Assessing spectral indices to estimate the fraction of photosynthetically active radiation absorbed by the vegetation canopy

ABSTRACT

The fraction of absorbed photosynthetically active radiation (FPAR) by the vegetation canopy (FPAR_canopy) is an important parameter for vegetation productivity estimation using remote-sensing data. FPAR_canopy is widely estimated using many different spectral vegetation indices (VIs), especially the simple ratio vegetation index (SR) and normalized difference vegetation index (NDVI). However, there have been few studies into which VIs are most suitable for this estimation or into their sensitivities to the leaf area index and the observation geometry of remote-sensing data, which are very important for the accurate estimation of FPAR_canopy based on the plant growth stage and satellite imagery. In this study, nine main VIs calculated from field-measured spectra were evaluated and it was found that the SR and NDVI underestimated and overestimated FPAR_canopy, respectively. It was also found that the enhanced vegetation index produced lesser errors and a higher agreement than other broadband VIs used to estimate FPAR_canopy. Among all the selected VIs, the photochemical reflectance index (PRI) turned out to have the lowest root mean square error of 0.17. The SR produced the highest errors (about 0.37) and lowest index of agreement (about 0.50) compared to the measured values of FPAR_canopy. Except for carotenoid reflectance index (CRI), FPAR_canopy estimated by VIs are evidently sensitive to the leaf area index (LAI), especially for FPAR_canopy (SR), which are also most sensitive to solar zenith angles (SZA). SR, CRI, PRI, and EVI have remarked variations with view zenith angles. Our study shows that FPAR_canopy can be simply and accurately estimated using the most suitable VIs – i.e. EVI and PRI – with broadband and hyperspectral remote-sensing data, respectively, and that the nadir reflectance or nadir bidirectional reflectance distribution function adjusted reflectance should be used to calculate these VIs.     

Spectral estimation of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index or absorption of radiation; however, direct measurement of LAI or light absorption can be tedious and time-consuming. The object of this study was to develop relationships between photosynthetically active radiation (PAR) absorbed by corn (Zea mays L.) canopies and the spectral reflectance of the canopies. Absorption of PAR was measured near solar noon in corn canopies planted in north-south rows at densities of 50,000 and 100,000 plants ha.^?1 Reflectance factor data were acquired with a radiometer with spectral bands similar to the Landsat MSS. Three spectral vegetation indices (ratio of near infrared to red reflectance, normalized difference, and greenness) were associated with more than 95% of the variability in absorbed PAR from planting to silking. The relationships developed between absorbed PAR and the three indices were tested with reflectance factor data acquired from corn canopies planted in 1979–1982 that excluded those canopies from which the equations were developed. Treatments included in these data were two hybrids, four planting densities (25, 50, 75, and 100 thousand plantsha^?1), three soil types (Typic Argiaquoll, Udollic Ochraqualf, and Aeric Ochraqualf), and several planting dates. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR, estimated indirectly from the multispectral reflectance of the canopies, were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index.     

Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies

Accurate measurement of leaf area index (LAI), an important characteristic of plant canopies directly linked to primary production, is essential for monitoring changes in ecosystem C stocks and other ecosystem level fluxes. Direct measurement of LAI is labor intensive, impractical at large scales and does not capture seasonal or annual variations in canopy biomass. The need to monitor canopy related fluxes across landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index (NDVI), tend to saturate at LAI levels > 4 although tropical and temperate forested ecosystems often exceed that threshold. Using two monospecific shrub thickets as model systems, we evaluated the potential of a variety of algorithms specifically developed to improve accuracy of LAI estimates in canopies where LAI exceeds saturation levels for other indices. We also tested the potential of indices developed to detect variations in canopy chlorophyll to estimate LAI because of the direct relationship between total canopy chlorophyll content and LAI. Indices were evaluated based on data from direct (litterfall) and indirect measurements (LAI-2000) of LAI. Relationships between results of direct and indirect ground-sampling techniques were also evaluated. For these two canopies, the indices that showed the highest potential to accurately differentiate LAI values > 4 were derivative indices based on red-edge spectral reflectance. Algorithms intended to improve accuracy at high LAI values in agricultural systems were insensitive when LAI exceeded 4 and offered little or no improvement over NDVI. Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also saturate when LAI exceeds 4. Comparisons between hyperspectral vegetation indices and a saturated LAI value from indirect measurement may overestimate accuracy and sensitivity of some vegetation indices in high LAI communities. We recommend verification of indirect measurements of LAI with direct destructive sampling or litterfall collection, particularly in canopies with high LAI.     

