A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index

Remote sensing estimation of leaf chlorophyll content is of importance to crop nutrition diagnosis and yield assessment, yet the feasibility and stability of such estimation has not been assessed thoroughly for mixed pixels. This study analyses the influence of spectral mixing on leaf chlorophyll content estimation using canopy spectra simulated by the PROSAIL model and the spectral linear mixture concept. It is observed that the accuracy of leaf chlorophyll content estimation would be degraded for mixed pixels using the well-accepted approach of the combination of transformed chlorophyll absorption index (TCARI) and optimized soil-adjusted vegetation index (OSAVI). A two-step method was thus developed for winter wheat chlorophyll content estimation by taking into consideration the fractional vegetation cover using a look-up-table approach. The two methods were validated using ground spectra, airborne hyperspectral data and leaf chlorophyll content measured the same time over experimental winter wheat fields. Using the two-step method, the leaf chlorophyll content of the open canopy was estimated from the airborne hyperspectral imagery with a root mean square error of 5.18 μg cm^?2, which is an improvement of about 8.9% relative to the accuracy obtained using the TCARI/OSAVI ratio directly. This implies that the method proposed in this study has great potential for hyperspectral applications in agricultural management, particularly for applications before crop canopy closure.     

Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content,leaf mass per area,leaf area index and leaf canopy biomass

This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, µg cm_leaf^? 2), sun leaf mass per area (LMA, g_{dry matter} m_leaf^? 2), canopy leaf area index (LAI, m²_leaf m_soil^? 2) and leaf canopy biomass (B_leaf, g_{dry matter} m_soil^? 2). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 µg cm^? 2 for CHL, 9.1 g m^? 2 for LMA, 1.7 m² m^? 2 for LAI and 50.6 g m^? 2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: ND_chl = (ρ₉₂₅ ? ρ₇₁₀)/(ρ₉₂₅ + ρ₇₁₀), for leaf mass per area: ND_LMA = (ρ₂₂₆₀ ? ρ₁₄₉₀)/(ρ₂₂₆₀ + ρ₁₄₉₀), for leaf area index: D_LAI = ρ₁₇₂₅ ? ρ₉₇₀, and for canopy leaf biomass: ND_Bleaf = (ρ₂₁₆₀ ? ρ₁₅₄₀)/(ρ₂₁₆₀ + ρ₁₅₄₀).     

Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion

Optimizing nitrogen (N) fertilization in crop production by in-season measurements of crop N status may improve fertilizer N use efficiency. Hyperspectral measurements may be used to assess crop N status indirectly by estimating leaf and canopy chlorophyll content. This study evaluated the ability of the PROSAIL canopy-level reflectance model to predict leaf chlorophyll content of spring wheat (Triticum aestivum L.) during the growth stages between pre-tillering (Zadoks Growth Stage (ZGS 15)) to booting (ZGS50). Spring wheat was grown under different N fertility rates (0–200 kg N ha^?1) in 2002. Canopy reflectance, leaf chlorophyll content, N content and leaf area index (LAI) values were measured. There was a weakly significant trend for the PROSAIL model to over-estimate LAI and under-estimate leaf chlorophyll content. To compensate for this interdependency by the model, a canopy chlorophyll content parameter (the product of leaf chlorophyll content and LAI) was calculated. The estimation accuracy for canopy chlorophyll content was generally low earlier in the growing season. This failure of the PROSAIL model to estimate leaf and canopy variables could be attributed to model sensitivity to canopy architecture. Earlier in the growing season, full canopy closure was not yet achieved, resulting in a non-homogenous canopy and strong soil background interference. The canopy chlorophyll content parameter was predicted more accurately than leaf chlorophyll content alone at booting (ZGS 45). A strong relationship between canopy chlorophyll content and canopy N content at ZGS 45 indicates that the PROSAIL model may be used as a tool to predict wheat N status from canopy reflectance measurements at booting or later.     

Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements

Fifty-three leaves were randomly sampled on different deciduous tree species, representing a wide range of chlorophyll contents, tree ages, and leaf structural features. Their reflectance was measured between 400 and 800 nm with a 1-nm step, and their chlorophyll content determined by extraction. A larger simulated database (11,583 spectra) was built using the PROSPECT model, in order to test, calibrate, and obtain universal indices, i.e., indices applicable to a wide range of species and leaf structure. To our knowledge, almost all leaf chlorophyll indices published in the literature since 1973 have been tested on both databases. Fourteen canonical types of indices (published ones and new ones) were identified, and their wavelengths calibrated on the simulated database as well as on the experimental database to determine the best wavelengths and, hence, the best performances in chlorophyll estimation for each index types. These indices go from simple reflectance ratios to more sophisticated indices using reflectance first derivatives (using the Savitzky and Golay method). We also tested other nondestructive methods to obtain total chlorophyll concentration: SPAD (Minolta Camera, Osaka, Japan) and neural networks. The validity of the actual PROSPECT model is challenged by our results: Important discordances are found when the indices are calculated with PROSPECT compared to experimental data, especially for some indices and wavelengths. The discordance is even greater when the indices are determined with PROSPECT and applied on the experimental database. A new calibration of PROSPECT is therefore necessary for any study aiming at using simulated spectra to determine or to calibrate indices. The “peak jump” and the multiple-peak feature observed on the first derivative of the reflectances (e.g., in the Red-Edge Inflection Point [REIP] index) has been investigated. It was shown that chlorophyll absorption alone can explain this feature. The peak jump disqualifies the REIP to be a valuable chlorophyll index. A simple modified difference ratio gave the best results among all published indices (cross-validated RMSE=2.1 μg/cm² on the experimental database). After calibration on the experimental database, modified Simple Ratio (mSR) and modified Normalized Difference (mND) indices gave the best performances (RMSECV=1.8 μg/cm² on the experimental database). The new Double Difference (DD) index, although not the best on the experimental database (RMSECV=2.9 μg/cm²), has the best results on the larger simulated database (RMSE=3.7 μg/cm²) and is expected to give good results on larger experimental databases. The best reflectance-based indices give better performances than the current commercial nondestructive device SPAD (RMSECV=4.5 μg/cm²). In this leaf-level study, the best indices are very near from each other, so that complex methods are useless: REIP-like, neural networks, and derivative-based indices are not necessary and give worst results than simpler properly chosen indices. These conclusions will certainly be different for a canopy-level study, where the derivative-based indices may perform significantly better than the other ones.     

Effects of chlorophyll fluorescence on the estimation of microphytobenthos biomass using spectral reflectance indices

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675 nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluorescence emission peaks at 683 nm, counterbalancing the decrease in reflectance centered at 675 nm, thus causing the underestimation of NDVI. The interference of Chl fluorescence was found to be easily identified by a conspicuous double-peak feature in the 670-700 nm region of the second-derivative reflectance spectra. The fluorescence-induced NDVI underestimation was shown to be most pronounced for high surface biomass levels and low incident solar irradiance. Particular aspects of microphytobenthos biofilms, such as the increase in surface Chl fluorescence due the contribution of emission by subsurface layers, and vertical migratory responses by motile microalgae to changes in ambient light, further complicate the effects on biomass estimation using NDVI-like indices. By comparing NDVI with a fluorescence-independent biomass index for a wide range of natural light conditions, it was found that Chl fluorescence interference may cause the underestimation of microalgal biomass to reach over 25%, with errors above 10% being expected for more than half of the measuring occasions. These results indicate that the use of NDVI may compromise the correct assessment of important aspects of microphytobenthos ecology, such as the characterisation of migratory behaviour or the determination of biomass-specific productivity rates, and call for the use of alternative biomass indices, not based on the Chl a red absorption peak.     

