TY - JOUR ID - bannari1995 AU - Bannari, A. AU - Morin, D. AU - Bonn, F. AU - Huete, A. R. TI - A review of vegetation indices UR - http://dx.doi.org/10.1080/02757259509532298 DO - 10.1080/02757259509532298 PR - Taylor & Francis T2 - Remote Sensing Reviews PY - 1995 DA - 1995/08/01 SN - 0275-7257 VL - 13 IS - 1-2 SP - 95-120 AB - Abstract In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial?temporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification. ER - TY - JOUR ID - baret1991 AU - Baret, F. AU - Guyot, G. TI - Potentials and limits of vegetation indices for LAI and APAR assessment UR - http://www.sciencedirect.com/science/article/B6V6V-48BK8FF-1B/2/44379b7ec48e94418f4bd5657f05ec0e T2 - Remote Sensing of Environment PY - 1991 SN - 0034-4257 VL - 35 IS - 2-3 SP - 161-173 ER - TY - ABST ID - bausch1993 AU - Bausch, Walter C. TI - Soil background effects on reflectance-based crop coefficients for corn DO - 10.1016/0034-4257(93)90096-G T2 - Remote Sensing of Environment PY - 1993 VL - 46 SP - 213-222 ER - TY - ABST ID - broge2000 AU - Broge, N. H. AU - Leblanc, E. TI - Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density DO - 10.1016/s0034-4257(00)00197-8 T2 - Remote Sensing of Environment PY - 2000 SN - 0034-4257 VL - 76 IS - 2 SP - 156-172 AB - Hyperspectral reflectance data representing a wide range of canopies were simulated using the combined PROSPECT+SAIL model. The simulations were used to study the stability of recently proposed vegetation indices (VIs) derived from adjacent narrowband spectral reflectance data across the visible (VIS) and near infrared (NIR) region of the electromagnetic spectrum. The prediction power of these indices with respect to green leaf area index (LAI) and canopy chlorophyll density (CCD) was compared, and their sensitivity to canopy architecture, illumination geometry, soil background reflectance, and atmospheric conditions were analyzed. The second soil-adjusted vegetation index (SAVI2) proved to be the best overall choice as a greenness measure. However, it is also shown that the dynamics of the VIs are very different in terms of their sensitivity to the different external factors that affects the spectral reflectance signatures of the various modeled canopies. It is concluded that hyperspectral indices are not necessarily better at predicting LAI and CCD, but that selection of a VI should depend upon (1) which parameter that needs to be estimated (LAI or CCD), (2) the expected range of this parameter, and (3) a priori knowledge of the variation of external parameters affecting the spectral reflectance of the canopy. ER - TY - JOUR ID - buschmann1993 AU - Buschmann, Claus TI - Fernerkundung von Pflanzen - Ausbreitung, Gesundheitszustand und Produktivitfit T2 - Naturwissenschaften PY - 1993 VL - 80 SP - 439-453 ER - TY - JOUR ID - chen1996 AU - Chen, J.M. TI - Evaluation of vegetation indices and a modified simple ratio for boreal applications T2 - Canadian Journal of Remote Sensing PY - 1996 VL - 22 IS - 3 SP - 229-242 AB - A Modified Simple Ratio (MSR) Is proposed for retrieving biophysical parameters of boreal forests using remote sensing data. This vegetation index is formulated based on an evaluation of several two-band vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Indices (SAVI, SAVI1, SAVI2), Weighted Difference Vegetation Index (WDVI), Global Environment Monitoring Index (GEMI), Non-Linear Index (NLI), and Renormalized Difference Vegetation Index (RDVI). MSR is an improved version of RDVI for the purpose of linearizing their relationships with biophysical parameters. All indices were obtained from Landsat-5 TM band 3 (visible) and band 4 (near infrared) images after atmospheric corrections (except for GEMI) and were correlated with ground-based measurements made in 20 Jack Pine (Pinus banksiana) and Black Spruce (Picea mariana) stands during the BOREAS field experiment in 1994. The measurements include Leaf Area Index (LAI) and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the forest canopies. Among these vegetation