Evaluation of the influence of two operational fraction of absorbed photosynthetically active radiation (FAPAR) products on terrestrial ecosystem productivity modelling

The fraction of photosynthetically active radiation absorbed by vegetation (FAPAR) represents the available light energy for plant productivity and is the key variable influencing photosynthesis, transpiration, and energy balance in most terrestrial vegetation productivity models. With availability of earth observation data from different satellite sensors increasing, a number of FAPAR products are being generated. Several studies have investigated the differences between these products. However, very few studies have investigated how the differences between these products influence the output from ecosystem productivity models that utilise them. This study evaluated the influence of two operational FAPAR products (i.e. the MODIS and CYCLOPES FAPAR products) on the terrestrial vegetation primary productivity predicted by the Carnegie–CASA model across various biomes in the USA. The GPP predicted by the Carnegie–CASA model was compared to GPP measurements from various flux tower sites representing five biomes (i.e. croplands, broadleaf deciduous forests, grassland, needle-leaf evergreen forests, and savanna woodland). With the exception of cropland sites, the two FAPAR products resulted in GPP predictions which were higher than the in situ GPP measurements for the evaluated biomes. However, the CYCLOPES FAPAR product resulted in GPP outputs which were closer (lower RMSE values) to the in situ measurements than the MODIS FAPAR product. The two FAPAR products do not account for the FAPAR absorbed by non-photosynthetic elements of the canopy, which may lead to overestimation of the FAPAR that is actually used in photosynthesis. This could explain the higher GPP values derived using these products when compared to the in situ GPP measurements.     

The potential for regional estimation of absorbed photosynthetically active radiation using new moderate spatial resolution sensors

Under typical field conditions of green vegetation over a dark soil, vegetation indices have been shown to vary in a near-linear fashion with the fraction of absorbed photosynthetically active radiation (APAR). On the basis of model results the AVHRR/2 sensor, which was not designed originally to make biophysical measurements, provides the best match to criteria for producing a near-linear variation of vegetation indices with APAR. The radiance measured by the AVHRR/2 sensor, however, is highly sensitive to atmospheric conditions. In the near future sensors will be launched which have been designed with biophysical imaging as an important criterion. This letter examines the potential of three of these new sensors for estimating APAR. Scattering coefficients calculated for each sensor are shown to provide poor simulation of the pholosyn-thetic action efficiency of photosynthesis. Using the combined visible wavebands to calculate the scattering coefficients, however, produce values that are comparable with AVHRR/2.     

Specularly modified vegetation indices to estimate photosynthetic activity

The hypothesis tested was that some part of the ecosystem-dependent variability of vegetation indices was attributable to the effects of light specularly reflected by leaves. ‘Minus specular’ indices were defined excluding effects of specular light which contains no cellular pigment information

Results, both empirical and theoretical, show that the ‘minus specular’ indices, when compared to the traditional vegetation indices, potentially provide better estimates of the photosynthetic activity within a canopy—and therefore canopy primary production—specifically as a function of Sun and view angles.     