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

Leaf chlorophyll content in coniferous forest canopies, a measure of stand condition, is the target of studies and models linking leaf reflectance and transmittance and canopy hyperspectral reflectance imagery. The viability of estimation of needle chlorophyll content from airborne hyperspectral optical data through inversion of linked leaf level and canopy level radiative transfer models is discussed in this paper. This study is focused on five sites of Jack Pine (Pinus banksiana Lamb.) in the Algoma Region (Canada), where field, laboratory and airborne data were collected in 1998 and 1999 campaigns. Airborne hyperspectral CASI data of 72 bands in the visible and near-infrared region and 2 m spatial resolution were collected from 20×20 m study sites of Jack Pine in 2 consecutive years. It was found that needle chlorophyll content could be estimated at the leaf level (r²=0.4) by inversion of the PROSPECT leaf model from needle reflectance and transmittance spectra collected with a special needle carrier apparatus coupled to the Li-Cor 1800 integrating sphere. The Jack Pine forest stands used for this study with LAI>2, and the high spatial resolution hyperspectral reflectance collected, allowed the use of the SPRINT canopy reflectance model coupled to PROSPECT for needle chlorophyll content estimation by model inversion. The optical index R750/R710 was used as the merit function in the numerical inversion to minimize the effect of shadows and LAI variation in the mean canopy reflectance from the 20×20 m plots. Estimates of needle pigment content from airborne hyperspectral reflectance using this linked leaf-canopy model inversion methodology showed an r²=0.4 and RMSE=8.1 μg/cm² when targeting sunlit crown pixels in Jack Pine sites with pigment content ranging between 26.8 and 56.8 μg/cm² (1570-3320 μg/g).     

A shadow identification method using vegetation indices derived from hyperspectral data

Shadows in high-spatial-resolution remote-sensing images become more pronounced. The detection of shadows is an essential requirement for both detailed high-spatial land-cover classification and applications such as three-dimensional (3D) reconstruction of buildings as well as cloud removal. This article presents a method for integrating the photochemical reflectance index (PRI) and Red Edge normalized difference vegetation index (RENDVI) for shadow identification (IPRSI) using high-spatial-resolution airborne hyperspectral data. This method detects shadows by setting thresholds to the PRI and RENDVI to separate shadows from vegetated and non-vegetated areas. The proposed method outperformed the invariant colour spaces model and the object-based method in terms of shadow extraction accuracy. The overall shadow identification accuracy of the IPRSI was 88.97% with an F-score of 90.96 (81.32% with F-score 81.97 for the invariant colour spaces model and 78.02% with F-score 82.07 for the object-based method). The IPRSI is a potential method with the wide application of hyperspectral data in high spatial resolution that is increasingly easier to be obtained with the development of remote-sensing platforms (such as unmanned aerial vehicles (UAVs), small satellites, and airships).     

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.     

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth.In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R², absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R², absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.     

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy).At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation.At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT_canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level (). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT_canopy and was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT_canopy and an overestimation of leaf with relative errors around 19%.Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer 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.     

Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery

Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensing technology. However, combining ecological ground truth information and remote sensing datasets for mapping approaches is complicated by the complexity of ecological datasets. In this study, we present a new approach that uses high spatial resolution hyperspectral datasets to map vegetation units of a semiarid rangeland in Central Namibia. Field vegetation surveys provide the input to the workflow presented in this study. The collected data were classified by hierarchical cluster analysis into seven vegetation units that reflect different ecological states occurring in the study area. Spectral indices covering vegetation and soil characteristics were calculated from hyperspectral remote sensing imagery and used as environmental variables in a constrained ordination by applying redundancy analysis (RDA). The resulting statistical relationships between vegetation data and spectral indices were transferred into images of ordination axes, which were subsequently used in a supervised fuzzy c-means classification approach relying on a k-NN distance metric. Membership images for each vegetation unit as well as a confusion image of the classification result allowed a sound ecological interpretation of the resulting hard classification map. Classification results were validated with two independent reference datasets. For an internal and external validation dataset, overall accuracy reached 98% and 64% with kappa values of 0.98 and 0.53, respectively. Critical steps during the mapping workflow were highlighted and compared with similar mapping approaches.     

Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: application to PROBA/CHRIS data

