Leaf Biochemical Contents Research Articles

Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection.

Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants' biochemical traits have become feasible due to advances in multispectral imaging sensors in recent years. This study evaluated the field application of proximal multispectral imaging combined with feature selection and regressive analysis to estimate the biochemical pigment contents of poplar leaves. The combination of 6 spectral bands and 26 vegetation indices (VIs) derived from the multispectral bands was taken as the group of initial variables for regression modeling. Three variable selection algorithms, including the forward selection algorithm with correlation analysis (CORR), recursive feature elimination algorithm (RFE), and sequential forward selection algorithm (SFS), were explored as candidate methods for screening combinations of input variables from the 32 spectral-derived initial variables. Partial least square regression (PLSR) and nonlinear support vector machine regression (SVR) were both applied to estimate total chlorophyll content (Chla+b) and carotenoid content (Car) at the leaf scale. The results show that the nonlinear SVR prediction model based on optimal variable combinations, selected by SFS using multiple scatter correction (MSC) preprocessing data, achieved the best estimation accuracy and stable prediction performance for the leaf pigment content. The Chla+b and Car models developed using the optimal model had R2 and RMSE predictive statistics of 0.849 and 0.825 and 5.116 and 0.869, respectively. This study demonstrates the advantages of using a nonlinear SVR model combined with SFS variable selection to obtain a more reliable estimation model for leaf biochemical pigment content.

GSV-L: A general spectral vector model for hyperspectral leaf spectra simulation

Leaf optical spectra determine vegetation canopy optical spectra. Physically based leaf models are relatively complicated as they require many biophysical and biochemical parameters that are usually difficult to obtain. The aim of this study is to develop a new empirical model, the general spectral vector-leaf (GSV-L) model using globally collected leaf spectra data. A global field-measured leaf spectra database, including 66,478 leaf reflectance and 3575 transmittance spectra in the wavelength range of 400–2400 nm, was first constructed. The singular value decomposition (SVD) method was applied to the field data matrix to derive the spectral vectors for leaf spectral modeling. The SVD method was also compared with the successive approximation (SA) and principal component analysis (PCA) methods using the same training and validation datasets, and the GSV-L model was compared with two other physical models, PROSPECT and LEAF-SIP. The GSV-L model can be used to simulate full leaf reflectance and transmittance spectra using a few wavelengths in different bands with very high R2 (0.99, 0.99), small RMSE (0.014, 0.014) and bias (0, 0.002) values. The model also performs better than the PROSPECT and LEAF-SIP models for leaf spectral simulation. The coefficients of the reflectance spectral vectors can be used to estimate leaf structure parameters, equivalent water thickness, and leaf biochemical contents. As an efficient and accurate approach for leaf hyperspectral modeling, GSV-L can also be integrated with various canopy reflectance and land surface models.

Angular effect in proximal sensing of leaf-level chlorophyll content using low-cost DIY visible/near-infrared camera

Proximal sensing is increasingly highlighted, owing to its potential for various applications in precision agriculture. However, the uses of its related special devices like visible/near-infrared (VNIR) cameras for crop mapping are yet limited in practice, due to their high costs in market. To handle this issue, this case study was dedicated to modifying a low-cost RGB camera as a VNIR one in a do-it-yourself (DIY) way – replacing its infrared-blocking optical filter with a red-blocking one – for estimating leaf-level chlorophyll content. To enhance its performance, the angular effect that proved to impact the quality of imaging in conventional remote sensing was examined here. Specifically, in the cases of 120 individual leaves for four plant species, 24 vegetation indices (VIs) were proposed and derived from the three bands (blue, green, and near-infrared) of the collected images; then, the same operation was repeated for a set of leaf inclinations (θ, the angle between the optical axis of the camera and the normal vector of the leaf-fixed plate, set from 0°, 5°, …, to 50°). The results showed that for each of the four plant species, different optimal VIs for leaf chlorophyll content retrieval were detected for different geometries of leaf reflectance. This suggested that more modular retrieval models capable of integrally reflecting the angular effect best should be developed for accurately sensing of leaf biochemical content. Overall, the contribution of this study is of fundamental implications for advancing quantitative proximal sensing in plant biochemistry and precision agriculture.

Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

Effect of different regression algorithms on the estimating leaf parameters based on selected characteristic wavelengths by using the PROSPECT model.

In this study, the characteristic wavelengths of leaf biochemical parameters (including carotenoid content, chlorophyll ${a} + { b}$a+b content, dry matter content, equivalent water thickness, and leaf structure parameter) were obtained through a sensitivity analysis based on a physical model. Then, performance of the selected characteristic wavelengths for monitoring leaf biochemical contents (LBC) was analyzed by using the following six popular regression algorithms: random forest, backpropagation neural network, support vector regression, radial basic function neural network, partial least-squares regression, and Gaussian process regression of different parameter values/kernel functions/training functions. In addition, the optimal parameters of each regression algorithm for estimating LBC were determined. Lastly, the effect of different regression algorithms on the accuracy of LBC estimation using four different data sets was also discussed. The results demonstrated that the selected 10 characteristic wavelengths combined with the Gaussian process regression model can be efficiently applied in estimating LBC.

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.

Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance

Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters.

Informative bands used by efficient hyperspectral indices to predict leaf biochemical contents are determined by their relative absorptions

Hyperspectral indices have proven to be useful for quantifying various plant parameters including a number of biochemical components. However, it remains a great challenge to unveil the physical and physiological mechanisms of wavelengths used by these statistically/empirically identified indices, and this unveiling is a critical step towards developing generally applicable indices. In this study, we have introduced a dummy variable to the well-accepted leaf scale radiative transfer model (PROSPECT-4) and have conducted a series of virtual experiments by tweaking different absorption features (intensity, peak wavelength, and half-width) of the dummy variable for investigating the underlying mechanisms of those wavelengths used by efficient indices. Results clearly indicated that the informative bands used in efficient indices, instead of the target parameter’s absorption peaks, are generally concentrated among wavelengths that the targeted parameter has relatively high specific absorption coefficients compared with other biochemical components. This finding has also been validated by replacing the dummy variable with carotenoids based on PROSPECT-5B simulated dataset. Further analysis reveals that although the concentrations of chlorophyll are higher than other pigments in most leaves, the commonly recommended wavelength of 550 nm may be disturbed by other pigments and may only be applicable when anthocyanins are minor. Results obtained in this study have largely explained why specific wavelengths are used in efficient indices, and thus this should lay a basis for understanding the underlying mechanisms and help to develop robust indices for estimating vegetation biochemical parameters.

A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents

Vegetation variable retrieval from reflectance data is typically grouped into three categories: the statistical–empirical category, the physical category and the hybrid category (physical models applied to statistical models). Based on the similarities between the spectra of leaves in the optical domain, the leaf reflectance spectra can be linearly modelled using a very limited number of principal components (PCs) if the PCA (principal component analysis) transformation is carried out at the sample dimension. In this paper, we present a novel data-driven approach that uses the PCA transformation to reconstruct leaf reflectance spectra and also to retrieve leaf biochemical contents. First, the PCA transformation was carried out on a training dataset simulated by the PROSPECT-5 model. The results showed that the leaf reflectance spectra can be accurately reconstructed using only a few leading PCs, as the ten leading PCs contained 99.999% of the total information in the 3636 training samples. The spectral error between the simulated or measured reflectance and the reconstructed spectra was also investigated using the simulated and measured datasets (ANGERS and LOPEX’93). The mean root mean squared error (RMSE) values varied from 5.56 × 10−5 to 6.18 × 10−3, which is about 3–10 times more accurate than the PROSPECT simulation method for measured datasets. Secondly, the relationship between PCs and leaf biochemical components was investigated, and we found that the PCs are closely related to the leaf biochemical components and to the reflectance spectra. Only when the weighting coefficient of the most sensitive PC was employed to retrieve the leaf biochemical contents, the coefficients of determination for the PCA data-driven model were 0.69, 0.99, 0.94 and 0.68 for the specific leaf weight (SLW), equivalent water thickness (EWT), chlorophyll content (Cab) and carotenoid content (Car), respectively. Finally, statistical models for the retrieval of leaf biochemical contents were developed based on the weighting coefficients of the sensitive PCs, and the PCA data-driven models were validated and compared to the traditional VI-based and physically-based approaches for the retrieval of leaf properties. The results show that the PCA method shows similar or better performance in the estimation of leaf biochemical contents. Therefore, the PCA method provides a new and accurate data-driven method for reconstructing leaf reflectance spectra and also for retrieving leaf biochemical contents.

