First Derivative Reflectance Research Articles

Estimation of rice neck blasts severity using spectral reflectance based on BP-neural network

Estimation of rice disease using spectral reflectance is important to non-destructive, rapid, and accurate monitoring of rice health. In this study, the rice reflectance data and disease index (DI) were determined experimentally and analyzed by single wave correlation, regression model and neural network model. The result showed that raw spectral reflectance and first derivative reflectance (FDR) difference of the rice necks under various disease severities is clear and obvious in the different spectral regions. There was also significantly negative or positive correlation between DI and raw spectral reflectance, FDR. The regression model was built with raw and first derivative spectral reflectance, which was correlated highly with the DI. However, due to rather complicated non-linear relations between spectral reflectance data and DI, the results of DI retrieved from the regression model was not so ideal. For this reason, an artificial neural network model (BP model) was constructed and applied in the retrieval of DI. For its superior ability for solving the non-linear problem, the BP model provided better accuracy in retrieval of DI compared with the results from the statistic model. Therefore, it was implied that the rice neck blasts could be predicted by remote sensing technology.

Identifying hyperspectral characteristics of wetland species using in situ data

Hyperspectral data have become a powerful tool for ground feature identification and have been applied to build a hyperspectral library. Identification of wetland species is important in determining wetland composition, ecosystem types and spatial mapping. This paper investigated continuity and hyper-bands in the hyperspectral characteristics of wetland plants by considering attributes of hyperspectral data. Derivative reflectance (DR) and continuum removal (CR) methods were used to extract characteristics from the original spectra to define the continuity attribute. Four factors were used for species identification in the three spectral absorption regions (1: 400–530 nm, 2: 550–730 nm and 3: 930–1000 nm): slope ( K), area of absorption curve ( A), area of left wing of absorption curve (AL), and symmetry ( S) were constructed by CR method. We reduced spectral bands from hyper-bands by using the Mahalanobis distance (MD) applied for identifying wetland species. The results from CR factor analysis showed that all the selected bands are mainly located in the typical chlorophyll absorption and water absorption region. The factors derived from absorption regions and factors (e.g., K1, K3, S3, Al, AL1, A2 and AL2) vary for different species and allow wetland species identification, while the factors like K2, A3 and AL3 are limited to identify emergent, floating-leaf and submerged plant types. The results from MD analysis showed that the first derivative reflectance (FDR), second derivative reflectance (SDR) and CR illustrated the spectra variances separating wetland species. There are five bands of the original spectra, five bands of FDR, six bands of SDR, and four bands of CR were selected as the optimal bands for species identification. Our findings indicate that CR factor analysis and MD analysis can be applied to model establishment for wetland species identification.

Correlation between soil apparent electroconductivity and plant hyperspectral reflectance in a managed wetland

The apparent electrical conductivity (σa) of soil is influenced by a complex combination of soil physical and chemical properties. For this reason, σa is proposed as an indicator of plant stress and potential community structure changes in an alkaline wetland setting. However, assessing soil σa is relatively laborious and difficult to accomplish over large wetland areas. This work examines the feasibility of using the hyperspectral reflectance of the vegetation canopy to characterize the σa of the underlying substrate in a study conducted in a Central California managed wetland. σa determined by electromagnetic (EM) inductance was tested for correlation with in-situ hyperspectral reflectance measurements, focusing on a key waterfowl forage species, swamp timothy (Crypsis schoenoides). Three typical hyperspectral indices, individual narrow-band reflectance, first-derivative reflectance and a narrow-band normalized difference spectral index (NDSI), were developed and related to soil σa using univariate regression models. The coefficient of determination (R 2) was used to determine optimal models for predicting σa, with the highest value of R 2 at 2206 nm for the individual narrow bands (R 2 = 0.56), 462 nm for the first-derivative reflectance (R 2 = 0.59), and 1549 and 2205 nm for the narrow-band NDSI (R 2 = 0.57). The root mean squared error (RMSE) and relative root mean squared error (RRMSE) were computed using leave-one-out cross-validation (LOOCV) for accuracy assessment. The results demonstrate that the three indices tested are valid for estimating σa, with the first-derivative reflectance performing better (RMSE = 30.3 mS m−1, RRMSE = 16.1%) than the individual narrow-band reflectance (RMSE = 32.3 mS m−1, RRMSE = 17.1%) and the narrow-band NDSI (RMSE = 31.5 mS m−1, RRMSE = 16.7%). The results presented in this paper demonstrate the feasibility of linking plant–soil σa interactions using hyperspectral indices based on in-situ spectral measurements.