Diurnal patterns of bidirectional vegetation indices for wheat canopies

The perpendicular vegetation index (PVI) and normalized difference vegetation index (NDVI) were calculated from Mark II radiometer RED (0.63-0.69 μm) and NIR (0.76–0.90 μ) bidirectional radiance observations for wheat canopies. Measurements were taken over the plant development interval flag leaf expansion to watery ripeness of the kernels during which the leaf area index (LAI) decreased from 40 to 2-5. Spectral data were taken on four cloudless days five times (09.30, 11.00, 12.30, 14.00 and 15.30 hours (central standard time, C.S.T.) at five view zenith, Zv (0, 15, 30,45 and 60°) and eight view azimuth angles relative to the Sun, A_v (0, 45, 90, 135, 180, 225, 270 and 315°). The PVI was corrected to a common solar irradiance (PVIC) based on simultaneously observed insolation readings.

The PVIC at nadir view (?=0°) increased as (l/cosZ_s) increased on all the measurement days whereas the NDVI changed little as solar zenith angle (Z_s) changed. Thus, the PVIC responded to increasing path length through the canopy, or the number of leaves encountered, as solar zenith angle changed whereas the NDVI, which has saturated by the time an LAI of 2 was achieved, was nonresponsive.

Off-nadir PVIC ratioed to nadir PVIC increased as the view zenith and solar zenith angles increased (reciprocity in Sun and view angles), and as the view azimuth, A angle approached the Sun position (back scattering stronger that forwardscattering). In contrast, the DNVI was very stable for all view and solar angles consistent with saturation in its response. Even though the PVI is subject to bidirectional effects, it contains more useful information about wheat canopies at LAI > 2 than does the NDVI. The NDVI of the plant canopies changed rapidly at low vegetative cover but its bidirectional sensitivity at low LAI was not investigated.     

On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption

This paper examines the rationale for and implications of using a near-infrared band to estimate the absorption of visible light by vegetation canopies. The benefits of using near-infrared observations have already been documented extensively in the literature, notably in the context of applications based on vegetation indices. These include, for instance, a degree of normalization with respect to undesirable perturbing factors. Our intent here is twofold: provide the theoretical basis to justify using measurements outside the main absorption band of vegetation for the purpose of retrieving canopy properties, and uncover the implications of doing so. On the basis of simple radiation transfer considerations, we conclude that near-infrared observations are critical to ensure the accurate retrieval of absorption estimates in the visible domain, and that observations within the absorption band help control the perturbing effect of the soil background.The analytical approach implemented here is conceptually similar to a scale analysis which permits us assessing the most significant contributions to the absorption and scattering processes in the vast majority of geophysical situations. Our final conclusions derived from a series of intermediate steps that need to be performed first. To this end, we illustrate in Section 2 the fact that a suitably-defined one-dimensional radiation transfer model can always be setup to represent accurately the reflected, transmitted and absorbed fraction of vertical fluxes in any vegetation volume at medium spatial resolutions (100 m or lower), and this irrespective of the local variability exhibited by the canopy attributes. This finding is exploited throughout the paper to show that 1) measurements performed in the near-infrared band are needed to ensure a large dynamic range in albedo for dense canopy conditions, by contrast to the visible domain, 2) measurements in the visible domain are effective to remove the contribution due to the background below vegetation for low to intermediate LAI conditions. This is made possible thanks to the soil line concept and the spectral invariance of the interception process, and 3) the estimation of visible light absorption in a canopy on the basis of combinations of spectral bands (as implemented in traditional vegetation indices) hinges on spectral correlations between variables, most notably those controlling the absorbing and scattering properties of the soil and leaves. A series of implications and consequences is drawn from our analysis and, in particular, the suggestion to adopt modern interpretation techniques, superseding the commonly used vegetation index approaches. These advances allow us to improve on current approaches, in particular by lifting some of the hypotheses associated with approaches based on combinations of spectral bands.     

Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

The approach of using primarily satellite observations to estimate ecosystem gross primary production (GPP) without resorting to interpolation of many surface observations has recently shown promising results. Previous work has shown that the remote sensing based greenness and radiation (GR) model can give accurate GPP estimates in crops. However, the feasibility of its application and the model calibration to other ecosystems remain unknown. With the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images and the surface based estimates of photosynthetically active radiation (PAR), we provide an analysis of the GR model for estimating monthly GPP using flux measurements at fifteen sites, representing a wide range of ecosystems with various canopy structures and climate characteristics. Results demonstrate that the GR model can provide better estimates of GPP than that of the temperature and greenness (TG) model for the overall data classified as non-forest (NF), deciduous forest (DF) and evergreen forest (EF) sites. Calibration of the GR model is also conducted and has shown reasonable results for all sites with a root mean square error of 47.18 g C/m²/month. Different coefficients acquired for the three plant functional types indicate that there are shifts of importance among various factors that determine the monthly vegetation GPP. The analysis firstly shows the potential use of the GR model in estimating GPP across biomes while it also points to the needs of further considerations in future operational applications.     

Local-scale heterogeneity of photosynthetically active radiation (PAR), absorbed PAR and net radiation as a function of topography, sky conditions and leaf area index

The local-scale spatial distribution of photosynthetically active radiation (PAR), absorbed PAR (APAR) and net all-wave radiation (Q^?) across the top of a forest canopy was investigated as a function of topography, sky conditions and forest heterogeneity for a forested hilly study site located in south-central Indiana, USA that is part of the FLUXNET and SpecNet networks. The method to estimate spatial variability of radiation components utilized theoretical radiation modeling applied to a topographic model combined with spatial distribution of leaf area index derived from IKONOS imagery and empirical models derived from data collected on a single flux tower. Modeled PAR and Q^? compared consistently well with observations from a single tower with differences typically less than 10%, although clear-sky conditions were simulated more accurately than cloudy conditions. Spatial variability of radiation was found to be very sensitive to topographic relief and could be scaled linearly by mean slope angle. Decreases in optical transmissivity and increases in cloudiness had a strong effect of reducing both the spatial average and standard deviation of radiation components. Spatial variability of APAR was 53% greater than PAR and the characteristic scale of variance was reduced due to finer scale and magnitude of variance of LAI. Clear seasonal patterns existed in both spatial average and standard deviation values with summer producing the largest mean values and weakest spatial variability due to smaller solar zenith angles and seasonality in both optical transmissivity (scaled linearly by specific humidity) and cloudiness. These findings of spatial variability illustrate the need to characterize the complex landscape patterns at flux tower sites, particularly where the goal is to relate flux tower data to satellite imagery.     

Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy of R² = 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of only R² = 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling.     

Estimation of foliar pigment concentration in floating macrophytes using hyperspectral vegetation indices

Foliar pigment concentrations of chlorophylls and cartenoids are important indicators of plant physiological status, photosynthesis rate, and net primary productivity. Although the utility of hyperspectral derived vegetation indices for estimating foliar pigment concentration has been documented for many vegetation types, floating macrophytes have not been assessed despite their ecological importance. This study surveyed 39 wetland species (12 floating macrophytes (FM), 8 grasses/sedges/rushes (GSR), and 19 herbs/wildflowers (HWF)) to determine whether foliar pigment concentrations could be estimated from hyperspectral reflectance. Hyperspectral reflectance of samples was recorded using an ASD FieldSpec3 Max portable spectroradiometer with the plant probe attachment or via a typical laboratory set-up. A semi-empirical relationship was established using either a linear, second-degree polynomial or logarithmic function between 13 candidate vegetation indices and chl-a, chl-b, Car, and chl-a + b pigment concentrations. Vegetation indices R-M, CI-Red, and MTCI were strongly correlated with foliar pigment concentrations using a linear fitting function. Chl-a + b and chl-b concentrations for all samples were reasonably estimated by the R-M index (R² = 0.66 and 0.64), although Chl-a and Car concentration estimates using CI-Red were weaker (R² = 0.63 and 0.51). Regression results indicate that pooled samples to estimate individual foliar pigments were less correlated than when each type of vegetation type was treated separately. For instance, chl-a + b was best estimated by CI-Red for FM (R² = 0.80), MTCI for HWF (R² = 0.77), and R-M for GSR (R² = 0.67). Although floating macrophytes feature unique adaptions to their aquatic environment, their foliar pigment concentrations and spectral signatures were comparable to other wetland vegetation types. Overall, vegetation indices that exploit the red-edge region were a reasonable compromise, having good explanatory power for estimation of foliar pigments across the sampled wetland vegetation types and with CI-Red the best suited index for floating macrophytes.     

