TY - CPAPER ID - apan2003 AU - Apan, Armando AU - Held, Alex AU - Phinn, Stuart AU - Markley, John TI - Formulation and assessment of narrow-band vegetation indices from EO-1 hyperion imagery for discriminating sugarcane disease UR - http://eprints.usq.edu.au/8061/ PR - Spatial Sciences Institute T2 - 2003 Spatial Sciences Institute Conference: Spatial Knowledge Without Boundaries (SSC2003) CY - Canberra, Australia PY - 2003 SP - 1-13 N1 - No evidence of copyright restrictions. AB - The increasing commercial availability of hyperspectral image data promotes growing interests in the development of application-specific narrow-band spectral vegetation indices (SVIs). However, the selection of the optimum SVIs for a particular purpose is not straightforward, due to the wide choice of band combinations and transformations, combined with specific application purposes and conditions. Thus, the aim of this study was to develop an approach for formulating and assessing narrow-band vegetation indices, particularly those from EO-1 Hyperion imagery. The focus of SVI development was for discriminating sugarcane areas affected by 'orange rust' (Puccinia kuehnii) disease in Mackay, Queensland, Australia. After a series of pre-processing and post-atmospheric correction techniques, an empirical-statistical approach to SVI development was designed and implemented. This included the following components: a) selection of sample pixels of diseased and nondiseased areas, b) visual examination of spectral plots to identify bands of maximum spectral separability, c)generation of SVIs, d) use of multiple discriminant function analysis, and e) result interpretation and validation. From the forty existing and newly developed vegetation indices, the output discriminant function (i.e. a linear combination of three indices) attained a classification accuracy of 96.9% for the hold-out sample pixels. The statistical analyses also produced a list of function coefficients and correlation rankings that indicate the predictive potential of each SVI. The newly formulated 'Disease-Water Stress Indices' (DSWI) produced the highest correlations. The approach designed for this study provided a systematic framework in the formulation and assessment of SVIs for sugarcane disease detection. KW - hyperspectral remote sensing, spectral vegetation indices, sugarcane disease, Hyperion ER - TY - JOUR ID - daughtry2000 AU - Daughtry, C. S. T. AU - Walthall, C. L. AU - Kim, M. S. AU - de Colstoun, E. Brown AU - McMurtrey Iii, J. E. TI - Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance UR - http://www.sciencedirect.com/science/article/pii/S0034425700001139 DO - 10.1016/s0034-4257(00)00113-9 T2 - Remote Sensing of Environment PY - 2000 SN - 0034-4257 VL - 74 IS - 2 SP - 229-239 AB - Farmers must balance the competing goals of supplying adequate N for their crops while minimizing N losses to the environment. To characterize the spatial variability of N over large fields, traditional methods (soil testing, plant tissue analysis, and chlorophyll meters) require many point samples. Because of the close link between leaf chlorophyll and leaf N concentration, remote sensing techniques have the potential to evaluate the N variability over large fields quickly. Our objectives were to (1) select wavelengths sensitive to leaf chlorophyll concentration, (2) simulate canopy reflectance using a radiative transfer model, and (3) propose a strategy for detecting leaf chlorophyll status of plants using remotely sensed data. A wide range of leaf chlorophyll levels was established in field-grown corn (Zea mays L.) with the application of 8 N levels: 0%, 12.5%, 25%, 50%, 75%, 100%, 125%, and 150% of the recommended rate. Reflectance and transmittance spectra of fully expanded upper leaves were acquired over the 400-nm to 1,000-nm wavelength range shortly after anthesis with a spectroradiometer and integrating sphere. Broad-band differences in leaf spectra were observed near 550 nm, 715 nm, and >750 nm. Crop canopy reflectance was simulated using the SAIL (Scattering by Arbitrarily Inclined Leaves) canopy reflectance model for a wide range of background reflectances, leaf area indices (LAI), and leaf chlorophyll concentrations. Variations in background reflectance and LAI confounded the detection of the relatively subtle differences in canopy reflectance due to changes in leaf chlorophyll concentration. Spectral vegetation indices that combined near-infrared reflectance and red reflectance (e.g., OSAVI and NIR/Red) minimized contributions of background reflectance, while spectral vegetation indices that combined reflectances of near-infrared and other visible bands (MCARI and NIR/Green) were responsive to both leaf chlorophyll concentrations and background reflectance. Pairs of these spectral vegetation indices plotted together produced isolines of leaf chlorophyll concentrations. The slopes of these isolines were linearly related to leaf chlorophyll concentration. A limited test with measured canopy reflectance and leaf chlorophyll data confirmed these results. The characterization of leaf chlorophyll concentrations at the field scale without the confounding problem of background reflectance and LAI variability holds promise as a valuable aid for decision making in managing N applications. ER - TY - JOUR ID - eitel2007 AU - Eitel, J. U. H. AU - Long, D. S. AU - Gessler, P. E. AU - Smith, A. M. S. TI - Using insitu measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status UR - http://dx.doi.org/10.1080/01431160701422213 DO - 10.1080/01431160701422213 T2 - International Journal of Remote Sensing PY - 2007 DA - 2007/09/20 SN - 0143-1161 VL - 28 IS - 18 SP - 4183-4190 AB - This study assessed whether vegetation indices derived from broadband RapidEye? data containing the red edge region (690?730 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in?situ spectroradiometer data and simulated RapidEye? data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r 2 values ranged from 0.45 to 0.69 (p<0.01) for narrow bands and from 0.35 and 0.77 (p<0.01) for broad bands. For leaf N, r 2 values ranged from 0.41 to 0.68 (p<0.01) for narrow bands and 0.37 to 0.56 (p<0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye? data is useful for predicting wheat N status. ER - TY - JOUR ID - galv_o2005 AU - Galvão, Lênio Soares AU - Formaggio, Antônio Roberto AU - Tisot, Daniela Arnold TI - Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data UR - http://www.sciencedirect.com/science/article/pii/S0034425704003669 DO - 10.1016/j.rse.2004.11.012 T2 - Remote Sensing of Environment PY - 2005 SN - 0034-4257 VL - 94 IS - 4 SP - 523-534 AB - Hyperspectral data acquired by the Hyperion instrument, on board the Earth Observing-1 (EO-1) satellite, were evaluated for the discrimination of five important Brazilian sugarcane varieties (RB72-454, SP80-1816, SP80-1842, SP81-3250, and SP87-365). The radiance values were converted into surface reflectance images by a MODTRAN4-based technique. To discriminate varieties with similar reflectance values, multiple discriminant analysis (MDA) was applied over the data. To obtain an adequate discriminant function, a stepwise method was used to select the best variables among surface reflectance values, ratios of reflectance, and several spectral indices potentially sensitive to changes in chlorophyll content, leaf water, and lignin-cellulose. Results showed that the five Brazilian sugarcane varieties were discriminated using EO-1 Hyperion data. Differences in canopy architecture affected sunlight penetration and reflectance, resulting in a higher reflectance for planophile (e.g., SP81-3250) than erectophile (e.g., SP80-1842) sugarcane plants. The variety SP80-1842 presented lower reflectance values, deeper lignin-cellulose absorption bands at 2103 nm and 2304 nm, shallower leaf liquid water absorption bands at 983 nm and 1205 nm, and lower leaf liquid water content than the other sugarcane varieties. To discriminate this cultivar, a single near-infrared (NIR) band threshold was used. To discriminate the other four sugarcane varieties with similar reflectance values, MDA was used producing a classification accuracy of 87.5% for a hold-out set of pixels. The comparison between the ground truth data and the MDA-derived classification image confirmed the model' capacity to differentiate the varieties accurately. The best results were obtained for the cultivar SP87-365 that presented the lowest spectral variability in the discriminant space. Some misclassified areas were associated with the cultivars SP80-1816 and SP81-3250 that showed the highest spectral variability. KW - Hyperspectral remote sensing KW - Sugarcane varieties KW - Hyperion KW - Discriminant analysis KW - Agriculture KW - Crops ER - TY - JOUR ID - haboudane2004 AU - Haboudane, Driss AU - Miller, John R. AU - Pattey, Elizabeth AU - Zarco-Tejada, Pablo J. AU - Strachan, Ian B. TI - Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture UR - http://www.sciencedirect.com/science/article/pii/S0034425704000264 DO - 10.1016/j.rse.2003.12.013 T2 - Remote Sensing of Environment PY - 2004 SN - 0034-4257 VL - 90 IS - 3 SP - 337-352 AB - A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVI1, MCARI1, MTVI2, and MCARI2) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTVI2) and a modified chlorophyll absorption ratio index (MCARI2), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCARI2 and MTVI2 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r2) being 0.98 for soybean, 0.89 for corn, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively. KW - Hyperspectral KW - Spectral indices KW - Green LAI KW - Prediction algorithms KW - Chlorophyll content KW - Precision agriculture ER - TY - JOUR ID - haboudane2002 AU - Haboudane, Driss AU - Miller, John R. AU - Tremblay, Nicolas AU - Zarco-Tejada, Pablo J. AU - Dextraze, Louise TI - Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture UR - http://www.sciencedirect.com/science/article/pii/S0034425702000184 DO - 10.1016/s0034-4257(02)00018-4 T2 - Remote Sensing of Environment PY - 2002 SN - 0034-4257 VL - 81 IS - 2–3 SP - 416-426 AB - Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L'Acadie, Quebec, Agriculture and Agri-Food Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r2=.81 between estimated and field measured chlorophyll content data. ER - TY - JOUR ID - herrmann2010 AU - Herrmann, I. AU - Karnieli, A. AU - Bonfil, D. J. AU - Cohen, Y. AU - Alchanatis, V. TI - SWIR-based spectral indices for assessing nitrogen content in potato fields UR - http://dx.doi.org/10.1080/01431160903283892 DO - 10.1080/01431160903283892 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 2010 DA - 2010/10/01 SN - 0143-1161 VL - 31 IS - 19 SP - 5127-5143 AB - Nitrogen (N) is an essential element in plant growth and productivity, and N fertilizer is therefore of prime importance in cultivated crops. The amount and timing of N application has economic and environmental implications and is consequently