indices, SR, MSR, and NDVI were found to be best correlated with LAI and FPAR in both spring and summer. All other indices performed poorly. Both NDVI and MSR can be expressed as a function of SR. Measurement errors in remote sensing data often occur due to changes in solar zenith angle, subpixel contamination of clouds, or dissimilar surface features and the variation in the local topography and other environmental factors. These errors generally cause simultaneous increases or decreases in the red and near infrared reflectances, and their effects can be greatly reduced by taking the ratio. All other indices involving mathematical operations other than ratioing could retain the errors or even amplify them. The major problem in using the vegetation indices obtained from red and near infrared bands is the small sensitivity to the overstorey vegetation conditions. Although many of the vegetation indices such as SAVI, SAVI1, and SAVI2 are developed to minimize the effect of the background on retrieving the vegetation information, they also reduce their sensitivity to the changes in the overstorey conditions. KW - Boreal forests KW - FPAR KW - LAI KW - Vegetation index ER - TY - GEN ID - clevers1989 AU - Clevers, J. G. P. W. TI - Application of a weighted infrared-red vegetation index for estimating leaf Area Index by Correcting for Soil Moisture UR - http://www.sciencedirect.com/science/article/pii/003442578990076X DO - 10.1016/0034-4257(89)90076-x T2 - Remote Sensing of Environment PY - 1989 SN - 0034-4257 VL - 29 IS - 1 SP - 25-37 AB - The simplified reflectance model described earlier (Clevers, 1988b) for estimating leaf area index (LAI) is further simplified. In this model the nearinfrared reflectance was corrected for soil background (in particular differences in soil moisture content) and subsequently used for estimating LAI by applying the inverse of a special case of the Mitscherlich function. In the specific situation that the ratio of the reflectances of bare soil in the red and near-infrared is constant for a given soil background, the corrected near-infrared reflectance is now ascertained as a weighted difference of total measured near-infrared and red reflectances (socalled WDVI = weighted difference vegetation index). The validity of this approach is confirmed by simulations with the SAIL model. The above concept was tested at the experimental farm of the Agricultural University Wageningen, by using reflectance factors measured in field trials by means of multispectral aerial photography. The soil type at the experimental farm yielded constant ratios between green, red, and near-infrared reflectances independent of soil moisture content (that is, as a function of time). The difference between measured near-infrared and red reflectances provided a satisfactory approximation of the corrected near-infrared reflectance. The estimation of LAI by this corrected near-infrared reflectance for real field data yielded good results in this study, resulting in the ascertainment of treatment effects with larger precision than by means of the LAI measured in the field by conventional field sampling methods. ER - TY - JOUR ID - dorigo2007 AU - Dorigo, W. A. AU - Zurita-Milla, R. AU - de Wit, A. J. W. AU - Brazile, J. AU - Singh, R. AU - Schaepman, M. E. TI - A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling UR - ://WOS:000246320700008 DO - 10.1016/j.jag.2006.05.003 T2 - International Journal of Applied Earth Observation and Geoinformation PY - 2007 DA - May SN - 0303-2434 VL - 9 IS - 2 SP - 165-193 N1 - ISI Document Delivery No.: 165QP Times Cited: 39 Cited Reference Count: 181 Dorigo, W. A. Zurita-Milla, R. de Wit, A. J. W. Brazile, J. Singh, R. Schaepman, M. E. Conference on Advances in Airborne Electromagnetics and Remote Sensing of Agro-Ecosystems 2004 Wageningen, NETHERLANDS Elsevier science bv Amsterdam AB - During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave. (c) 2006 Elsevier B.V. All rights reserved. KW - data assimilation KW - agroecosystem modeling KW - vegetation