Monitoring the effects of arsenic and chromium accumulation in Chinese brake fern (Pteris vittata)

The objectives of this study were (i) to investigate the feasibility of using spectral reflectance for monitoring As and Cr accumulation in Chinese brake fern (Pteris vitatta), and (ii) to search for spectral indices sensitive to structural changes caused by metal accumulation during the process of phytoremediation. Potted Chinese brake fern plants were exposed to As (100 and 300 ppm) and Cr (300 and 600 ppm) treatments for 22 days. The plants were then harvested and analysed for metal accumulation. Diffuse reflectance spectra (350–2500 nm) of the plant canopies were collected regularly throughout the metal treatment period using a portable spectroradiometer. Leaf reflectance is governed by leaf surface properties, internal structure, and foliar pigments and biochemical components. Leaf samples were collected and analysed for structural changes through microscopic observations. Our microscopic studies on changes of leaf structure provide insight into the physical changes that are remotely detected as changes in reflectance, and may permit extrapolation of these results to other plant species. Cr accumulation resulted in a decrease in biomass, relative water content (RWC), and changes in the internal structure of the leaf. The structural and spectral results show significant changes in Cr‐treated plants while the changes were minimal in As‐treated plants compared to untreated plants. Our spectral analysis revealed that a unique ratio index R ₁₁₁₀/R ₈₁₀ can be used to monitor structural changes in plants due to accumulation of Cr. This index distinguished Cr‐treated plants from untreated and As‐treated plants. The Normalized Difference Vegetative Index (NDVI) distinguished stressed plants, but NDVI cannot distinguish Cr‐stressed plants from As‐stressed plants. Our results show that brake fern can accumulate significant amounts of Cr in shoots (2108 mg kg^?1 dry weight), but it is not a hyperaccumulator for Cr because much higher Cr accumulation was found in roots (7686 mg kg^?1 dry weight). This study suggests that the infrared reflectance spectrum (800–1300 nm) of plant canopy may provide a non‐intrusive monitoring method to access the physiological status of plants grown in heavy metal‐contaminated soil.     

Reflectance characteristics of major land surfaces in slash‐and‐burn ecosystems in Laos

The in situ reflectance spectra in the 400–2500 nm wavelength region were obtained using a portable radiometer over a range of land surfaces including burnt fields, crop canopies, and fallow vegetation at different community ages in slash‐and‐burn ecosystems in Laos. Normalized difference spectral indices (NDSI[i,j] = [R_j ?R_i ]/[R_j +R_i ]) were derived using reflectance R_i and R_j at i and j nm wavelengths for a thorough combination (14 706 pairs) of 172 wavebands (10‐nm resolution). The separability of burnt fields from dry/senescent vegetation was highest at NDSI[1090, 2390], whereas it was highly discriminated from fallow and crop vegetation by NDSI[760, 1970]. NDSIs using 730–760 nm with 1970–1990 nm showed the largest differences between dry/senescent vegetation and fallow or crop vegetation. None of the NDSIs was useful in discriminating between fallow and crop vegetations or between slashed/senescent vegetation and crop residue/abandoned field. Community age and biomass of fallow vegetation could not be inferred directly from spectral information, since no NDSIs showed any significant differences among crop and fallow vegetation that had a large variability in the amount of green vegetation. Results would provide useful information for various applications of optical satellite sensor images especially in assessments of land use or post‐fire regeneration of vegetation.     

Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat

While certain spectral reflectance indices have been shown to be sensitive to the expression of a range of performance-related traits in crops, knowledge of the potentially confounding effects associated with plant anatomy could help improve their application in phenotyping. Morphological traits (leaf and spike wax content, leaf and spike orientation, and awns on spikes) were studied in 20 contrasting advanced wheat lines to determine their influence on spectral indices and in their association with grain yield under well-irrigated conditions. Canopy reflectance (400–1100 nm) was determined at heading and grain filling during two growing seasons and three vegetation indices (VIs; red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), and simple ratio (SR)), and five water indices (WIs; one simple WI and four normalized WIs (NWI-1, NWI-2, NWI-3, and NWI-4)) were calculated. The major reflectance fluctuations caused by the differences in leaf and spike morphology mainly occurred in the infrared region (700–1100 nm) and little variation in the visible region (400–700 nm). The NWI-3 ((R₉₇₀ – R₈₈₀)/(R₉₇₀ + R₈₈₀)) consistently showed a stronger association with yield than the RNDVI by using uncorrected canopy reflectance (original raw data) and data adjusted by scattering and smoothing. When canopy reflectance was corrected by a scattering method, the NWI-3 and a modified RNDVI with 958 nm showed the strongest correlations with grain yield by grouping lines for waxy leaves and spikes, curved leaves, and erect and awnless spikes. The results showed that the relationship between the spectral indices and grain yield can be improved (higher correlations) by correcting canopy reflectance for confounding effects associated with differences in leaf and spike morphology.     

Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.)

Bahia grass (Paspalum notatum Flugge.) plants were grown in silica sand and irrigated daily with one of five levels of Zn (0, 0.5, 25, 50, or 100 mg l⁻¹) to determine the effects of the heavy metal on the growth and development of plant canopies. Healthy and stressed plants were measured with two hyperspectral imagers, laser-induced fluorescence spectroscopy (LIFS), and laser-induced fluorescence imaging (LIFI) systems in order to determine if the four handheld remote sensing instruments were equally capable of detecting plant stress and measuring canopy chlorophyll levels in bahia grass. Symptoms of bahia grass plants grown at deficient (0 mg l⁻¹) or toxic (25, 50, or 100 mg l⁻¹) concentrations of Zn were dominated by leaf chlorosis and plant stunting. Leaf fresh weight, leaf dry weight, CO₂ assimilation, total chlorophyll, and leaf thickness followed (+) quadratic models in which control plants (0.5 mg l⁻¹ Zn) exhibited higher responses than plants grown at either deficient or toxic levels of Zn. Normalized difference vegetation index [NDVI=(NIR−Red)/(NIR+Red)] and ratio vegetation index [RVI=R₇₅₀/R₇₀₀, in which R denotes reflectance] values were calculated for calibrated digital images from both hyperspectral imagers. The NDVI and RVI values from both hyperspectral imagers were fit best by (+) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (−) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. Furthermore, both NDVI and RVI algorithms were effective in predicting the concentrations of chlorophyll in canopies of bahia grass grown at the various levels of Zn. In contrast, red/far-red (R/FR) fluorescence ratios estimated from leaf fluorescence values measured with the LIFS and LIFI instruments were fit best by (−) quadratic models when treatments were constrained between 0 and 100 mg l⁻¹ Zn, but were fit best by linear regression models with (+) slopes when treatments were constrained between 0.5 and 100 mg l⁻¹ Zn. A series of regression analyses were conducted among plant biometric, biochemical, and leaf anatomical parameters (treated as independent variables) and the remote sensing algorithms, NDVI, RVI, blue/green (BL/GR), and R/FR (treated as dependant variables). In general, residuals were significantly higher for NDVI and RVI models compared to the BL/GR and R/FR models indicating that the NDVI and RVI algorithms were able to measure total chlorophyll and plant biomass more accurately than the BL/GR and R/FR algorithms. However, unique capabilities of LIFS and LIFI instruments continue to argue for the development of laser-induced fluorescence remote sensing technologies.     

