Assessment of Aphid Infestation in Mustard by Hyperspectral Remote Sensing

Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece

The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.     

Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices

In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.     

Differentiation of oil seed crops by remote sensing based on their spectral reflectance pattern

The feasibility of differentiating four oil-seed crops viz., mustard, toria, yellow sarson and sunflower, based on their spectral reflectance in the visible and near infra red region was studied in a field experiment, The spectral vegetative index profiles, generated during the growth period of different oil seed crops indicated two vegetative growth peaks and a depression between the two peaks, due to the conspicuous yellow colour of flowers, which masked the green leaves. The magnitude of such depression in the spectral vegetation indices viz., ‘Greenness’ and ‘Perpendicular Vegetation Index’ (PVI), were of higher magnitude in yellow sarson. The flowering period parameters viz., flowering time, duration and intensity, deduced from the spectral vegetation indices were found to be beneficial in differentiating different oil-seed crops by remote sensing. A plot of ‘Brightness’ vs. ‘Greenness’ values determined during the growth of the crops formed typical clusters. The cluster representing toria crop was significantly different from the other crops, thereby making toria identifiable from others by remote sensing.     

Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST)

Land cover changes associated with urbanisation modify microclimate, leading to urban heat islands, whereby cities are warmer than the surrounding countryside. Understanding the factors causing this phenomenon could help urban areas adapt to climate change and improve living conditions of inhabitants. In this study, land surface temperatures (LST) of Aarhus, a city in the high latitudes, are estimated from the reflectance of a thermal band (TIRS1; Band 10; 10.60–11.19 μm) of Landsat 8 on five dates in the summer months (one in 2015, and four in 2018). Spectral indices, modelled on the normalised difference vegetation index (NDVI), using all combinations of the first seven bands of Landsat 8 are calculated and their relationships with LST, analysed. Land cover characteristics, in terms of the percentages of tree cover, building cover and overall vegetation cover are estimated from airborne LiDAR data, building footprints and 4-band aerial imagery, respectively. The correlations between LST, the spectral indices and land cover are estimated.The difference in mean temperature between the rural and urban parts of Aarhus is up to 3.96 °C, while the difference between the warmer and colder zones (based on the mean and SD of LST) is up to 13.26 °C. The spectral index using the near infrared band (NIR; Band 5; 0.85-0.88 μm) and a short-wave infrared band (SWIR2; Band 7; 2.11–2.29 μm) has the strongest correlations (r: 0.62 to 0.89) with LST for the whole study area. This index is the inverse of normalised burn ratio (NBR), which has been used for mapping burnt areas. Spectral indices using different combinations of the infrared bands have stronger correlations with LST than the more widely used vegetation indices such as NDVI. The percentage of tree cover has a higher negative correlation (Pearson’s r: -0.68 to -0.75) with LST than overall vegetation cover (r: -0.45 to -0.63). Tree cover and building cover (r: 0.53 to 0.71) together explain up to 68 % of the variation in LST. Modification of tree and building cover may therefore have the potential to regulate urban LST.     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery

To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively.     

Predicting Wheat Grain and Biomass Yield Using Canopy Reflectance of Booting Stage

Field experiment was conducted in a sandy loam soil of Indian Agricultural Research Institute, New Delhi during the year 2011–13 to see the effect of irrigation, mulch and nitrogen on canopy spectral reflectance indices and their use in predicting the grain and biomass yield of wheat. The canopy reflectances were measured using a hand held ASD FieldSpec Spectroradiometer at booting stage of wheat. Four spectral reflectance indices (SRIs) viz. RNDVI (Red Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio) and WI (Water Index) were computed using the spectral reflectance data. Out of these four indices, RNDVI, GNDVI and SR were significantly and positively related with the grain and biomass yield of wheat whereas WI was significantly and negatively related with the grain and biomass yield of wheat. Calibration with the second year data showed that among the SRIs, WI could account for respectively, 85 % and 86 % variation in grain and biomass yield of wheat with least RMSE (395 kg ha^?1 (15 %) for grain yield and 1609 kg ha^?1 (20 %) for biomass yield) and highest d index (0.95 for grain yield and 0.91 for biomass yield). Therefore it can be concluded that WI measured at booting stage can be successfully used for prediction of grain and biomass yield of wheat.     

