the estimation of soil organic matter (SOM) concentration. This study presented a methodology to estimate and map SOM content by crop canopy reflectance spectra combining with land parameters in the Yarlung Zangbo River (YZR) basin. The reflectance spectra of the oat canopy were collected in the field, and then were processed by savitzky-golay filtering (S-G), continuous removal (CR), and first derivative of reflectivity (FDR). The principal components were extracted from the processed spectral data. Land parameters such as elevation, slope, NDVI, and land surface temperature (LST) were selected to establish 18 models to estimate SOM content. The results showed that the estimation accuracy of SOM content could be improved by combining crop canopy reflectance spectra with land parameters. FDR-BPNN model with land parameters had the best estimation effect (R2 = 0.973, MAE = 0.847 g·kg−1).

Detection of Rise Damage by Leaf Folder (Cnaphalocrocis medinalis) Using Unmanned Aerial Vehicle Based Hyperspectral Data

Crop pests and diseases are key factors that damage crop production and threaten food security. Remote sensing techniques may provide an objective and effective alternative for automatic detection of crop pests and diseases. However, ground-based spectroscopic or imaging sensors may be limited in practically guiding the precision application and reduction of pesticide. Therefore, this study developed an unmanned aerial vehicle (UAV)-based remote sensing system to detect leaf folder (Cnaphalocrocis medinalis). Rice canopy reflectance spectra were obtained in the booting growth stage by using the UAV-based hyperspectral remote sensor. Newly developed and published multivariate spectral indices were initially calculated to estimate leaf-roll rates. The newly developed two-band spectral index (R490−R470), three-band spectral index (R400−R470)/(R400−R490), and published spectral index photochemical reflectance index (R550−R531)/(R550+R531) showed good applicability for estimating leaf-roll rates. The newly developed UAV-based micro hyperspectral system had potential in detecting rice stress induced by leaf folder. The newly developed spectral index (R490−R470) and (R400−R470)/(R400−R490) might be recommended as an indicator for estimating leaf-roll rates in the study area, and (R550−R531)/(R550+R531) might serve as a universal spectral index for monitoring leaf folder.

Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time

Leaf chlorophyll content (LCC), as an important indicator of photosynthetic capacity and nitrogen status, has been non-destructively estimated from canopy reflectance spectra in recent studies. They have also found significant canopy structure and solar angle effects on canopy reflectance spectra, especially for row-structured open crop canopies with varying sunlit and shaded soil backgrounds over seasonal and diurnal cycles. Since the canopy reflectance signature of crops is negligibly contaminated by shaded soil, it can be inferred that LCC should be better estimated when the soil background is under shaded conditions than under sunlit conditions. However, the effects of solar angle or spectral observation time and canopy structure on LCC estimation for row crops have been poorly understood. This study aimed to determine the optimal observation time and to reduce the canopy structural effect for LCC estimation over row-structured crops. To achieve these goals, we simulated canopy reflectance spectra covering four typical crop orientations over seasonal and diurnal cycles based on the leaf optical models (PROSPECT-5B and PROSPECT-D) coupled with canopy radiative transfer models (4SAIL and 4SAIL-RowCrop). Moreover, this study proposed the leaf area index (LAI)-insensitive chlorophyll index (LICI) to mitigate the canopy structural effect on the LCC ~ LICI relationship. LICI and 11 traditional vegetation indices (VIs) were investigated to calibrate semi-empirical LCC ~ VI models with canopy spectral simulations. The performance of those models in LCC estimation was evaluated with experimental datasets measured from both ground and unmanned aerial vehicle (UAV) platforms.Our results showed linear relationships between LICI and LCC for both synthetic and experimental datasets and negligible sensitivity of those models to LAI. Compared to traditional spectral measurements around 12:00 h, LCC estimation could be improved at observation time (15:00 h) that minimized observed sunlit soil fraction for the north-south (NS) row crop orientation. The semi-empirical LCC ~ LICI model (LCC = 52.85 × LICI - 16.71) calibrated with the combination of PROSPECT-D and 4SAIL-RowCrop showed the best accuracies among all VIs, with root mean square error (RMSE) values of 5.01 μg/cm2 and 3.32 μg/cm2 for ground-based and UAV-based canopy spectra measured at 15:00 h, respectively. For the commonly used NS row orientation, this study recommends off-noon spectral measurements to avoid the exposure of sunlit soil to the sensor. These findings have great potential for improving the understanding of the canopy structure and solar angle effects on LCC estimation and facilitating the spectral sampling protocols for crop chlorophyll status monitoring.

Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance

Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration.

Hyperspectral vegetation indexes to monitor wheat plant height under different sowing conditions

Accurate estimation of plant height is essential for precision crop production. Remote sensing provides a beneficial tool for monitoring plant height, but few attempts had been made to monitor wheat plant height using ground-based hyperspectral vegetation indexes. This study systematically analyzed the quantitative relationship between various hyperspectral vegetation indexes and wheat plant height based on a field experiment under different sowing conditions (i.e., normal sowing and late sowing). The optimal vegetation indexes that were highly sensitive to wheat plant height were derived by comparing the newly constructed hyperspectral vegetation indexes with published vegetation indexes. The experiment was conducted at the Agrometeorological Station, Nanjing University of Information Science and Technology, Jiangsu Province, China. Wheat canopy reflectance spectra were synchronously determined with plant height in tillering-anthanis stage. Results showed that the newly developed vegetation index (R785 – R810)/(R785 + R810 – 2×R802) in normal sowing treatment and R1029 – R1004 in late sowing treatment, generated the optimal testing performance for estimating wheat plant height with the coefficient of determination (R2) of 0.78 and 0.89, root mean square error (RMSE) of 7.37 and 5.92 cm, and residual prediction deviation (RPD) of 2.04 and 3.09, respectively. This study suggests that the ground-based hyperspectral remote sensing technique can provide spectral indexes for accurately monitoring wheat plant height.

A novel restoration approach for vegetation reflectance spectra at noisy bands using the principal component analysis method

ABSTRACTDue to the signal-to-noise ratio (SNR) of sensors, as well as atmospheric absorption and illumination conditions, etc., hyperspectral data at some bands are of poor quality. Data restoration for noisy bands is important for many remote sensing applications. In this paper, we present a novel data-driven Principal Component Analysis (PCA) approach for restoring leaf reflectance spectra at noisy bands using the spectra at effective bands. The technique decomposes the leaf reflectance spectra into their principal components (PCs), selects the leading PCs that describe the most variance in the data, and restores the data from these components. First, the first 10 PCs were determined from a training dataset simulated by the leaf optical properties model (PROSPECT-5) that contained 99.998% of the total information in the 3636 training samples. Then, the performance of the PCA method for restoration of the reflectance at noisy bands was investigated using the ANGERS leaf optical properties dataset; the results showed the spectral root mean squared error (RMSE) is in the range 6.46 × 10−4 to 6.44 × 10−2, which is about 3 − 34 times more accurate than the stepwise regression method and partial least squares method (PLSR) for the ANGERS dataset. The results also showed that if the noisy bands are far away from the effective bands, the accuracy of the restored leaf reflectance spectra will decrease. Thirdly, the reliability of the restored reflectance spectra for retrieving leaf biochemical contents was assessed using the ANGERS dataset and leaf optical properties dataset established by the Beijing Academy of Agriculture and Forestry Sciences (BAAFS). Three water-sensitive vegetation indices − normalized difference water index (NDWI), normalized difference infrared index (NDII) and Datt water index (DWI), derived from the restored leaf spectra − were employed to retrieve the equivalent water thickness (EWT). The results showed that the leaf water content can be accurately retrieved from the restored leaf reflectance spectra. In addition, the PCA method to restore vegetation spectral reflectance can be applied on canopy level as well. The results showed that the spectral root mean squared error (RMSE) is in the range 8.22 × 10−4 to 1.87 × 10−2. The performance of the restored canopy spectra was investigated according to the results of retrieving canopy equivalent water thickness (CEWT) using the five spectral indices NDWI, NDWI1370, NDWI1890, NDII and DWI. The results indicated that the restored canopy spectra can be used for retrieving CEWT reliably and improve the accuracy of retrieval compared to the results using original canopy reflectance spectra.