Estimating leaf nitrogen concentration from similarities in fresh and dry leaf spectral bands using a model population analysis framework

Fresh leaf spectral reflectance is primarily influenced by leaf water content and structural aspects such as the inter-cellular spaces within the spongy mesophyll, which also interfere with the estimation of the leaf nitrogen content. It is therefore essential to identify spectral bands that are least affected by the above perturbing factors for improving leaf nitrogen estimation for fresh leaves across any landscape. Wavelengths selection plays a vital role in identifying the best spectral features for assessing leaf nitrogen concentration from hyperspectral data of dry and fresh leaves. The primary objective of this study was to determine typical optimal bands for leaf nitrogen estimation from spectra (400–2500 nm) of whole fresh and dry leaves for the same specimens of Eucalyptus grandis. This was achieved via the use of competitive adaptive re-weighted sampling (CARS), and Monte Carlo cross-validation-competitive adaptive re-weighted sampling (MCCV-CARS) band selection approaches. Bands selected (931 nm, 1003 nm, 1027 nm, 1036 nm, 1177 nm, and 1180 nm) via the MCCV-CARS approach yielded the highest estimation accuracy for both fresh predicted coefficient of determination (R²_cal) = 0.82 and predicted root mean square error (RMSE_P) = 0.14) and dry leaves (R²_P = 0.88 and RMSE_P = 0.13) when compared to CARS (2044 nm, 2107 nm, and 2188 nm) only. The identified spectral features could be relevant for assessing leaf nitrogen concentration for different seasons, for example, wet to dry season.     

Exploring the potential of leaf reflectance spectra for retrieving the leaf maximum carboxylation rate

The maximum carboxylation rate (V_cmax) is a key photosynthetic parameter that is determined by the leaf biochemistry and environmental conditions. Numerous studies have shown that plant biochemical, physiological and structural parameters can be estimated from reflectance spectra. Therefore, it is reasonable to assume that V_cmax can be spectrally determined. Here, we investigate the potential of leaf reflectance spectra for retrieving the maximum carboxylation rate of leaves. Measurements of leaf reflectance, carbon dioxide (CO₂) response curves, leaf chlorophyll-a + b (chl-a + b) etc., were made on 80 crop, shrub and tree leaves. Then, the leaf V_cmax,25 was linked to leaf biochemistry and spectral reflectance. A reliable relationship, with a coefficient of determination (R²) value of 0.75, was found between the leaf chl-a + b content and V_cmax,25. The leaf V_cmax,25 values were also significantly correlated with chl-a + b-sensitive spectral indices with the highest R² value that was found being 0.83 for the ratio spectral index (RSI) using reflectances at 1089 nm and 695 nm. Finally, multiple stepwise regression (MSR) and a partial least-squares regression (PLSR) modelling approach were used to estimate V_cmax,25 from leaf reflectances. The results confirmed that V_cmax,25 can be reliably estimated from leaf reflectance spectra and give an R² value >0.80. These findings show that leaf chl-a + b can be used as a proxy for leaf V_cmax,25 and that leaf V_cmax,25 can be spectrally determined using leaf reflectance data.     

Reflectance spectroscopy for the assessment of soil salt content in soils of the Yellow River Delta of China

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, we collected 95 soil samples from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. Sample plots were selected based on a field investigation and the corresponding soil salinity classification map to maximize variations of saline characteristics in the soil. Spectral reflectances of air‐dried soil samples were measured using an Analytical Spectral Device (ASD) spectrometer (350–2500 nm) with an artificial light source. In the Yellow River Delta, the dominant chemical in the saline soil was NaCl and MgCl₂. Soil spectra were analysed using two‐thirds of the available samples, with the remaining one‐third withheld for validation purposes. The analysis indicated that with some preprocessing, the reflectance at 1931–2123 nm and 2153–2254 nm was highly correlated with soil salt content (S _SC). In the spectral region of 1931–2123 nm, the correlation R ranged from ?0.80 to ?0.87. In the region of 2153–2254 nm, the S _SC was positively correlated with preprocessed reflectance (0.79–0.88). The preprocessing was done by fitting a convex hull to the reflectance curve and dividing the spectral reflectance by the value of the corresponding convex hull band by band. This process is called continuum removal, and the resulting ratio is called continuum removed reflectance (CR reflectance). However, the S _SC did not have a high correlation with the unprocessed reflectance, and the correlation was always negative in the entire spectrum (350–2500 nm) with the strongest negative correlation at 1981 nm (R = ?0.63). Moreover, we found a strong correlation (R = 0.91) between a soil salinity index (S _SI: constructed using CR reflectance at 2052 nm and 2203 nm) and S _SC. We estimated S _SC as a function of S _SI and S _SI′ (S _SI′: constructed using unprocessed reflectance at 2052 nm and 2203 nm) using univariate regression. Validation of the estimation of S _SC was conducted by comparing the estimated S _SC with the holdout sample points. The comparison produced an estimated root mean squared error (RMSE) of 0.986 (S _SC ranging from 0.06 to 12.30 g kg^?1) and R ² of 0.873 for S _SC with S _SI as independent variable and RMSE of 1.248 and R ² of 0.8 for S _SC with S _SI′ as independent variable. This study showed that a soil salinity index developed for CR reflectance at 2052 nm and 2203 nm on the basis of spectral absorption features of saline soil can be used as a quick and inexpensive method for soil salt‐content estimation.     