Testing genetic algorithm as a tool to select relevant wavebands from field hyperspectral data for estimating pasture mass and quality in a mixed sown pasture using partial least squares regression

AbstractIt is now accepted that partial least squares (PLS) regression with waveband selection might improve their predictive accuracy in multivariate calibration of models to describe pasture mass and quality. Recently, genetic algorithm (GA) has been shown to be a suitable method for selecting wavebands in the laboratory calibrations. This study aimed to investigate the performance of genetic algorithms PLS (GA‐PLS) regression analyses for estimating green forage biomass (GBM), neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL) and crude protein (CP) concentrations of herbage and herbage mass in CP (CPm) from field hyperspectral data at canopy scale. The predictive ability of GA‐PLS was compared with that of iterative stepwise elimination PLS (ISE‐PLS) and standard full‐spectrum PLS (FS‐PLS), using first derivative reflectance (FDR) spectra data. Canopy reflectance measurements and plant sampling were obtained from 50 selected sites in each of two seasons; spring (May) and summer (July) 2007. For all parameters, cross‐validated coefficients of determination (R2) increased and root mean square error values decreased, respectively, with GA wavebands selection. The number of wavebands selected in the GA‐PLS model ranged between 63 (4.0% of all 1563 available wavebands) and 167 (10.7%), suggesting that over 89% of wavebands were redundant. Between ISE‐PLS and GA‐PLS models, higher R2 values and lower root mean squared errors of prediction were obtained from GA‐PLS prediction for all parameters except CP concentration. Particularly, GA based waveband selection greatly improved ADF (R2 = 0.68–0.77) and ADL (R2 = 0.47–0.59) predictions. These results suggest that pasture quality and GBM can be predicted from field hyperspectral measurements using a GA‐PLS model, and that the GA‐PLS has the advantage of tuning the optimum bands for PLS regression, giving better predictive ability.

Determination of leaf pigment content in Calluna vulgaris shoots from spectral reflectance

Leaf pigment concentrations are indicative of a range of plant physiological properties and processes. The measurement of leaf spectral reflectance is a rapid, non-destructive method for determining pigment content, and a large number of spectral indices have been developed for the estimation of leaf pigment content. Despite their ‘applicability’ across many species types, some ecologically important species remain to be explored. The objective of this paper was to investigate a wide range of hyperspectral indices for determining the chlorophyll and carotenoid content in a microphyllous and sclerophyllous species, Calluna vulgaris. We carried out spectral measurements on individual heather shoots with a handheld GER-1500 spectroradiometer, and sampled each measured shoot for biochemical analysis using high-performance liquid chromatography (HPLC). We found that several previously published indices performed relatively poorly and yielded coefficients of determination (R2) for chlorophyll ranging from 0.34 to 0.66, with the first derivative of reflectance at the red edge yielding the highest correlation with chlorophyll content (R2 = 0.66). Only one of the carotenoid indices we tested (the Photochemical Reflectance Index, PRI) provided a strong correlation with the de-epoxidation state of the xanthophyll cycle (R2 = 0.78). The other previously published carotenoid indices performed poorly within our data set. We concluded that only a few of the so-called ‘widely applicable’ indices were applicable to use with this data set, which would present limitations when working with remotely sensed data at a larger scale where a mix of species, including Calluna vulgaris, is present.

Hyperspectral analysis of snow reflectance to understand the effects of contamination and grain size

AbstractReflectance data for contaminated and different grain-size snow were collected using a spectroradiometer ranging from 350 to 2500 nm. Contamination was predominantly due to soil. The radiometer data were binned at 10 nm intervals by averaging, and then principal component analysis, shape, size and strength of the absorption peak, first and second derivatives were computed, providing information about the effect of grain size and contamination on snow reflectance. Relative strength for contamination and grain size showed a distinct reverse pattern at 1025 nm after continuum removal. Band absorption depth at 1025 nm showed an increase with increasing snow grain size, whereas the band depth was found to decrease with increased soil contamination. The curve shape was right asymmetric and showed a change to left asymmetry with increase in contamination. The first derivative of reflectance in the visible region showed a shift of peak due to contamination. Soil contamination significantly reduced the albedo of snow at a low level of contamination but showed little influence at higher level. Relative strength, shape of curve and reflectance characteristics have shown the potential to identify the influence of contamination and grain-size based metamorphism using satellite-based hyperspectral remote sensing.