Hyperspectral remote sensing to quantify the flowering phenology of winter wheat

Flowering phenology, as a sensitive bio-indicator to climate change, has been attracting increasing concern. However, less information is available so far for quantifying flowering phenological stages by use of hyperspectral remote sensing in winter wheat. The establishment of a vegetation index, which is sensitive to the changes of flowering rate, is important to allow the monitoring of flowering phenology through hyperspectral remote sensing. A field experiment was conducted at the Agro-meteorological Station, Nanjing University of Information Science and Technology, Jiangsu Province, China, with two nighttime warming levels, i.e., nighttime warming and ambient temperature (control), and two sowing levels, i.e., normal sowing and late sowing. The field canopy reflectance spectrum was synchronously determined with the flowering rate within 0.25 m2 area at the flowering stage of winter wheat. This study developed a new index for estimating the flowering rate that was compared with the published indices for estimating canopy structure parameter. The newly developed two bands-index (R446 − R472)/(R446 + R472), generated coefficients of determination, root mean square error, and residual prediction deviation values between the measured and predicted values of 0.75, 18.69%, and 1.66, respectively. The study suggests that hyperspectral remote sensing can be used to quantify flowering phenology in winter wheat.

Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

Water deficit in crops induces a stress that may ultimately result in low production. Identification of response of genotypes towards water deficit stress is very crucial for plant phenotyping. The study was carried out with the objective to identify the response of different rice genotypes to water deficit stress. Ten rice genotypes were grown each under water deficit stress and well watered or nonstress conditions. Thermal images coupled with visible images were recorded to quantify the stress and response of genotypes towards stress, and relative water content (RWC) synchronized with image acquisition was also measured in the lab for rice leaves. Synced with thermal imaging, Canopy reflectance spectra from same genotype fields were also recorded. For quantification of water deficit stress, Crop Water Stress Index (CWSI) was computed and its mode values were extracted from processed thermal imageries. It was ascertained from observations that APO and Pusa Sugandha-5 genotypes exhibited the highest resistance to the water deficit stress or drought whereas CR-143, MTU-1010, and Pusa Basmati-1 genotypes ascertained the highest sensitiveness to the drought. The study reveals that there is an effectual relationship (R 2 = 0.63) between RWC and CWSI. The relationship between canopy reflectance spectra and CWSI was also established through partial least square regression technique. A very efficient relationship (calibration R 2 = 0.94 and cross-validation R 2 = 0.71) was ascertained and 10 most optimal wavebands related to water deficit stress were evoked from hyperspectral data resampled at 5 nm wavelength gap. The identified ten most optimum wavebands can contribute in the quick detection of water deficit stress in crops. This study positively contributes towards the identification of drought tolerant and drought resistant genotypes of rice and may provide valuable input for the development of drought-tolerant rice genotypes in future.

Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis

Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages.

Detection of peanut leaf spots disease using canopy hyperspectral reflectance

Leaf spot is one of the most destructive diseases, which has a significant impact on the peanut production. Detecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease. In this study, we identified sensitive bands and derived hyperspectral vegetation index specific to leaf spot detection. Hyperspectral canopy reflectance spectra of peanut cultivars susceptibilities to leaf spot were measured at two experimental sites in 2017. The normalized difference spectral index (NDSI) was derived based on their correlation with disease index (DI) in the leaf spectrum between 325 nm and 1075 nm. The results showed that canopy spectral reflectance decreased significantly in the near-infrared regions (NIR) as DI increased (r < −0.90). The spectral index for detecting leaf spot in peanut were LSI: (NDSI (R938, R761)) with R2 values of up to 0.68 for the regression model. The high fit between the observed and estimated values indicates that the DI detecting model based on the index could be used in peanut leaf spot detection in the absence of other stresses causing unhealthy symptoms. The results of this study show that it will provide a reliable, effective and accurate method for detecting leaf spot diseases in peanut through the analysis of hyperspectral data in the future.