The response of wildtype and mutant cultivars of soybean to salt stress - comparing vegetative and reproductive phases on the basis of leaf biochemical contents, RWC, and stomatal conductance

ABSTRACTSoybean, as a major oilseed crop, is cultivated in many parts of the world including saline-affected regions. A four-replicated factorial experiment based on a completely randomized design with five salinity levels (control, 50, 100, 150, and 200 mM NaCl) and five wild-type and new mutant cultivars of soybean (Williams, Clark, M-4, M-7, and M-9) was done at two vegetative and reproductive phases to study leaf oxidants, antioxidants and osmolytes under salinity stress. The vegetative phase demonstrated higher K+:Na+ ratio, cell membrane stability, relative water content (RWC) and stomatal conductance to H2O (gs) accompanied with lower oxidative stress, antioxidants and osmolytes contrasting to reproductive phase. At both phenological phases, saline stress stimulated antioxidative defense owing to the enhancement of oxidative stress. The results indicated that mutants possessed a better antioxidative system than their wild-types.

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra.

Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (LIF) technology and reflectance spectra of crops are widely used to monitor leaf biochemical content. However, comparison between the fluorescence and reflectance spectra has been rarely investigated in the monitoring of LNC. In this study, the performance of the fluorescence and reflectance spectra for LNC estimation was discussed based on principal component analysis (PCA) and back-propagation neural network (BPNN). The combination of fluorescence and reflectance spectra was also proposed to monitor paddy rice LNC. The fluorescence and reflectance spectra exhibited a high degree of multi-collinearity. About 95.38%, and 97.76% of the total variance included in the spectra were efficiently extracted by using the first three PCs in PCA. The BPNN was implemented for LNC prediction based on new variables calculated using PCA. The experimental results demonstrated that the fluorescence spectra (R2 = 0.810, 0.804 for 2014 and 2015, respectively) are superior to the reflectance spectra (R2 = 0.721, 0.671 for 2014 and 2015, respectively) for estimating LNC based on the PCA-BPNN model. The proposed combination of fluorescence and reflectance spectra can greatly improve the accuracy of LNC estimation (R2 = 0.912, 0.890 for 2014 and 2015, respectively).

Deriving backscatter reflective factors from 32-channel full-waveform LiDAR data for the estimation of leaf biochemical contents.

Hyperspectral light detection and ranging (HSL) is a newly developed active remote sensing technique. In this study, we firstly presented an improved hyperspectral full-waveform LiDAR system with 32 detection channels. Then, the quality of the data collected from two types of leaves by this system was evaluated using signal to noise ratio. Two different reflective factors that can describe the backscatter capability of detected targets were developed based on the HSL data. Hundreds of vegetation indices (VIs) were calculated through a full search for the possible combination of the reflective factors at near-infrared and visible wavelengths. Finally, the high-dimensional VIs (n = 998) were used to estimate three leaf biochemical contents using principle component regression (PCR) models with cross validation. Results showed that high correlations were found between leaf biochemical contents and the HSL-derived VIs at shorter visible wavelengths. The prediction of biochemical contents obtained satisfactory results with a root mean squared error of 0.45% for nitrogen content (R2 = 0.71), 1.41 mgg-1 for chlorophylla/b content (R2 = 0.83), and 0.38 mgg-1 for carotenoid content (R2 = 0.77), respectively. To conclude, the improved HSL system showed great potential for the remote estimation of vegetation biochemical contents, which will significantly extend the scope of quantitative remote sensing with vegetation.

Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves

Hyperspectral remote sensing of leaf biochemicals is critical for understanding many biochemical processes. Leaf biochemical contents (e.g., protein, cellulose and lignin) in fresh and dry leaves have been quantified from hyperspectral data using empirical models. However, they cannot be retrieved for fresh leaves by inverting radiative transfer models. We demonstrated the applicability of PROSPECT leaf optical properties model in the separation of specific absorption coefficients for protein and cellulose+lignin following a newly proposed algorithm, and evaluated the feasibility in estimating leaf protein and cellulose+lignin content through model inversion. Assessment was performed across a large variety of plant species benefiting from the Leaf Optical Properties Experiment (LOPEX) dataset. To alleviate ill-posed problems, inversion was performed over different spectral subsets. The PROSPECT model with newly calibrated specific absorption coefficients was able to accurately reconstruct leaf reflectance and transmittance. Leaf protein and cellulose+lignin were estimated at moderate to good accuracies for both fresh and dry leaves. The spectral subset of 2100–2300nm yielded the most accurate estimation of leaf cellulose+lignin (R2=0.70, RMSE=5.21E−04g/cm2) and protein (R2=0.47, RMSE=2.75E−04g/cm2) in fresh leaves, which were comparable with those obtained from stepwise multiple linear regressions (protein: R2=0.83, RMSE=3.91E−04g/cm2; cellulose+lignin: R2=0.66, RMSE=2.02E−04g/cm2). Our results confirm the importance of selecting a proper spectral subset that contains sufficient information for a successful inversion. For the first time, we provide promising estimations of leaf protein in fresh leaves through inversion of a radiative transfer model, which can be applied at canopy level for regional mapping if coupled with a canopy reflectance model and air- or space-borne hyperspectral imaging.

Estimation of leaf biochemical content using a novel hyperspectral full-waveform LiDAR system

A novel hyperspectral full-waveform light detection and ranging (LiDAR) system was proposed in this study. First, the optical set-up of this system was introduced. Second, the spectral reflectance of two kinds of leaves was measured using this system as well as a common spectrometer. A workflow was proposed to show how to calculate spectral reflectance from the LiDAR data. Three vegetation indices (VIs) – simple ratio index, normalized difference vegetation index and photochemical reflectance index – were calculated using the reflectance derived from the data collected by these two instruments. Finally, the LiDAR-derived VIs were used to test for the estimation of leaf biochemical contents. Results show that: (1) LiDAR reflectance is different from that measured by spectrometer, but the LiDAR and spectrometer spectral shapes are clearly similar; (2) leaf biochemical contents can be successfully estimated by LiDAR-derived VIs with a root mean square error of 0.46–0.55% for nitrogen content, 1.42–2.98 mg g−1 for chlorophyll content and 0.35–0.56 mg g−1 for carotenoid content. These results demonstrate the possibilities for estimating leaf biochemical contents using the new hyperspectral full-waveform LiDAR data.