Estimation of chlorophyll‐a concentration in Lake Tai,China using in situ hyperspectral data

Reflectance spectra of water in Lake Tai of East China were measured at 28 monitoring stations with an ASD FieldSpec spectroradiometer at an interval of 1.58 nm over five days in each month from June to August of 2004. Water samples collected at these stations were analyzed in the laboratory to determine chlorophyll‐a (chl‐a) concentration. Twenty‐eight spectral reflectance curves were standardized and correlated with chl‐a concentration. Examination of these curves reveals a peak reflectance at 719 nm. Chl‐a concentration level in the Lake was most closely correlated with the reflectance near 700 nm. If regressed against the reflectance at the wavelength of 667 nm (R ₆₆₇), chl‐a concentration was not accurately estimated at R ² = 0.494. Accuracy of estimation was improved to R ² = 0.817 using the maximum reflectance. A higher accuracy of 0.837 was achieved using the peak reflectance at 719 nm (R ₇₁₉) because it does not drift with the level of chl‐a concentration. The highest accuracy of estimation was achieved at R ² = 0.868 using R ₇₁₉/R ₆₆₇.     

Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing

Arsenic (As) is a common soil contaminant that can be accumulated into plant parts. The ability to detect As in contaminated plants is an important tool to minimize As-induced health risks in humans. Near-infrared (NIR) spectra are strongly affected by leaf structural characteristics. Therefore, quantitative analyses of structural changes in the arrangement of mesophyll cells caused by As will help to explain spectral responses to As. The objectives of this study were to use stereological methods to quantify internal structural changes in leaves with As treatment in spinach plants, and to relate these changes to leaf spectral properties in NIR spectra. Hydroponically grown spinach was treated with 0, 5, 10 and 20 μmol l^?1 for four weeks in a growth chamber. Spectral properties of leaves were obtained for visible and infrared frequencies. Leaf structural properties, such as mesophyll thickness and mesophyll surface area, were measured using stereological methods. Quantitative analysis of leaf structure showed that total leaf thickness and intercellular spaces in spongy mesophyll cells decreased with increasing As treatment. Changes in leaf reflectance in NIR wavelengths were strongly correlated with leaf As concentration and leaf structural changes. Multiple linear regression of leaf reflectance values at the highest correlated wavelengths (1048, 1098 and 1080 nm) generated an R ² value of 0.69. Results from this study support the hypothesis that relationships between leaf structure and reflectance may be useful in the interpretation of spectral data to detect plant leaf As concentration.     

Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance

As a first step in developing classification procedures for remotely acquired hyperspectral mapping of mangrove canopies, we conducted a laboratory study of mangrove leaf spectral reflectance at a study site on the Caribbean coast of Panama, where the mangrove forest canopy is dominated by Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle. Using a high‐resolution spectrometer, we measured the reflectance of leaves collected from replicate trees of three mangrove species growing in productive and physiologically stressful habitats. The reflectance data were analysed in the following ways. First, a one‐way ANOVA was performed to identify bands that exhibited significant differences (P value<0.01) in the mean reflectance across tree species. The selected bands then formed the basis for a linear discriminant analysis (LDA) that classified the three types of mangrove leaves. The contribution of each narrow band to the classification was assessed by the absolute value of standardised coefficients associated with each discriminant function. Finally, to investigate the capability of hyperspectral data to diagnose the stress condition across the three mangrove species, four narrow band ratios (R ₆₉₅/R ₄₂₀, R ₆₀₅/R ₇₆₀, R ₆₉₅/R ₇₆₀, and R ₇₁₀/R ₇₆₀ where R ₆₉₅ represents reflectance at wavelength of 695nm, and so on) were calculated and compared between stressed and non‐stressed tree leaves using ANOVA.

Results indicate a good discrimination was achieved with an average kappa value of 0.9. Wavebands at 780, 790, 800, 1480, 1530, and 1550 nm were identified as the most useful bands for mangrove species classification. At least one of the four reflectance ratio indices proved useful in detecting stress associated with any of the three mangrove species. Overall, hyperspectral data appear to have great potential for discriminating mangrove canopies of differing species composition and for detecting stress in mangrove vegetation.     

Spectral reflectance at sub‐leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression

This paper introduces a methodology able to discriminate between non‐stressed plants and N, P and K stress symptoms in spring barley grown under controlled conditions, utilizing the spectral and spatial dimensions simultaneously. Nine spectral measurements in the range 450–1000 nm were taken for each plant. The measuring points were spatially located at the tip, middle and base of the last three fully developed leaves. This design generated a four‐way data set consisting of measurements as a function of (i) the specific plant, (ii) the spectral wavelength, (iii) the plant leaf position and (iv) the position on the leaf. Multiway partial least squares regression analysis with dummy variables was able correctly to classify the four nutrient conditions with 92% accuracy regardless of the respective growth stages within a time window of 2 weeks. The addition of the spatial dimension to the spectral dimension proved to be a promising nutrient diagnostic tool. Without performance loss it was possible to reduce the hyperspectral resolution to a resolution of three wavelengths. The three selected 2 nm wide bands were R450, R700 and R810, which agrees well with the literature on plant spectral reflectance in relation to nutritional stress.     

Hyperspectral indices to diagnose leaf biotic stress of apple plants,considering leaf phenology

Novel and existing hyperspectral vegetation indices were evaluated in this study, with the aim of assessing their utility for accurate tracking of leaf spectral changes due to differences in biophysical indicators caused by apple scab. Novel indices were extracted from spectral profiles by means of narrow‐waveband ratioing of all possible two‐band combinations between 350 nm and 2500 nm at nanometer intervals (2 311 250 combinations) and all possible two‐band derivative combinations. Narrow‐waveband ratios consisting of wavelengths of approximately 1500 nm and 2250 nm, associated with water content, have proven to be the most appropriate for detecting apple scab at early developmental stages. Logistic regression c‐values ranged from 0.80 to 0.88. At a more developed infection stage, vegetation indices such as R₄₄₀/R₆₉₀ and R₆₉₅/R₇₆₀ exhibited superior distinction between non‐infected and infected leaves. Identified derivative indices were located in similar regions. It therefore was concluded that the most appropriate indices at early stages of infection are ratios of wavelengths situated at the water band slopes. The choice of appropriate indices and their discriminatory performances, however, depended on the phenological stage of the leaves. Hence, an undisturbed 20‐day growth profile was examined to assess the effect of physiological changes on spectral variations at consecutive growth stages of leaves. Results suggested that an accurate distinction could be made between different leaf developmental stages using the 570 nm, 1460 nm, 1940 nm and 2400 nm wavelengths, and the red‐edge inflection point. These results are useful to crop managers interested in an early warning system to aid proactive system management and steering.     