Using the red-near infrared spectral to estimate ground cover based on vegetative indices

Rapid and accurate estimation of Ground Cover (GC) at regional and global scales for agricultural management application is only possible by using remote sensing (RS). In this study, two Vegetation Indices (VIs) including the Perpendicular Vegetation Index (PVI) and Normalized Difference Vegetation Index (NDVI) were used for estimating GC. Since the parameters of the bare soil line have an important role in calculating GC based on PVI, this line was extracted based on the red-NIRmin (minimum near infrared) method with different intervals (0.0001, 0.0005, and 0.0010). In addition to traditional statistics such as Root Mean Square Error (RMSE), the sensitivity analysis (S) was also used to sharpen the accuracy of the models' estimations. The results indicated that the PVI-based method, in contrast to the NDVI-based approach, had a better performance in estimating GC of wheat. The highest correlation between the observed GC and the estimated GC based on PVI method was achieved in interval length of 0.0005 (R2 = 0.91) with RMSE equal to 8.82. This regression line (GCEST = -3.47 + 0.96 GCOBS) was not significantly different from the 1:1 line. As expected, the best estimation was achieved when the sensitivity of estimated GC based on PVI (length of the interval: 0.0005) was almost constant and low compared to the other models.     

Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

In previous studies of the universal pattern decomposition method (UPDM), spectral shifts, which are very common in hyperspectral imaging spectrometers, were not taken into account when calculating standard spectral pattern vectors. This study evaluated the effect of spectral shifts on the sensor dependence of the vegetation index based on the UPDM (VIUPD) and 11 other vegetation indices (VIs). Spectral shifts were calculated using Gao's spectrum-matching method. The influences of smoothing techniques (moving average and Savitzky–Golay filters) on the consistency of these VIs were also evaluated and compared. Data from the typical narrowband imaging spectrometers, Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), were chosen for the study. For all VIs, both smoothing and spectral calibration changed the consistency between Hyperion and CHRIS. Spectral calibration had a positive effect on the majority of VIs, whereas smoothing improved the performance of some VIs but decreased the consistency of others. When compared with spectral calibration and Savitzky–Golay smoothing, moving average generated greater variations within the results. Among the smoothing techniques employed, moving average smoothing exhibited a larger distortion of VI sensor dependency than that of Savitzky–Golay smoothing of the same order. VIUPD based on narrowband hyperspectral data was sensitive to spectral operations (spectral calibration and smoothing). For VIUPD, spectral calibration increased its sensor independence, whereas smoothing had a negative effect. After spectral calibration, VIUPD was more sensor independent than any other VI examined in this study.     

Using vegetation reflectance variability for species level classification of hyperspectral data

A study was conducted to investigate whether reflectance data from vegetation in a tropical forest canopy could be used for species level discrimination. Reflectance spectra of 11 species were analysed at the scale of the leaf, branch, tree and species. To enhance separation of species-of-interest spectra from the other spectra in the data, the variation in reflectance values for the species-of-interest were used to create a characteristic spectral shape. With a simple algorithm, the resultant shape-space was used as a data filter that correctly discriminated against 94% of the non-species-of-interest trees.     

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.     