Biophotonic in situ sensor for plant leaves

Knowledge of the water concentration of plants can be helpful in several environmental and agricultural domains. There are many methods for the determination of water content in plant leaves; however, most of them give a relative moisture level or an analytical measure after a previous calibration procedure. Even for other biochemical compounds such as dry matter or chlorophyll, the measurement techniques could be destructive. For this reason, a nondestructive method has been developed to measure the biochemical compounds of a plant leaf, using an infrared spectroscopy technique. One important advantage is the simplicity of the device (RAdiomètre portatif de Mesure In Situ, RAMIS) and its capability to perform measurements in situ. The prototype is a leaf-clip configuration and is made of LEDs at five wavelengths (656, 721, 843, 937, and 1550 nm), and a silicon/germanium photosensor. To compute the water content of vegetative leaves, the radiative transfer model PROSPECT was implemented. This model can accurately predict spectral transmittances in the 400 nm to 2500 nm spectral region as a function of the principal leaf biochemical contents: water, dry matter, and chlorophyll. Using the transmittance measured by RAMIS into an inversion procedure of A Model of Leaf Optical Properties Spectra, we are able to compute the values of water contents that show an agreement with the water contents measured directly using dry weight procedures. This method is presented as a possibility to estimate other leaf biochemical compounds using appropriate wavelengths.

Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations

This study focuses on the potential of satellite hyperspectral imagery to monitor vegetation biophysical and biochemical characteristics through narrow-band indices and different viewing angles. Hyperspectral images of the CHRIS/PROBA sensor in imaging mode 1 (5 observation angles, 62 bands, 410–1005 nm) were acquired throughout a two-year period for a Mediterranean ecosystem fully covered by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident ecophysiological field measurements were conducted. Leaf area index (LAI), leaf biochemical content (chlorophyll a, chlorophyll b, carotenoids) and leaf water potential were measured. The hyperspectral images were corrected for coherent noises, cloud and atmosphere, in order to produce ground reflectance images. The reflectance spectrum of each image was used to calculate a variety of vegetation indices (VIs) that are already published in relevant literature. Additionally, all combinations of the 62 bands were used in order to calculate Normalized Difference Spectral Indices (NDSI ( x, y) ) and Simple Subtraction Indices (SSI ( x, y )). The above indices along with raw reflectance and reflectance derivatives were examined for linear relationship with the ground-measured variables and the strongest relationships were determined. It is concluded that higher observation angles are better for the extraction of biochemical indices. The first derivative of the reflectance spectra proved to be very useful in the prediction of all measured variables. In many cases, complex and improved spectral indices that are proposed in the literature do not seem to be more accurate than simple NDSIs such as NDVI. Even traditional broadband NDVI is proved to be adequate in LAI prediction, while green bands seem also very useful. However, in biochemical estimation narrow bands are necessary. Indices that incorporate red, blue and IR bands, such as PSRI, SIPI and mNDVI presented good performance in chlorophyll estimation, while CRI did not show any relevance to carotenoids and WI was poorly correlated to water potential. Moreover, analyses indicated that it is very important to use a near red-edge band (701 nm) for effective chlorophyll index design. SSIs that incorporate 701 nm with 511 or 605 nm showed best performance in chlorophyll determination. For carotenoid estimation, a band on the edge of carotenoid absorption (511 nm) combined with a red band performed best, while a normalized index of two water absorption bands (945, 971 nm) proved to be an effective water index. Finally, the attempt to investigate stress conditions through pigment ratios resulted in the use of the band centred at 701 nm.

GROWTH AND BIOCHEMICAL CONSTITUENTS OF OLIVES AS INFLUENCED BY IRRIGATION WITH TREATED INDUSTRIAL WASTEWATER

The suitability of treated industrial wastewater for irrigation of ‘Improved Nabali’, ‘Nabali’, and ‘Manzanillo’ olives was investigated. Irrigation with six electrical conductivity (EC = 0.78, 1.0, 2.0, 3.0, 4.0 and 5.0 dS m-1, respectively T0, T1, T2, T3, T4, T5) were compared in the greenhouse experiment. The olive trees in the field experiment were trickle-irrigated with potable water and treated wastewater. Regardless of the cultivar, length and number of new shoots, diameter of the main trunk, leaf area and dry weight of the plants were significantly enhanced under the influence of different treatments. ‘Improved Nabali’ was the most positively affected cultivar. The leaf biochemical contents from proline and total chlorophyll contents showed no variations among the treatments and cultivars. Proline content was enhanced by irrigation with wastewater only under the field condition. Olive oil acidity and peroxide value were not affected by treatments. These results indicate that this kind of wastewater is suitable for application to olive orchards under irrigation with continuous monitoring of mineral levels.