Estimation of heavy-metal concentrations in winter wheat leaves from typical sewage irrigation area based on canopy reflectance spectra

Heavy-metal pollution of crops seriously affects food safety and human health in China. The purpose of this study is to estimate the concentrations of heavy metals economically and accurately using spectroscopy. The spectral data of winter wheat canopy at 61 sampling points were collected in Longkou City, and the concentration of Pb, Zn, Cd, Cr, Ni, and Hg was measured. Eight estimation models were established based on the back-propagation neural network (BPNN), the partial least squares regression, and the stepwise multiple linear regression methods combined with reflectance spectra, first-order derivative of spectral reflectance (FDR), second-order derivative of spectral reflectance, and spectral parameters (SPs). Spatial interpolation was used to map the heavy-metal concentration. The results showed that the best model for Pb, Zn, and Cd was the BPNN model with SPs, for Cr, Ni, and Hg was the BPNN model with FDR. The concentration of Hg was lower in the northwest, Pb and Zn were higher in the middle and south, Cd was lower in the middle and west, and Cr and Ni were higher in the southeast. This study will provide effective technical support for the economical and accurate monitoring of heavy metals.

Development of an Apparatus for Crop-Growth Monitoring and Diagnosis.

To non-destructively acquire leaf nitrogen content (LNC), leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW) data at high speed and low cost, a portable apparatus for crop-growth monitoring and diagnosis (CGMD) was developed according to the spectral monitoring mechanisms of crop growth. According to the canopy characteristics of crops and actual requirements of field operation environments, splitting light beams by using an optical filter and proper structural parameters were determined for the sensors. Meanwhile, an integral-type weak optoelectronic signal processing circuit was designed, which changed the gain of the system and guaranteed the high resolution of the apparatus by automatically adjusting the integration period based on the irradiance received from ambient light. In addition, a coupling processor system for a sensor information and growth model based on the microcontroller chip was developed. Field experiments showed that normalised vegetation index (NDVI) measured separately through the CGMD apparatus and the ASD spectrometer showed a good linear correlation. For measurements of canopy reflectance spectra of rice and wheat, their linear determination coefficients (R2) were 0.95 and 0.92, respectively while the root mean square errors (RMSEs) were 0.02 and 0.03, respectively. NDVI value measured by using the CGMD apparatus and growth indices of rice and wheat exhibited a linear relationship. For the monitoring models for LNC, LNA, LAI, and LDW of rice based on linear fitting of NDVI, R2 were 0.64, 0.67, 0.63 and 0.70, and RMSEs were 0.31, 2.29, 1.15 and 0.05, respectively. In addition, R2 of the models for monitoring LNC, LNA, LAI, and LDW of wheat on the basis of linear fitting of NDVI were 0.82, 0.71, 0.72 and 0.70, and RMSEs were 0.26, 2.30, 1.43, and 0.05, respectively.

Using wavelet analysis of hyperspectral remote-sensing data to estimate canopy chlorophyll content of winter wheat under stripe rust stress

ABSTRACTChlorophyll content can be used as an indicator to monitor crop diseases. In this article, an experiment on winter wheat stressed by stripe rust was carried out. The canopy reflectance spectra were collected when visible symptoms of stripe rust in wheat leaves were seen, and canopy chlorophyll content was measured simultaneously in laboratory. Continuous wavelet transform (CWT) was applied to process the smoothed spectral and derivative spectral data of winter wheat, and the wavelet coefficient features obtained by CWT were regarded as the independent variable to establish estimation models of chlorophyll content. The hyperspectral vegetation indices were also regarded as the independent variable to build estimation models. Then, two types of models above-mentioned were compared to ascertain which type of model is better. The cross-validation method was used to determine the model accuracies. The results indicated that the estimation model of chlorophyll content, which is a multivariate linear model constructed using wavelet coefficient features extracted by Mexican Hat wavelet function processing the smoothed spectrum (WSMH1 and WSMH2), is the best model. It has the highest estimation accuracy with modelled coefficient of determination (R2) of 0.905, validated R2 of 0.913, and root mean square error (RMSE) of 0.288 mg fg−1. The univariate linear model built by wavelet coefficient feature of WSMH1 is secondary and the modelled R2 is 0.797, validated R2 is 0.795, and RMSE is 0.397 mg fg−1. Both estimation models are better than those of all hyperspectral vegetation indices. The research shows that the feature information of canopy chlorophyll content of winter wheat can be captured by wavelet coefficient features which are extracted by the method of CWT processing canopy reflectance spectrum data. Therefore, it could provide theoretical support on detecting diseases of crop by remote sensing quantitatively estimating chlorophyll content.