Effects of adaxial and abaxial surface on the estimation of leaf chlorophyll content using hyperspectral vegetation indices

Vegetation indices are frequently used for the non-destructive assessment of leaf chemistry, especially chlorophyll content. However, most vegetation indices were developed based on the statistical relationship between the spectral reflectance of the adaxial leaf surface and chlorophyll content, even though abaxial leaf surfaces may influence reflectance spectra because of canopy structure or the inclination of leaves. In the present study, reflectance spectra from both adaxial and abaxial leaf surfaces of Populus alba and Ulmus pumila var. pendula were measured. The results showed that structural differences of the two leaf surfaces may result in differences in reflectance and hyperspectral vegetation indices. Among 30 vegetation indices tested, R₆₇₂/(R₅₅₀ × R₇₀₈) had the smallest difference (4.66% for P. alba, 2.30% for U. pumila var. pendula) between the two blade surfaces of the same leaf in both species. However, linear regression analysis showed that several vegetation indices (R₈₅₀ ? R₇₁₀)/(R₈₅₀ ? R₆₈₀), VOG₂, D₇₃₀, and D₇₄₀, had high coefficients of determination (R² > 0.8) and varied little between the two leaf surfaces of the plants we sampled. This demonstrated that these four vegetation indices had relatively stable accuracy for estimating leaf chlorophyll content. The coefficients of determination (R²) for the calibration of P. alba leaves were 0.92, 0.98, 0.93, and 0.95 on the adaxial surfaces, and 0.88, 0.87, 0.88, and 0.92 on the abaxial surfaces. The coefficients of determination (R²) for the calibration of U. pumila var. pendula leaves were 0.85, 0.91, 0.86, and 0.90 on adaxial surface, and 0.80, 0.80, 0.84, and 0.88 on abaxial surface. These four vegetation indices were readily available and were little influenced by the differences in the two leaf surfaces during the estimation of leaf chlorophyll content.     

Spatio-temporal remote sensing of vertical and seasonal chlorophyll-a:b ratio in Tamarix ramosissima

Remotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R² > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRI_mod, R₅₁₀–R₅₇₀ nm) performed better than the original PRI (PRI, R₅₃₁–R₅₇₀ nm) with R² improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRI_mod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.     

Bio‐optical properties and estimation of the optically active substances in Lake Tianmuhu in summer

Bio‐optical properties in an optically complex and biologically productive region of Lake Tianmuhu were determined in three cruises from June to August 2006. The concentrations of three optically active substances, tripton C _Tripton (calculated from total suspended matter and chlorophyll‐a (Chla) and phaeophytin‐a (Pa)), phytoplankton pigment C _Chla+Pa , and chromophoric dissolved organic matter (CDOM) a _CDOM(440), were predicted from the estimated irradiance reflectance based on in situ measurements and laboratory analyses. The total relative contributions of phytoplankton, tripton, CDOM and pure water over the range of photosynthetically active radiation (PAR) (400–700 nm) were 36.1%, 24.2%, 15.9% and 23.8%, respectively. The dominant contribution of phytoplankton to the total absorption was due to high phytoplankton pigment concentration. The range and variation in irradiance reflectance and diffuse attenuation coefficient derived from a bio‐optical model, based on inherent optical properties, compared well with the measured variability. A reasonably strong relationship (R² = 0.92) was observed between irradiance reflectance at 780 nm R(780) and C _Tripton. For our data set, the best algorithm for C _Chla+Pa used the three‐band reflectance model [R ^?1(688)?R ^?1(717)]×R(747). The a _CDOM(440) could be estimated using the ratio of irradiance reflectance R(682)/R(555). The retrieval accuracy (R²) of tripton, phytoplankton pigment and CDOM was 0.92, 0.87 and 0.91, respectively, while the rms. error was 0.90 mg l^?1 (18.2%), 3.27 µg l^?1 (14.8%) and 0.073 m^?1 (15.3%), respectively. Estimation of the concentrations of the three optically active substances was reasonably accurate based on inherent optical properties measurement.     