PROSPECT + SAIL models: A review of use for vegetation characterization

The combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model, also referred to as PROSAIL, has been used for about sixteen years to study plant canopy spectral and directional reflectance in the solar domain. PROSAIL has also been used to develop new methods for retrieval of vegetation biophysical properties. It links the spectral variation of canopy reflectance, which is mainly related to leaf biochemical contents, with its directional variation, which is primarily related to canopy architecture and soil/vegetation contrast. This link is key to simultaneous estimation of canopy biophysical/structural variables for applications in agriculture, plant physiology, or ecology, at different scales. PROSAIL has become one of the most popular radiative transfer tools due to its ease of use, general robustness, and consistent validation by lab/field/space experiments over the years. However, PROSPECT and SAIL are still evolving: they have undergone recent improvements both at the leaf and the plant levels. This paper provides an extensive review of the PROSAIL developments in the context of canopy biophysics and radiative transfer modeling.

The Propagation of Foliar Biochemical Absorption Features in Forest Canopy Reflectance: A Theoretical Analysis

Remotely sensed estimates of the foliar biochemical content of vegetation canopies could be used to derive indicators of ecosystem functioning at regional to global scales. In the past decade, a number of studies have reported strong correlations between the reflectance spectra of vegetation canopies and their foliar biochemical content. However, these studies have commonly employed multiple regression techniques or spectral indices to determine biochemical content, which have been found to be highly sensitive to variation in canopy architecture [such as leaf area index (LAI) and canopy closure] and understory. To date, these effects combined with the low signal-to-noise ratios of airborne spectrometers have inhibited the development of robust and portable spectral techniques for the estimation of canopy biochemical content. This paper reports on a theoretical study in which a leaf model, LIBERTY (leaf incorporating biochemicals exhibiting reflectance and transmittance yields), characterized specifically for conifer needles, was coupled with a hybrid geometric/radiative transfer bidirectional reflectance distribution function FLIGHT (forest light) model. By varying leaf biochemical content, LAI, canopy closure and understory, we analyzed the simulated canopy reflectance spectra to determine if the biochemical absorption features in leaf spectra were preserved at the canopy scale. Absorption features or wavelength regions that were both related to a specific biochemical of interest (water, lignin-cellulose) and persistent at the scale of both the leaf and the canopy were identified at a number of wavelengths or wavelength regions.

Remote sensing of forest canopy and leaf biochemical contents

Remote detection and measurement of the nitrogen and lignin contents of forest canopies could allow predictions of biogeochemical processes such as productivity, decomposition, and nutrient turnover rates. Spectral absorption features characteristic of proteins (containing nitrogen), lignin and other leaf constituents occur throughout the shortwave infrared region (1200–2400 nm). The lignin and nitrogen concentration of dried and ground deciduous leaves have been predicted from reflectance spectra obtained in the laboratory. The optimum wavelengths for prediction were selected using stepwise multiple linear regression. The prediction errors were comparable to chemical techniques. Analysis of the reflectance spectra of fresh, whole leaves has been limited thus far to conifer leaves but indicate spectral features predictive of nitrogen and lignin also found in airborne spectra. Airborne Imaging Spectrometer (AIS) were evaluated for whole forest canopy physical and chemical properties. Variations in spectral brightness were associated with variations in total water content of the foliar biomass. Comparison of forest spectra with spectrally flat targets revealed absorption features common to the canopy spectra between 1500 and 1700 nm which were tentatively attributed to absorption by lignin and starch. The AIS and laboratory data indicate strongly that absorption in the infrared region is influenced by biochemical characteristics.