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 - 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 - 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 -