Hyperspectral/multiangular data allow the retrieval of important vegetation properties at canopy level, such as the Leaf Area Index (LAI) and Leaf Chlorophyll Content. Current methods are based on the relationship between biophysical properties and retrievals from those spectral bands (from the complete hyperspectral/multiangular information) where specific absorption features are present within the considered spectral range. Furthermore, new sensors such as PROBA/CHRIS provide continuous hyperspectral reflectance measurements that can be considered as a continuous function of wavelength. The mathematical analysis of these continuous functions allows a new way of exploiting the relationships between spectral reflectance and biophysical variables by more powerful and stable mathematical tools, in particular for the retrieval of LAI and chlorophyll content. Within the overall context of the European Space Agency (ESA) Spectra Barrax Campaign (SPARC) experiment, an extensive field study was carried out in La Mancha, Spain, simultaneously to the overflight of airborne imaging spectrometers (AHS, HyMAP, ROSIS) and the overpass of CHRIS‐PROBA and MERIS sensors. During the SPARC‐2003 and SPARC‐2004 campaigns, numerous ground measurements were made in the Barrax study area (covering LAI, fCover, leaf chlorophyll a+b, leaf water content and leaf biomass), together with other complementary data, and a total of 17 CHRIS‐PROBA images were acquired. Representative points have been selected from a total of nine different crops, and also retrieved from the CHRIS‐PROBA images acquired within the days of the field campaign. About 250 reflectance spectra from five different observation angles have been analysed. Hyperspectral reflectance spectra have been adjusted by means of third‐degree polynomial functions between 500 nm and 750 nm, and correlations observed between LAI values and the coefficients of these polynomials yielded LAI as a result of the mathematical fitting. On the other hand, the area under the spectral reflectance curves has been calculated in the interval from 600 nm to 700 nm, the region of the red spectral interval where strong absorption features for chlorophyll have been observed, though areas under the curves are also strongly correlated to the chlorophyll content of the crops. Furthermore, a linear relationship between these areas and the chlorophyll content is proposed in this work. This relationship allows the retrieval of leaf chlorophyll by satellite data, based on the spectral information. Both of the proposed methods are almost independent of the observation angles employed. The high number of in situ measurements acquired simultaneously to satellite overpasses, and the broad available range of data, have allowed validation of both methods, with a large number of data and in a statistically consistent manner.     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

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.     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

Remote estimation of chlorophyll content in higher plant leaves

Indices for the non-destructive estimation of chlorophyll content were formulated using various instruments to measure reflectance and absorption spectra in visible and near-infrared ranges, as well as chlorophyll contents from several non-related species from different climatic regions. The proposed new algorithms are simple ratios between percentage reflectance at spectral regions that are highly sensitive (540 to 630nm and around 700nm) and insensitive (nearinfrared) to variations in chlorophyll content: R NIR / R 700 and R NIR / R 550. The developed algorithms predicting leaf chemistry from the leaf optics were validated for nine plant species in the range of chlorophyll content from 0.27 to 62.9mug cm -2. An error of less than 4.2 mugcm -2 in chlorophyll prediction was achieved. The use of green and red (near 700nm) channels increases the sensitivity of NDVI to chlorophyll content by about five-fold.     

Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops

An investigation of the estimation of leaf biochemistry in open tree crop canopies using high-spatial hyperspectral remote sensing imagery is presented. Hyperspectral optical indices related to leaf chlorophyll content were used to test different radiative transfer modelling assumptions in open canopies where crown, soil and shadow components were separately targeted using 1 m spatial resolution ROSIS hyperspectral imagery. Methods for scaling-up of hyperspectral single-ratio indices such as R₇₅₀/R₇₁₀ and combined indices such as MCARI, TCARI and OSAVI were studied to investigate the effects of scene components on indices calculated from pure crown pixels and from aggregated soil, shadow and crown reflectance. Methods were tested on 1-m resolution hyperspectral ROSIS datasets acquired over two olive groves in southern Spain during the HySens 2002 campaign conducted by the German Aerospace Center (DLR). Leaf-level biochemical estimation using 1-m ROSIS data when targeting pure olive tree crowns employed PROSPECT-SAILH radiative transfer simulation. At lower spatial resolution, therefore with significant effects of soil and shadow scene components on the aggregated pixels, a canopy model to account for such scene components had to be used for a more appropriate estimation approach for leaf biochemical concentration. The linked models PROSPECT-SAILH-FLIM improved the estimates of chlorophyll concentration from these open tree canopies, demonstrating that crown-derived relationships between hyperspectral indices and biochemical constituents cannot be readily applied to hyperspectral imagery of lower spatial resolutions due to large soil and shadow effects. Predictive equations built on a MCARI/OSAVI scaled-up index through radiative transfer simulation minimized soil background variations in these open canopies, demonstrating superior performance compared to other single-ratio indices previously shown as good indicators of chlorophyll concentration in closed canopies. The MCARI/OSAVI index was demonstrated to be less affected than TCARI/OSAVI by soil background variations when calculated from the pure crown component even at the typically low LAI orchard and grove canopies.     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).