considered to be an important issue in precision agriculture. Spectral indices derived from handheld, airborne and spaceborne spectrometers are used for assessing N content. The majority of these indices are based on indirect indicators, mostly chlorophyll content, which is proven to be physiologically linked to N content. The current research aimed to explore the performance of new N spectral indices dependent upon the shortwave infrared (SWIR) region (1200?2500 nm), and particularly the 1510 nm band because it is related directly to N content. Traditional nitrogen indices (NIs) and four proposed new SWIR-based indices were tested with canopy-level spectral data obtained during two growing seasons in potato experimental plots in the northwest Negev, Israel. Above-ground biomass samples were collected at the same location of the spectral sampling to provide in-situ N content data. The performance of all indices was evaluated by three methods: (1) correlations between the existing and proposed indices and N as well as correlations among the indices themselves; (2) the root mean square error prediction (RMSEP) of the N content; and (3) the indices relative sensitivity (S r) to the N content. The results reveal a firm advantage for the proposed SWIR-based indices in their ability to predict, and in their sensitivity to, N content. The best index is one that combines information from the 1510 and 660 nm bands but no significant differences were found among the new SWIR-based indices. ER - TY - GEN ID - hunt_jr_2011 AU - Hunt Jr., E. Raymond AU - Daughtry, C. S. T. AU - Eitel, Jan U. H. AU - Long, D. S. TI - Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index T2 - Agronomy Journal PY - 2011 VL - 103 SP - 1090-1099 AB - Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose the triangular greenness index (TGI), which calculates the area of a triangle with three vertices: ('r, Rr), ('g, Rg), and ('b, Rb), where ' is the wavelength (nm) and R is the reflectance for bands in red (r), green (g), and blue (b) wavelengths. TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. Generally, indices using the chlorophyll red-edge (710-730 nm) had higher correlations with chlorophyll content compared to TGI. However, with broad bands, correlations between TGI and chlorophyll content were equal or higher than other indices for corn and wheat. Simulations using the Scattering by Arbitrarily Inclined Leaves canopy model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. TGI will enable the use of low-cost sensors, including digital cameras, for nitrogen management by remote sensing. ER - TY - JOUR ID - wu2008 AU - Wu, Chaoyang AU - Niu, Zheng AU - Tang, Quan AU - Huang, Wenjiang TI - Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation UR - http://www.sciencedirect.com/science/article/pii/S0168192308000920 DO - 10.1016/j.agrformet.2008.03.005 T2 - Agricultural and Forest Meteorology PY - 2008 SN - 0168-1923 VL - 148 IS - 8–9 SP - 1230-1241 AB - Leaf chlorophyll content, a good indicator of photosynthesis activity, mutations, stress and nutritional state, is of special significance to precision agriculture. Recent studies have demonstrated the feasibility of retrieval of chlorophyll content from hyperspectral vegetation indices composed by the reflectance of specific bands. In this paper, a set of vegetation indices belonged to three classes (normalized difference vegetation index (NDVI), modified simple ratio (MSR) index and the modified chlorophyll absorption ratio index (MCARI, TCARI) and the integrated forms (MCARI/OSAVI and TCARI/OSAVI)) were tested using the PROSPECT and SAIL models to explore their potentials in chlorophyll content estimation. Different bands combinations were also used to derive the modified vegetation indices. In the sensitivity study, four new formed indices (MSR[705,750], MCARI[705,750], TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750]) were proved to have better linearity with chlorophyll content and resistant to leaf area index (LAI) variations by taking into account the effect of quick saturation at 670 nm with relatively low chlorophyll content. Validation study was also conducted at canopy scale using the ground truth data in the growth duration of winter wheat (chlorophyll content and reflectance data). The results showed that the integrated indices TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750] are most appropriate for chlorophyll estimation with high correlation coefficients R2 of 0.8808 and 0.9406, respectively, because more disturbances such as shadow, soil reflectance and nonphotosynthetic materials are taken into account. The high correlation between the vegetation indices obtained in the developmental stages of wheat and Hyperion data (R2 of 0.6798 and 0.7618 for TCARI/OSAVI[705,750] and MCARI/OSAVI[705,750], respectively) indicated that these two integrated index can be used in practice to estimate the chlorophylls of different types of corns. KW - Vegetation indices KW - Sensitivity KW - Chlorophyll content KW - LAI KW - Validation ER - TY - JOUR ID - zarco-tejada_p_j_sepulcre-cant_2007 AU - Zarco-Tejada P.J. AU - Sepulcre-Cantó, G. TI - REMOTE SENSING OF VEGETATION BIOPHYSICAL PARAMETERS FOR DETECTING STRESS CONDITION AND LAND COVER CHANGES T2 - Estudios de la Zona No Saturada del Suelo PY - 2007 VL - 8 ER - TY - ELEC ID - idb AU - Henrich, V. AU - Krauss, G. AU - Götze, C. AU - Sandow, C. TI - The IndexDatabase UR - https://www.indexdatabase.de/ CY - Bonn PY - 2011 DA - 2011 ER -