Field radiometer with canopy pasture probe as a potential tool to estimate and map pasture biomass and mineral components: A case study in the Lake Taupo catchment, New Zealand

Precision farming requires data on resource status at a very fine, within‐paddock scale which is impractical to collect by traditional sampling methods. This paper demonstrates the potential of a field radiometer in conjunction with a canopy pasture probe (CAPP) and global positioning system (GPS) to predict and map the spatial distribution patterns of herbage biomass and mass of nutrients, such as nitrogen (N), phosphorous (P), potassium (K), and sulphur (S) in hill country grassland. The accuracy of the calibration model using partial least squares (PLS) regression was assessed by using coefficient of determination (R 2) and the ratio of prediction to standard deviation (RPD). Continuum‐removed derivative reflectance (CRDR) data used in a PLS model gave an excellent prediction of the standing masses of N, P, and S (R 2> 0.895, RPD > 3.0). Both first derivative reflectance (FDR) and CRDR datasets gave a good prediction of standing biomass (R 2 > 0.857, RPD > 2.5). Although relatively lower prediction accuracy was shown in standing K, it may still be possible to make a quantitative prediction using CRDR and FDR (RPD > 2.2). The semivariogram's parameter “range” of biomass was longer (58.7 m) than the ranges of the other parameters (10.6–17.4 m), suggesting that biomass values influenced neighbouring values of biomass over greater distances than the other pasture parameters (masses of N, P, K, and S).

Differences in spectrum reflectance and physiological indices among three typical semi‐natural grasslands in Japan

AbstractTo clarify the utility of spectrum reflection from plant canopy surfaces for distinguishing grassland vegetation, we tested a portable hyper‐spectrometer in three typical Japanese semi‐natural grasslands: Zoysia‐dominated (Z), Pleioblastus‐dominated (P), and mixed species types (Mixed). We calculated the spectrum reflectance, its coefficient of variation (CV) and first derivative reflectance (FDR), and three physiological indices (PRI, photochemical reflectance index; CCI, canopy chlorophyll index; CI, chlorophyll index). The CV of waveform reflectance in the Mixed grassland tended to be higher than those in Z‐ and P‐dominated grasslands. The values of PRI, CI and CCI differed among the three grasslands (P [PRI = −0.027, CCI = 1.469, CI = 0.550] > Mixed [−0.031, 1.421, 0.459] > Z [−0.042, 1.297, 0.428]). Therefore, the CV may be used to distinguish grasslands dominated by a single species from those dominated by multiple species, while the physiological indices may be able to distinguish the grassland type by its dominant species.

Comparison of different semi-empirical algorithms to estimate chlorophyll-a concentration in inland lake water

Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, a comparison of several existing semi-empirical algorithms to determine chlorophyll-a (Chl-a) content was made by applying them to the field spectra and in situ chlorophyll measurements. Results indicated that the first derivative of reflectance was well correlated with Chl-a. The highest correlation between the first derivative and Chl-a was at 680 nm. The two-band model, NIR/red ratio of R710/670, was also an effective predictor of Chl-a concentration. Since the two-band ratios model is a special case of the three-band model developed recently, three-band model in Lake Chagan showed a higher resolution. The new algorithm named reverse continuum removal relies on the reflectance peak at 700 nm whose shape and position depend strongly upon chlorophyll concentration: The depth and area of the peak above a baseline showed a linear relationship to Chl-a concentration. All of the algorithms mentioned proved to be of value and can be used to predict Chl-a concentration. Best results were obtained by using the algorithms of the first derivative, which yielded R2 around 0.74 and RMSE around 6.39 μg/l. The two-band and three-band algorithms were further applied to MERIS when filed spectral were resampled with regard to their center wavelengths. Both algorithms showed an adequate precision, and the differences on the outcome were small with R2=0.70 and 0.71.

Pre-visible Detection of Grub Feeding in Turfgrass using Remote Sensing

Japanese beetle grubs (Popillia japonica Newman) are rootfeeding pests of turfgrass in the Midwest and eastern United States causing millions of dollars of damage annually. To reduce unnecessary pesticide output by applying only where needed, turfgrass managers need a practical, noninvasive method to locate patchy infestations before unsightly damage has occurred. Spectrometer data and multispectral aerial imagery were evaluated for detecting pre-visible symptoms of grub damage in turfgrass, to facilitate sitespecific grub management. Using spectrometer reflectance data, first derivatives of reflectance, narrow-band vegetation indices, and linear combinations of multiple bands/indices, infested turfgrass plots were distinguished from un-infested plots 10 to16 days before visual differences appeared in the year when visual ratings were conducted. Pre-visible symptoms of grub feeding were not detected using the position of the red edge or edge of the red edge. Results using multispectral imagery were mixed, with early symptoms detected in only one of two years.