Multi-Index Soil Moisture Estimation from Satellite Earth Observations: Comparative Evaluation of the Topographic Wetness Index (TWI), the Temperature Vegetation Dryness Index (TVDI) and the Improved TVDI (iTVDI)

Soil moisture estimation from satellite earth observation has emerged effectively advantageous due to the high temporal resolution, spatial resolution, coverage, and processing convenience it affords. In this paper, we present a study carried out to estimate soil moisture level at every location within Enugu State Nigeria from satellite earth observation. Comparative analysis of multiple indices for soil moisture estimation was carried out with a view to evaluating the robustness, correlation, appropriateness and accuracy of the indices in estimating the spatial distribution of soil moisture level in Enugu State. Results were correlated and validated with In-Situ soil moisture observations from multi-sample points. To achieve this, the Topographic Wetness Index (TWI), based on digital elevation data, the Temperature Vegetation Dryness Index (TVDI) and an improved TVDI (iTVDI) incorporating air temperature and a Digital Elevation Model (DEM) were calculated from ASTER global DEM and Landsat images. Possible dependencies of the indices on land cover type, topography, and precipitation were explored. In-Situ soil moisture data were used to validate the derived indices. The results showed that there was a positive significant relationship between iTVDI versus TVDI (R = 0.53, P value < 0.05), while in iTVDI versus TWI (R = 0.00, P value > 0.05) and TVDI versus TWI (R = ?0.01, P value > 0.05) no significant relationship existed. There was a strong relationship between iTVDI and topography, land cover type, and precipitation than other indices (TVDI, TWI). In situ measured soil moisture values showed negative significant relationship with TVDI (R = ?0.52, P value < 0.05) and iTVDI (R = ?0.63, P value < 0.05) but not with TWI (R = ?0.10, P value > 0.05). The iTVDI outperformed the other two index; having a stronger relationship with topography, precipitation, land cover classes and soil moisture. It concludes that although iTVDI outperformed other indices (TVDI, TWI) in soil moisture estimation, the decision of which index to apply is dependent on available data, the intent of usage and spatial scale.     

Motivation,development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover

A method is presented for the development of a regional Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper plus (ETM+) spectral greenness index, coherent with a six-dimensional index set, based on a single ETM+ spectral image of a reference landscape. The first three indices of the set are determined by a polar transformation of the first three principal components of the reference image and relate to scene brightness, percent foliage projective cover (FPC) and water related features. The remaining three principal components, of diminishing significance with respect to the reference image, complete the set.The reference landscape, a 2200 km² area containing a mix of cattle pasture, native woodland and forest, is located near Injune in South East Queensland, Australia. The indices developed from the reference image were tested using TM spectral images from 19 regionally dispersed areas in Queensland, representative of dissimilar landscapes containing woody vegetation ranging from tall closed forest to low open woodland. Examples of image transformations and two-dimensional feature space plots are used to demonstrate image interpretations related to the first three indices. Coherent, sensible, interpretations of landscape features in images composed of the first three indices can be made in terms of brightness (red), foliage cover (green) and water (blue). A limited comparison is made with similar existing indices. The proposed greenness index was found to be very strongly related to FPC and insensitive to smoke. A novel Bayesian, bounded space, modelling method, was used to validate the greenness index as a good predictor of FPC. Airborne LiDAR (Light Detection and Ranging) estimates of FPC along transects of the 19 sites provided the training and validation data. Other spectral indices from the set were found to be useful as model covariates that could improve FPC predictions. They act to adjust the greenness/FPC relationship to suit different spectral backgrounds. The inclusion of an external meteorological covariate showed that further improvements to regional-scale predictions of FPC could be gained over those based on spectral indices alone.     

Assessment of Late Blight Induced Diseased Potato Crops: A Case Study for West Bengal District Using Temporal AWiFS and MODIS Data

Remote sensing technology becomes an effective and inexpensive technique for detecting disease in vegetation. In this study, an attempt has been done to discriminate healthy and late blight affected crop using remote sensing based indices such as NDVI and LSWI. NDVI and LSWI spectral profiles between healthy and late blight affected crop shows large difference. Mean difference in reflectance between two acquired dates Jan. 10 and 29, 2009 crop clusters varied from 31.28 % in red band, 7.7 % in NIR band and 6.23 % in SWIR bands in healthy crops while in late blight affected crops it is ?15.5 % in red, 44.4 % in NIR and ?14.61 % in SWIR bands. Negative percentage differences in reflectance indicate reflectance increases from Jan. 10, 2009 to Jan. 29, 2009, while positive difference indicate decrease in reflectance between the two dates. Since potato is an irrigated crop, these differences in reflectance are attributed to prevalent disease at that time. It is found that severely affected areas are Bardhman, Arambag, Bishnupur, Ghatal and Hugli taluka with crop damage areas are 4036.66, 1138.68, 2025.23, 469.15, and 380.08 ha, respectively.     