Variations in soil structure and reflectance during a controlled crusting experiment

The results of an experiment to show variations in the directional reflectance factor of a Luvisol during a controlled crusting experiment are described. Soil sampled in the field after tillage was sieved into free‐draining trays, and exposed to artificial rainfall for differing periods of time, ranging from 5 to 60 min. The resulting samples demonstrated different stages in the development of the soil's structural crust. The topography of each dried sample was characterized over a 5×5 cm area using a laser profilometer, and digital surface models (DSMs) were subsequently analysed using variogram models. DSMs were also used to generate statistical measures of random roughness. Directional reflectance factors of each sample were characterized in the solar principal plane under clean skies using an ASD FieldSpec Pro spectroradiometer, using an 8° foreoptic attached to an A‐frame device. Directional reflectance factors were analysed in relation to spatial statistical measures obtained from the laser profilometer data. The results demonstrate that changes in the sill variance of soil samples following crusting, and hence changes in soil structure, were best described by backscattered radiation measured at +30° in the visible and near‐infrared (e.g. R ² = 0.947 (658 nm)), and at +15° in the short‐wave infrared (e.g. R ² = 0.992 (1700 nm)). View zeniths are expressed from the nadir, and were relative to the solar zenith angle, which ranged from 80.76° to 74.55° during the measurement sequences. The results from these tests show great promise for broader‐scale monitoring of soil condition, particularly when considered in the context of the new pointable remote sensing systems in operation, coupled with new‐generation sensors with in‐built directional capabilities.     

Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements

Landsat TM data and field spectral measurements were used to evaluate chlorophyll‐a (Chl‐a) concentration levels and trophic states for three inland lakes in Northeast China. Chl‐a levels were estimated applying regression analysis in the study. The results obtained from the field reflectance spectra indicate that the ratio between the reflectance peak at 700 nm and the reflectance minimum at 670 nm provides a relatively stable correlation with Chl‐a concentration. Their determination of coefficients R ² is 0.69 for three lakes in the area. From Landsat TM data, the results show that the most successful Chl‐a was estimated from TM3/TM2 with R ² = 0.63 for the two lakes on 26 July 2004, from TM4/TM3 with R ² = 0.89 for the two lakes on 14 October 2004, and from the average of TM2, TM3 and TM4 with R ² = 0.72 for the three lakes tested on 13 July 2005. These results are applicable to estimate Chl‐a from satellite‐based observations in the area. We also evaluate the trophic states of the three lakes in the region by employing Shu's modified trophic state index (TSI_M) for the Chinese lakes' eutrophication assessment. Our study presents the TSI_M from different TM data with R ² more than 0.73. The study shows that satellite observations are effectively applied to estimate Chl‐a levels and trophic states for inland lakes in the area.     

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.     

Detection of nickel‐induced stress using laser‐induced fluorescence signatures from leaves of wheat seedlings

The continuous increase of heavy metal ions in the environment is imposing serious problems in agricultural yield and increases human health threats through accumulation in the food chain. Various studies have shown that heavy metals influence the metabolic processes and pigment concentrations in leaves, and thus affect the laser induced fluorescence (LIF) spectra. Leaf level in vivo LIF spectra using the 488 nm and 355 nm laser lines, fluorescence induction kinetics (FIK) using the 488 nm laser line, and photosynthetic pigments of the control and nickel treated wheat seedlings were measured. The peak parameters of the blue and UV‐excited spectral bands were calculated by Gaussian curve fitting. The FIK measured at both the chlorophyll (Chl) fluorescence bands was used to evaluate the fluorescence decrease ratio (R _Fd). The variations in R _Fd and fluorescence ratios of intensities, bandwidth and band area, with varying concentrations of nickel, were revealed as promising parameters in determining the health status of wheat seedlings. These leaf level findings can be extended at canopy level in the field using laser based light detection and ranging (LIDAR) systems.