Possibilities for the prognostication of the productivity of cereals from multizonal AVHRR, NOAA, and Landsat TM images (by the example of the Kyiv Region)
Heading:
1Lyalko, VI, 1Sakhatsky, AI, 1Khodorovsky, AYa., 1Khodorovsky, AYa., 1Zholobak, GM, 1Buyanova, IYa. 1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine |
Kosm. nauka tehnol. 2002, 8 ;(2-3):249-254 |
Publication Language: Russian |
Abstract: Not available
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Keywords: ecology, remote sensing of the Earth |
References:
1. Bochenek Z. Operational use of NOAA data for crop condition assessment in Poland. In: Proc. of the 19th EARSeL Symposium on Remote Sensing in the 21 st Century, Valladolid, Spain, 31 May— 2 June 1999, 387—392 (2000).
2. Bullok P. R. Operational estimates of Western Canada grain production using NOAA AVHRR LAC data. Canadian Journal of Remote Sensing, 18 (1), 23—25 (1992).
https://doi.org/10.1080/07038992.1992.10855139
https://doi.org/10.1080/07038992.1992.10855139
3. Illera P., Delgado J. A., Fernández Unzueta A., Fernández Manso A. A. Integration of NOAA-AVHRR and meteorological data in a CIS-Application for vegetation monitoring in Castilla y Leon, Spain. In: Proc. of the 19th EARSeL Symposium on Remote Sensing in the 21 st Century, Valladolid, Spain, 31 May— 2 June 1999, 47—54 (2000).
4. Kumar K., and Monteith G. L. Remote sensing of Crop Growth. In: Smith H. (Ed) Plants and the Daylight Spectrum, 133—144 (Academic Press, London, 1981).
5. Prince S. D. A model of regional primary production for use with coarse resolution satellite data. Int. J. of Remote sensing, 6, 1313—1330 (1991).
https://doi.org/10.1080/01431169108929728
https://doi.org/10.1080/01431169108929728
6. Rasmussen M. S. Assessment of millet yield and production in northern Burkina Faso using integrated NDVI from the AVHRR. Int. J. of Remote sensing, 18, 3431—3442 (1992).
https://doi.org/10.1080/01431169208904132
https://doi.org/10.1080/01431169208904132
7. Rasmussen M. S. Operational yield forecast using AVHRR NDVI data reduction of environmental and inter annual variability. Int. J. of Remote sensing, 18, 1059—1077 (1997).
https://doi.org/10.1080/014311697218575
https://doi.org/10.1080/014311697218575
8. Rasmussen M. S. Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information: Part 1. Assessment of net primary production. Int. J. of Remote sensing, 19, 97—117 (1998).
https://doi.org/10.1080/014311698216459
https://doi.org/10.1080/014311698216459
9. Rasmussen M. S. Developing simple, operational, consistent NDVI — vegetation models by applying environmental and climatic information. Part II: Crop yield assessment. Int. J. of Remote sensing, 19, 119—139 (1998).
https://doi.org/10.1080/014311698216468
https://doi.org/10.1080/014311698216468
10. Ruimy M. S., Saugier B. and Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely-sensed data. J. Geophys. Res., 99 (D3), 5263— 5283 (1994).
https://doi.org/10.1029/93JD03221
https://doi.org/10.1029/93JD03221
11. Steven M. D., and Demetriades-Shah T. H. Spectral indices of crop productivity under condition of stress. In: Advances in Digital Image Processing, Int. J. of Remote Sensing Society, 18, 593—601; 3431—3442 (Nottingham, 1987).