Estimating the characteristics of vegetation canopies with airborne radar measurements

Abstract

Possible use of synthetic aperture radars (SAR) for monitoring agricultural canopies is investigated in this paper. Data have been acquired on the Orgcval watershed during the AGRISCATT'88 campaign. Four radar experiments were carried out with the airborne scattcrometer ERASME (C and X bands, HH and VV polarizations, multi-incidence angles). Simultaneous ground measurements (soil moisture, leaf area index, water content of the canopy) were conducted on 11 wheat fields. Backscattering coefficients of the canopies arc interpreted in the framework of semi-empirical ‘water-cloud’ models. A simple paramctrization of the angular effect of soil roughness is introduced, allowing the simultaneous use of multi-incidence angle radar data. With a unique set of parameters for each radar configuration ‘ frequency and polarization’ the water-cloud model appears to describe adequately the backscattering of all the fields, over the range of incidence angles. It is shown that in this case, attenuation is the dominant effect of the vegetation and an inversion algorithm is proposed for estimating the water content of vegetation. This algorithm requires measurements at two different incidence angles and various combinations of radar configurations are then tested.     

Study of vegetation spectral anomaly behaviour in a porphyry copper mine area based on hyperspectral indices

ABSTRACT

Hyperspectral remote sensing is economical and fast, and it can reveal detailed spectral information of plants. Hence, hyperspectral data are used in this study to analyse the spectral anomaly behaviours of vegetation in porphyry copper mine areas. This analytical method is used to compare the leaf spectra and relative differences among the vegetation indices; then, the correlation coefficients were computed between the soil copper content and vegetation index of Quercus spinosa leaves at both the leaf scale and the canopy scale in the Chundu mine area with different geological backgrounds. Lastly, this study adopts hyperspectral data for the level slicing of vegetation anomalies in the Chundu mine area. The results showed that leaf spectra in the orebody and background area differed greatly, especially in the infrared band (750 nm – 1300 nm); moreover, some indices like the normalized water index (NWI) and normalized difference water index (NDWI) of Quercus spinosa and Lamellosa leaves are sensitive to changes in the geological background. Compared with the canopy, the leaf hyperspectral indices of Quercus spinosa in Chundu can better reflect soil cuprum (Cu) anomaly. In addition, the NWI and NDWI of Quercus spinosa are significantly correlated with the soil Cu content at both the canopy scale and the leaf scale. Consequently, the results of the vegetation anomaly level slicing can adequately reflect the plant anomalies from ore bodies and nearby areas, thereby providing a new ore-finding method for areas with a high degree of vegetation coverage.     

Mapping and discrimination of soya bean and corn crops using spectro-temporal profiles of vegetation indices

The use of remote-sensing technology has been studied as a way to make the monitoring of agricultural crops more efficient, dynamic, and reliable. The use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has proved to be an interesting tool regarding the mapping of large areas, however, some challenges still need to be addressed. One of these is the identification of specific types of crops, especially when they have similar phenologies. The purpose of this study was to perform discrimination and mapping of soya bean and corn crops in the state of Paraná, Brazil, for the 2010/2011 and 2011/2012 crop years. A methodology using spectro-temporal profile information of the crops derived from vegetation indices (VIs), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the wide dynamic range vegetation index (WDRVI) based on MODIS data was appraised. This method generated a series of maps of the respective crops that were later qualitatively or quantitatively appraised. Some of the maps drawn showed a global accuracy rate above 80% and a kappa coefficient (κ) of over 0.7. The data areas showed an average difference of 6% for the cultivation of soya beans, and 11% for corn when compared to official data. The WDRVI and EVI were similar and showed better performance when compared to the NDVI in the assessments made. The results demonstrate that the soya bean crop was better mapped compared to corn, particularly in terms of the size of the crop area. The use of spectro-temporal profiles of the VIs assisted in obtaining important information, enabling better identification of crops from regional scale mapping using the MODIS data.