Use of Reflectance Measurements for the Detection of N, P, K, ADF and NDF Contents in Sainfoin Pasture.

The objective of this study was to determine the relationships between nitrogen (N), phosphorus (P), potassium (P), acid detergent fiber (ADF) and neutral detergent fiber (NDF) contents of sainfoin (Onobrychis sativa Lam.) pasture and canopy reflectance. Canopy reflectance measurements were made by using a portable spectroradiometer. An experiment was conducted in the Turkey in May and June in 2007 and 2008. Sainfoin pasture N, P, K, ADF and NDF contents correlated linearly with the reflectance ratios R780/650 (0.61≤ r2 ≤0.80) and first derivatives of the reflectance ratios 760/630 (0.70≤ r2 ≤0.84). Linear equations between each forage variable and reflectance or first derivatives reflectance had high r2 (0.68≤ r2 ≤0.83 and 0.79≤ r2 ≤0.90, respectively) in R780 and R760 wavelengths. In stepwise regression of the reflectance (in 460, 550, 650 and 780 nm wavelengths), the r2 of predicted and measured N, P, K, ADF and NDF contents of sainfoin pasture were (0.85, 0.85, 0.78, 0.81 and 0.74, respectively), in stepwise regression of the first derivatives of reflectance (in 440, 530, 630 and 760 nm wavelengths), the r2 of predicted and measured N, P, K, ADF and NDF contents of sainfoin pasture were (0.87, 0.91, 0.83, 0.93 and 0.86, respectively). Our results suggest that canopy reflectance in blue, green, red and near infrared wavebands with NIR/Red and NDVI ratios can be used for nondestructive prediction of forage quality variables in sainfoin pasture.

Development of Canopy Reflectance Algorithms for Real‐Time Prediction of Bermudagrass Pasture Biomass and Nutritive Values

Timely assessments of forage biomass production and nutritive values during the growing season are important for livestock managers to make decisions for adjusting stocking rate and pasture management. Remote sensing of canopy reflectance may provide a rapid and inexpensive means of estimating pasture nutritive values and biomass. The objective of this study was to determine the relationships between neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP) concentration, biomass, and CP availability of bermudagrass [Cynodon dactylon (L.) Pers.] pastures and canopy reflectance. An experiment was conducted in the 2002 and 2003 growing seasons using three bermudagrass pastures of ‘Midland’, ‘Ozarka’, and an experimental hybrid, 72x12–12. Pasture CP concentration, biomass and CP availability correlated linearly with the reflectance ratios of R605/R515, R915/R975, and R875/R725 (0.44 ≤ r2 ≤ 0.63) as well as with the first derivatives of reflectance with wavebands centered at 545, 935, and 755 nm (0.49 ≤ r2 ≤ 0.68). Linear equations between each forage variable and the ratios or derivatives of reflectance were developed on the basis of data pooled across years, plant genotypes, and sampling dates. Validation of developed equations indicated that the CP concentration, biomass, and CP availability could be predicted by either the ratios or derivatives of the reflectance. Pasture NDF and ADF had lower correlation with canopy reflectance than other measured variables. Our results suggest that two‐narrow‐waveband reflectance ratios or the first derivatives in visible and near‐infrared spectral regions can be used for real‐time and nondestructive prediction of forage productivity and CP content in bermudagrass pastures.

Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central appalachian mountains using hyperion and aviris

Earth Observing 1 (EO-1) Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery were used to predict canopy nitrogen (N) concentration for mixed oak forests of Green Ridge State Forest in Maryland. Nitrogen concentration was estimated for 27 ground plots using leaf samples of the dominant tree species from each plot that were dried, ground and analyzed in the laboratory for foliar N concentration. Foliar N data were composited based on relative species composition to determine overall canopy N concentration for the plot. Hyperion and AVIRIS images were converted to surface reflectance and related to canopy N using partial least squares (PLS) regression of first-derivative reflectance for wavelengths reported in the literature to be associated with N absorption features. The PLS model for Hyperion employed four factors and accounted for 97.8% of the variation in N concentrations and 40.4% of the variation in the spectral data whereas the AVIRIS model used three factors accounting for 84.9% of the variation in N and 72.4% of the variation in the spectral information. In the area of overlap between the AVIRIS and Hyperion images, >70% of the estimates from the two sensors were within 0.25%N of each other, indicating a very close fit between the models generated using data from Hyperion and AVIRIS. This research indicates the applicability of hyperspectral data in general and Hyperion data in particular for mapping canopy nitrogen concentration.