Investigation into the role of canopy structure traits and plant functional types in modulating the correlation between canopy nitrogen and reflectance in a temperate forest in northeast China

A precise estimate of canopy leaf nitrogen concentration (CNC, based on dry mass) is important for researching the carbon assimilation capability of forest ecosystems. Hyperspectral remote sensing technology has been applied to estimate regional CNC, which can adjust forest photosynthetic capacity and carbon uptake. However, the relationship between forest CNC and canopy spectral reflectance as well as its mechanism is still poorly understood. Using measured CNC, canopy structure and species composition data, four vegetation indices (VIs), and near-infrared reflectance (NIR) derived from EO-1 Hyperion imagery, we investigated the role of canopy structure traits and plant functional types (PFTs) in modulating the correlation between CNC and canopy reflectance in a temperate forest in northeast China. A plot-scale forest structure indicator, named broad foliar dominance index (BFDI), was introduced to provide forest canopy structure and coniferous and broadleaf species composition. Then, we revealed the response of forest canopy reflectance spectrum to BFDI and CNC. Our results showed that leaf area index had no significant effect on NIR (P>0.05) but indicated that there was a significant correlation (R2=0.76, P 0.05). On the contrary, removing the CNC effect, the partial correlation between BFDI and NIR was positively significant (R=0.69, P<0.0001). These findings proved that canopy structure and coniferous and broadleaf species composition had a greater influence on the remote sensing signal than canopy nitrogen concentration. The functional convergence of plant traits resulted in the relation of CNC and canopy structure and determined the positive correlation between CNC and NIR. We maintain that the repeatable relationship between CNC and NIR can be used in the remote sensing retrieval of CNC during various forest types. Nevertheless, the relationship cannot be considered as a feasible approach of CNC estimation for a single PFT.

Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration

The leaf or canopy reflectance spectra of vegetation have been widely employed in estimating foliar nitrogen (N) concentration; however, they alone may not actually reflect the spectral and detailed information at a sampling plot. In this study, the potential spectral details of Carex (C. cinerascens) at a plot scale were derived using discrete wavelet transform, in which a simple operation of addition was employed to combine the reconstructed leaf and canopy reflectance at the fourth decomposition level (named “leaf-canopy d4 reflectance”). Partial least squares regression (PLSR), successive projections algorithm-based multiple linear regression (SPA-MLR) and random forest regression (RFR) models with leaf, canopy and leaf-canopy d4 reflectance were established and validated for foliar N estimation, respectively. The results showed that the PLSR (R2CV=0.718, determination coefficient of cross-validation; R2Val=0.743, determination coefficient of independent validation; RPD=1.91, residual prediction deviation), SPA-MLR (R2CV=0.709, R2Val=0.747, RPD=1.97) and RFR (R2CV=0.714, R2Val=0.783, RPD=2.16) models with leaf-canopy d4 reflectance outperformed their corresponding models with leaf or canopy reflectance. We conclude that the wavelet-based coupling of leaf and canopy reflectance spectra has great potential in the accurate estimation of foliar N concentration. This proposed strategy helps to understand the spectral details of vegetation at a plot scale, providing the potential for improving the plot-based estimation of plant nutrients in grassland, precision agriculture or forestry.