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 - crist1984 AU - Crist, Eric P. AU - Cicone, Richard C. TI - A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap DO - 10.1109/tgrs.1984.350619 T2 - Geoscience and Remote Sensing, IEEE Transactions on PY - 1984 SN - 0196-2892 VL - GE-22 IS - 3 SP - 256-263 AB - In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space. ER - TY - JOUR ID - ferencz2004 AU - Ferencz, C. AU - Bognar, P. AU - Lichtenberger, J. AU - Hamar, D. AU - Tarscai, G. AU - Timar, G. AU - Molnar, G. AU - Pasztor, S. AU - Steinbach, P. AU - Szekely, B. AU - Ferencz, O. E. AU - Ferencz-Arkos, I. TI - Crop yield estimation by satellite remote sensing UR - ://WOS:000224024000004 DO - 10.1080/01431160410001698870 T2 - International Journal of Remote Sensing PY - 2004 DA - Oct SN - 0143-1161 VL - 25 IS - 20 SP - 4113-4149 N1 - ISI Document Delivery No.: 856DL Times Cited: 8 Cited Reference Count: 53 Ferencz, C Bognar, P Lichtenberger, J Hamar, D Tarscai, G Timar, G Molnar, G Pasztor, S Steinbach, P Szekely, B Ferencz, OE Ferencz-Arkos, I Taylor & francis ltd Abingdon AB - Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R(2)=0.75. The county-average yield data showed higher correlation (R(2)=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R(2)=84.6-87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too. KW - high-resolution radiometer KW - near-infrared channels KW - noaa-avhrr KW - cloud KW - detection KW - postlaunch calibration KW - spectral reflectance KW - winter-wheat KW - sensed data KW - leaf-area KW - landsat ER - TY - JOUR ID - friedl1994 AU - Friedl, M. A. AU - Schimel, D. S. AU - Michaelsen, J. AU - Davis, F. W. AU - Walker, H. TI - Estimating grassland biomass and leaf area index using ground and satellite data UR - http://dx.doi.org/10.1080/01431169408954174 DO - 10.1080/01431169408954174 T2 - International Journal of Remote Sensing PY - 1994 DA - 1994/05/10 SN - 0143-1161 VL - 15 IS - 7 SP - 1401-1420 AB - Abstract We compared estimates of regional biomass and LAI for a tallgrass prairie site derived from ground data versus estimates derived from satellite data. Linear regression models were estimated to predict LAI and biomass from Landsat-TM data for imagery acquired on three dates spanning the growing season of 1987 using co-registered TM data and ground measurements of LAl and biomass collected at 27 grassland sites. Mapped terrain variables including burning treatment, land-use, and topographic position were included as indicator variables in the models to acccount for variance in biomass and LAI not captured in the TM data. Our results show important differences in the relationships between Kauth-Thomas greenness (from TM), LAI, biomass and the various terrain variables. In general, site-wide estimates of biomass and LAI derived from ground versus satellite-based data were comparable. However, substantial differences were observed in June. In a number of cases, the regression models exhibited significantly higher explained variance due to the incorporation of terrain variables, suggesting that for areas encompassing heterogeneous landcover the inclusion of categorical terrain data in calibration procedures is a useful technique. ER - TY - JOUR ID - kauth1976 AU - Kauth, R. J. and Thomas, G. S. TI - The tasselled cap - a graphic description of the spectraltemporal development of agricultural crops as seen by Landsat PR - Purdue University, West Lafayette, Indiana T2 - Procs. Symposium on Machine Processing of Remotely Sensed Data PY - 1976 SP - 41-51 ER - TY - JOUR ID - lee2010 AU - Lee, W. S. AU - Alchanatis, V. AU - Yang, C. AU - Hirafuji, M. AU - Moshou, D. AU - Li, C. TI - Sensing technologies for precision specialty crop production UR - ://WOS:000283271800001 DO - 10.1016/j.compag.2010.08.005 T2 - Computers and electronics in agriculture PY - 2010 DA - Oct SN - 0168-1699 VL - 74 IS - 1 SP - 2-33 N1 - ISI Document Delivery No.: 668LQ Times Cited: 11 Cited Reference Count: 367 Lee, W. S. Alchanatis, V. Yang, C. Hirafuji, M. Moshou, D. Li, C. Elsevier sci ltd Oxford AB - With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed. (C) 2010 Elsevier B.V. All rights reserved. KW - Specialty crop KW - Precision agriculture KW - Sensing KW - Review KW - on-the-go KW - infrared reflectance spectroscopy KW - soil-moisture content KW - airborne hyperspectral imagery KW - grain-sorghum yield KW - leaf-area index KW - quickbird satellite imagery KW - difference vegetation index KW - tree canopy KW - characteristics KW - compaction profile sensor ER - TY - JOUR ID - thenkabail2002 AU - Thenkabail, P. S. AU - Smith, R. B. AU - De Pauw, E. TI - Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization T2 - Photogrammetric Engineering And Remote Sensing PY - 2002 VL - 68 IS - 6 SP - 607-621 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 -