Correlation Analysis Procedures for Canopy Spectral Reflectance Data of Seashore Paspalum under Traffic Stress

Traffic stresses often cause a decline in turfgrass quality. Analysis of spectral reflectance is valuable for assessing turfgrass canopy status. The objectives of this study were to determine correlations of narrow band canopy reflectance and selected reflectance indices with canopy temperature and turf quality for seashore paspalum exposed to wear and wear plus soil compaction traffic stresses, and to evaluate the effects of the first derivative of reflectance and degree of data smoothing (spectral manipulations) on such correlations. `Sea Isle 1' seashore paspalum (Paspalum vaginatum Swartz) was established on a simulated sports field during 1999 and used for this study. Compared to original reflectance, the first derivative of reflectance increased the correlation coefficient (r) of certain wavelengths with canopy temperature and turf quality under both traffic stresses. Among 217 wavelengths tested between 400 and 1100 nm, the peak correlations of the first derivative of reflectance occurred at 661 nm and 664 nm for both canopy temperature and turf quality under wear stress, respectively, while the highest correlations were found at 667 nm and 820 to 869 nm for both variables under wear plus soil compaction. Collectively, the first derivative of reflectance at 667 nm was the optimum position to determine correlation with canopy temperature (r > 0.62) and turf quality (r < -0.72) under both traffic stresses. All correlations were not sensitive to degrees of smoothing of reflectance from 400 to 1100 nm. A ratio of R936/R661 (IR/R, Infrared/red) and R693/759 (stress index) had the strongest correlations with canopy temperature for wear (r = -0.63) and wear plus soil compaction (r = 0.66), respectively; and a ratio of R693/R759 had the strongest correlation with turf quality for both wear (r = -0.89) and wear plus soil compaction (r = -0.82). The results suggested that the first derivative of reflectance could be used to estimate any single wavelength simultaneously correlated with multiple turf canopy variables such as turf quality and canopy temperature, and that the stress index (R693/R759) was also a good indicator of canopy stress status.

Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery

This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR) instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge ( λ RE) and pigment concentrations per unit ground area (e.g., R 2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R 2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m −2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g −1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands ( R 2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and λ RE remained unrelated to pigment concentrations per unit ground area for the coniferous stands. However, the masking process substantially improved the strength of the relationship between λ RE and pigment concentrations per unit leaf mass for the coniferous stands (e.g., for Chl a R 2=0.85**; RMSE of prediction=0.84 mg g −1 [22% of the mean]). Therefore, the study demonstrates that for broad-leaved stands, spectral models can be applied to imaging spectrometer data to quantify forest pigments and LAI with moderate accuracy. For coniferous stands, the use of LIDAR data to remove canopy gap areas from the CASI imagery considerably increases the accuracy of spectral predictive models for quantifying pigment concentrations per unit leaf mass.

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages

Leaf pigment content can provide valuable insight into the physiological performance of leaves. Measurement of spectral reflectance provides a fast, nondestructive method for pigment estimation. A large number of spectral indices have been developed for estimation of leaf pigment content. However, in most cases these indices have been tested for only one or at most a few related species and thus it is not clear whether they can be applied across species with varying leaf structural characteristics. Our objective in this study was to develop spectral indices for prediction of leaf pigment content that are relatively insensitive to species and leaf structure variation and thus could be applied in larger scale remote-sensing studies without extensive calibration. We also quantified the degree of spectral interference between pigments when multiple pigments occur within the same leaf tissue. We found that previously published spectral indices provided relatively poor correlations with leaf chlorophyll content when applied across a wide range of species and plant functional types. Leaf surface reflectance appeared to be the most important factor in this variation. By developing a new spectral index that reduces the effect of differences in leaf surface reflectance, we were able to significantly improve the correlations with chlorophyll content. We also found that an index based on the first derivative of reflectance in the red edge region was insensitive to leaf structural variation. The presence of other pigments did not significantly affect estimation of chlorophyll from spectral reflectance. Previously published carotenoid and anthocyanin indices performed poorly across the whole data set. However, we found that the photochemical reflectance index (PRI, originally developed for estimation of xanthophyll cycle pigment changes) was related to carotenoid/chlorophyll ratios in green leaves. This result has important implications for the interpretation of PRI measured at both large and small scales. Our results demonstrate that spectral indices can be applied across species with widely varying leaf structure without the necessity for extensive calibration for each species. This opens up new possibilities for assessment of vegetation health in heterogeneous natural environments.