indices KW - canopy KW - reflectance modeling KW - biophysical variables KW - biochemical variables KW - parallel processing KW - radiative-transfer models KW - leaf-area index KW - hyperspectral vegetation KW - indexes KW - hydrologic data assimilation KW - multiple linear-regression KW - canopy chlorophyll density KW - ensemble kalman filter KW - bidirectional KW - reflectance KW - soil-moisture KW - crop models ER - TY - JOUR ID - gitelson2003 AU - Gitelson, Anatoly A. AU - Viña, Andrés AU - Arkebauer, Timothy J. AU - Rundquist, Donald C. AU - Keydan, Galina: Leavitt, Bryan TI - Remote estimation of leaf area index and green leaf biomass in maize canopies DO - 10.1029/2002gl016450 PR - AGU T2 - Geophys. Res. Lett. PY - 2003 SN - 0094-8276 VL - 30 IS - 5 SP - 1248 AB - Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to-high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6. KW - Remote sensing, Instruments and techniques. General or miscellaneous ER - TY - GEN ID - goel1994 AU - Goel, Narendra S. AU - Qin, Wenhan TI - Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation UR - http://dx.doi.org/10.1080/02757259409532252 DO - 10.1080/02757259409532252 PR - Taylor & Francis T2 - Remote Sensing Reviews PY - 1994 DA - 1994/10/01 SN - 0275-7257 VL - 10 IS - 4 SP - 309-347 AB - Abstract Vegetation Indices (VIs) are often used to estimate important biophysical parameters, like LAI and Fpar, from vegetation canopy reflectance data. In this study, a three?dimensional model (Diana) is utilized to generate architecturally realistic tree and crop canopies in various development stages and to calculate bidirectional reflectance factors in the principal plane. We investigate the influence of various factors like soil brightness, optical properties of canopy elements, leaf angle distribution, spacing distance between plants and solar and view geometries on relationships between VI and Fpar, LAI and percentage ground cover (GC) in order to determine optimal VI and viewing conditions for the estimation of Fpar and LAI/GC. These simulation studies suggest that: (1) in most cases, Vis using off?nadir reflectances are more informative and useful than those based on nadir reflectances; (2) the optimal VI and sun/view geometries are usually different for inferring different parameters, depending on canopy architecture; (3) LAI can be practically estimated by VI only for homogeneous canopies while GC and Fpar can be inferred even for an inhomogeneous canopy; and (4) when optical properties of vegetation elements vary within a canopy, neither LAI/GC nor Fpar can be estimated by means of VI method with an acceptable accuracy. ER - TY - GEN ID - huete1988 AU - Huete, A. R. TI - A soil-adjusted vegetation index (SAVI) UR - http://www.sciencedirect.com/science/article/pii/003442578890106X DO - 10.1016/0034-4257(88)90106-x T2 - Remote Sensing of Environment PY - 1988 SN - 0034-4257 VL - 25 IS - 3 SP - 295-309 AB - A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal” that can describe dynamic soil-vegetation systems from remotely sensed data ER - TY - JOUR ID - jackson1991 AU - Jackson, Ray D. AU - Huete, Alfredo R. TI - Interpreting vegetation indices T2 - Preventive Veterinary Medicine PY - 1991 VL - 11 IS - 3-4 SP - 185-200 ER - TY - JOUR ID - major1990 AU - Major, D. J. AU - Baret, F. AU - Guyot, G. TI - A ratio vegetation index adjusted for soil brightness UR - http://dx.doi.org/10.1080/01431169008955053 DO - 10.1080/01431169008955053 PR - Taylor & Francis T2 - International Journal of Remote Sensing PY - 1990 DA - 1990-05-01 SN - 0143-1161 VL - 11 IS - 5 SP - 727-740 AB - Abstract Improved parameters for a soil-adjusted vegetation index (SAVI) are derived using SAIL model output for simulated wheat canopy reflectance. The SAVI is much less sensitive than the ratio vegetation index to changes in background caused by soil colour or surface soil moisture content. The parameters are developed to minimize variability due to soil brightness over the practical range of solar elevations, lear angle distributions (LAD) and leaf area indices (LAI). The parameters are added to the near-infrared (NIR) and red reflectances before calculating the NIR/red ratio. Three new versions of the SAVI are developed based on the theoretical consideration of the effects of wet and dry soils. All three are superior to the NIR/red ratio, the perpendicular vegetation index and a soil-adjusted vegetation index. Our simplest version requires the addition of a parameter to the red reflectance. The second and third versions require an iterative procedure to find the best parameters that are then added to the red or the NIR and red reflectances. In general, SAVI adjustment parameters required to remove soil-brightness effects decrease as factors that obscure the soil-surface increase. Low solar elevation and high LAI result in increasingly negative values for the parameters. The NIR/red ratio is the vegetation index least influenced by soil brightness at LAI greater than three. Our iterated versions have lowest overall errors but are occasionally subject to higher errors at the combination of low zenith angles and erectophile canopies. The SAVI based on the bare soil reflectance performs best on field data. ER - TY - JOUR ID - richardson1992 AU - Richardson, A. J. AU - Everitt, J. H. TI - Using spectral vegetation indices to estimate rangeland productivity T2 - Geocartogr Int PY - 1992 VL - 1 SP - 63–69 ER - TY - JOUR ID - richardson1977 AU - Richardson, A.J. AU - Wiegand, C.L. TI - Distinguishing vegetation from soil background information T2 - Photogrammetric Engineering and Remote Sensing PY - 1977 VL - 43 IS - 2 SP - 1541-1552 ER - TY - JOUR ID - schlerf2005 AU - Schlerf, M. AU - Atzberger, C. AU - Hill, J. TI - Remote sensing of forest biophysical variables using HyMap imaging spectrometer data T2 - Remote Sensing of Environment PY - 2005 VL - 95 SP - 177−194 ER - TY - JOUR ID - schmidt2001 AU - Schmidt, H. AU - Karnieli, A. TI - Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: The Makhtesh Ramon Crater (Israel) case study UR - http://dx.doi.org/10.1080/01431160110063779 DO - 10.1080/01431160110063779 T2 - International Journal of Remote Sensing PY - 2001 DA - 2001/01/01 SN - 0143-1161 VL - 22 IS - 17 SP - 3503-3520 AB - The influence of soil background on most vegetation indices (VIs) derived from remotely sensed imagery is a well known phenomenon, and has generated interest in the development of indices that would be less sensitive to this influence. Several such indices have been developed thus far. This paper focuses on testing and comparing the sensitivity of seven intensively used, Landsat Thematic Mapper (TM) derived, VIs (NDVI, SAVI, MSAVI, PVI, WDVI, SAVI 2 and TSAVI) to bare surface variation with almost no vegetation signal. The study was conducted on a terrain composed of a high variety of bare surface materials of which basalt and gypsum are two extremely dark and bright substrates respectively. It was found that SAVI and MSAVI respond to bare surface material very similarly. Such close similarity was also found between PVI and WDVI, and between SAVI 2 and TSAVI. NDVI tends to be overestimated on dark surfaces, while SAVI, PVI and TSAVI show more sensitivity to bright surfaces. Comparison between DeltaVI (the difference between pairs of VIs) and the brightness of the different surface materials showed a high correlation in each case, which underlines the fact that the response of different VIs to bare surface variation is mainly related to the surface brightness. ER - TY - JOUR ID - wu2007 AU - Wu, Jindong AU - Wang, Dong AU - Bauer, Marvin E. TI - Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies UR - http://www.sciencedirect.com/science/article/pii/S0378429007000160 DO - 10.1016/j.fcr.2007.01.003 T2 - Field Crops Research PY - 2007 SN - 0378-4290 VL - 102 IS - 1 SP - 33-42 AB - Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI. KW - Leaf area index KW - QuickBird KW - Remote sensing KW - Sensitivity KW - Stability KW - Vegetation index 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 -