Soil organic carbon mapping using remote sensing techniques and multivariate regression model

Soil organic carbon (SOC) is an important aspect of soil quality and plays an imperative role in soil productivity in the agriculture ecosystems. The present study was applied to estimate the SOC stock using space-borne satellite data (Landsat 4–5 Thematic Mapper [TM]) and ground verification in the Medinipur Block, Paschim Medinipur District and West Bengal in India. In total, 50 soil samples were collected randomly from the region according to field surveys using a hand-held Global Positioning System (GPS) unit to estimate the surface SOC concentrations in the laboratory. Bare soil index (BSI) and normalized difference vegetation ndex (NDVI) were explored from TM data. The satellite data-derived indices were used to estimate spatial distribution of SOC using multivariate regression model. The regression analysis was performed to determine the relationship between SOC and spectral indices (NDVI and BSI) and compared the observed SOC (field measure) to predict SOC (estimated from satellite images). Goodness fit test was performed to determine the significance of the relationship between observed and predicted SOC at p ≤ 0.05 level. The results of regression analysis between observed SOC and NDVI values showed significant relationship (R² = 0.54; p < 0.0075). A significant statistical relationship (r = ?0.72) was also observed between SOC and BSI. Finally, our model showed nearly 71% of the variance of SOC distribution could be explained by SOC and NDVI values. The information from this study has advanced our understanding of the ongoing ecological development that affects SOC dissemination and might be valuable for effective soil management.     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.     

Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Relationship of Hyperspectral Reflectance Indices with Leaf N and P Concentration,Dry Matter Accumulation and Grain Yield of Wheat

A field experiment was conducted on wheat during rabi season of year 2010–2011 and 2011–2012 at IARI, New Delhi to study the reflectance response of wheat to the nutrient omissions and identify the appropriate indices for assessing the nutrient deficiencies. Treatments comprised omission of N, P, K, S and Zn, 50% omission of N, P, and K, absolute control and optimum dose of nutrition (150–26.4–50–15–3 kg/ha N–P–K–S–Zn). The R² were significant and higher for the hyperspectral indices than the broad band vegetation indices. GMI-I, RI-2 dB and RI-3d, GNDVI, VOGa, VOGb, VOGc, ND₇₀₅, PRI, PSNDc and REIP had higher R² (>0.61) for the leaf N concentration. The hyperspectral indices having highly significant correlation with leaf P concentration were PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR and REIP. Among the indices analysed PSSRc, GMI-I, VOGa, RI-2 dB, RI-3 dB, GNDVI, VOGb, VOGc and ND₇₀₅ had almost a similar degree of relationship with DM accumulation with R² values ranging from 0.70 to 0.73. However, REIP displayed a higher degree of relationship with leaf N concentration, drymatter accumulation and grain yield as indicated by R² of 0.85, 0.81 and 0.95 (P = ≤0.01), respectively. It can be concluded from the study that among the hyperspectral indices REIP had a highly significant relationship with leaf N concentration, DM accumulation and grain yield. However, for leaf P concentration several hyperspectral indices viz PSSRc, GMI-1, ZM, RI-half, VOGa, VOGb, VOGc, mSR had though significant but almost similar R² values.     

Relationship of wheat grain yield with spectral indices

An experiment was conducted during 1996–97 and 1997–98 to study spectral indices and their relationships with grain yield of wheat. Variations of ratio vegetation index (RVI), normalized differences vegetation index (NDVI). difference vegetation index (DVI), transformed vegetation index (TVI), perpendicular vegetation index (PVI) and greenness vegetation index (GVI) have been studied at anthesis stage under different moisture and nitrogen levels. Spectral indices were correlated with crop parameters and it was found that GVI was the best index for yield estimation (r = 0.91 ).     

A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery

Abstract

Extracting built-up areas from remote sensing data like Landsat 8 satellite is a challenge. We have investigated it by proposing a new index referred as built-up land features extraction index (BLFEI). The BLFEI index takes advantage of its simplicity and good separability between the four major component of urban system, namely built-up, barren, vegetation and water. The histogram overlap method and the spectral discrimination index (SDI) are used to study separability. BLFEI index uses the two bands of infrared shortwaves, the red and green bands of the visible spectrum. OLI imagery of Algiers, Algeria, was used to extract built-up areas through BLFEI and some new previously developed built-up indices used for comparison. The water areas are masked out leading to Otsu’s thresholding algorithm to automatically find the optimal value for extracting built-up land from waterless regions. BLFEI, the new index improved the separability by 25% and the accuracy by 5%.     

An investigation into robust spectral indices for leaf chlorophyll estimation

Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690–730 nm).     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.