Colour classification of coastal waters of the Ebro river plume from spectral reflectances

This paper presents a colour classification of coastal waters from features of spectral reflectance. Experimental data were collected in coastal waters of the Ebro river mouth area during two periods of non-bloom conditions and low suspended load. Examination of the reflectance spectra reveals a few significant shapes corresponding to different apparent colours and specific values of hydrological parameters. A colour classification is proposed. An empirical method and a semi-analytical method are presented in order to retrieve suspended particulate matter concentration, namely SPM, chlorophyll a concentration, chl, sediment refractive index, mr , and absorption coefficient of yellow substance at 440 nm, a y (440). The first method is a direct application of the colour classification. It is based on correlations found for each water type between spectral features of the first derivative of reflectance and SPM, chl, mr , ay (440) values. A second method uses the inversion of a three component reflectance model. Measured reflectance spectra and typical values of mr , deduced from the colour classification, are assimilated in the model. As a result, it appears that estimates of SPM and chl are more accurate by calculating chl from the correlation technique and SPM along with ay (440) from inversion of the reflectance model.

Colour classification of coastal waters of the Ebro river plume from spectral reflectances

This paper presents a colour classification of coastal waters from features of spectral reflectance. Experimental data were collected in coastal waters of the Ebro river mouth area during two periods of non-bloom conditions and low suspended load. Examination of the reflectance spectra reveals a few significant shapes corresponding to different apparent colours and specific values of hydrological parameters. A colour classification is proposed. An empirical method and a semi-analytical method are presented in order to retrieve suspended particulate matter concentration, namely SPM, chlorophyll a concentration, chl, sediment refractive index, mr , and absorption coefficient of yellow substance at 440 nm, a y (440). The first method is a direct application of the colour classification. It is based on correlations found for each water type between spectral features of the first derivative of reflectance and SPM, chl, mr , ay (440) values. A second method uses the inversion of a three component reflectance model. Measured reflectance spectra and typical values of mr , deduced from the colour classification, are assimilated in the model. As a result, it appears that estimates of SPM and chl are more accurate by calculating chl from the correlation technique and SPM along with ay (440) from inversion of the reflectance model.

Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves

The visible/near infrared reflectance properties of leaves from several Eucalyptus species were studied to determine appropriate indices for remote sensing of chlorophyll content. A scatter correction technique was applied to the reflectance spectra to reduce the additive and multiplicative scattering effects of leaf surface and internal structure. This method gave improved calibrations for the estimation of chlorophyll content. Reflectance near 710nm wavelength showed maximum sensitivity to chlorophyll content. Reflectance near 550nm was a less sensitive indicator of chlorophyll content. Among several reflectance indices tested, the ratio (R850-R710)/(R850-R680) performed best, and is proposed as a new index for the remote estimation of chlorophyll content in higher plants. From the first derivative of reflectance, the ratio D1(754)/D1(704) and the wavelength position of the red edge were best correlated to chlorophyll content. The ratio D2(712)/D2(688) from the second derivative of reflectance was also an equally good indicator of chlorophyll content.

Processing of High Spectral Resolution Reflectance Data for the Retrieval of Canopy Water Content Information

This article examines how different processing, in particular different degrees of smoothing, affect the extraction of biophysical information from high spectral resolution data. Spectral reflectance data collected during the EISAC’89 Campaign from an experimental grassland site on Tadham Moor, Somerset, United Kingdom were used for the analysis, together with canopy biomass, chlorophyll, and water content variables. The analysis focuses mainly on the correlation between percentage water content per unit area of canopy and spectral parameters across the two water absorption features, around 940 nm and 1150 nm. Correlation analysis revealed that both the strength of the peak correlation between the first derivative of reflectance and water content and its wavelength position were sensitive to the degree of smoothing for the 1150 nm feature, although this was not the case for the 940 nm water absorption feature. This sensitivity is attributed to the fact that different levels of smoothing emphasize different spectral features across a wavelength region where the first derivative of reflectance and the biophysical variable are generally highly correlated. The effect emphasizes that the exact position of peak correlation between these two variables should be interpreted with caution, especially across spectral regions where the correlation is generally high. A simple reflectance ratio based on the relative depth of the water absorption feature was found to be more closely related to canopy water content and insensitive to the degree of spectral smoothing.