An experience on complex using of multiband NOAA/AVHRR and Landsat-7 images for a winter wheat yield forecast (for the case of the Kyiv region)
Heading:
1Lyalko, VI, 1Sakhatsky, AI, 1Zholobak, GM, 1Khodorovsky, AYa., 1Grekov, LD, 1Buyanova, IYa., 1Sokolov, VV, 2Yushchenko, MV 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 2State 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. 2003, 9 ;(4):099-103 |
https://doi.org/10.15407/knit2003.04.099 |
Publication Language: Russian |
Abstract: A theoretical basis of the use of NOAA/AVHRR data for a cereal crop forecast is presented. The substantiation of some features of calculation of AVHRR-based normalised difference vegetation index (NDVI) and the use of Landsat-7 images for a winter wheat yield forecast within the limits of the Kyiv region (Ukraine) is given. It is found that a linear regression relationship between the sum of NDVI and productivity is more preferable for performing a forecast at the given stage of studies.
|
Keywords: multiband images, yield forecast, «Landsat-7», «NOAA/AVHRR» |
References:
1. Vasyukhina T. M., Vinnichenko N. K. Determination of species and condition of agricultural crops based on materials of multizone aerial photography. In: Nekotorye rezul'taty issledovanija prirodnyh resursov s pomoshh'ju samoletnyh i poligonnyh sredstv, 64—72 (Gidrometeoizdat, Leningrad, 1980) [in Russian].
2. Kuperman F. M. Plant morphophysiology. Morphophysiological analysis of the stages of organogenesis of different life forms of angiosperm plants, 240 p. (Vysshaya Shkola, Moscow, 1984) [in Russian].
3. Bullok P. R. Operational estimates of Western Canada grain production using NOAA AVHRR LAC data. Can. J. Remote Sensing, 18 (1), 23—28 (1992).
https://doi.org/10.1080/07038992.1992.10855139
https://doi.org/10.1080/07038992.1992.10855139
4. Dabrowska-Zielinska K., Kogan F., Ciolkosz A., et al. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices. Int. J. Remote Sensing, 23 (6), 1109—1123 (2002).
https://doi.org/10.1080/01431160110070744
https://doi.org/10.1080/01431160110070744
5. Illera P., Delgado J. A., Fernández Unzueta A., Fernández Manso A. A. Integration of NOAA-AVHRR and meteorological data in a GIS — Application for vegetation monitoring in Castilla y Leon, Spain. In: Proc. of the 19th EARSeL Symposium on Remote Sensing in the 21st Century, Valladolid, Spain, 31 May—2 June, 1999, 47— 54 (Millpress, Rotterdam, 2000).
6. Kumar K., Monteith G. L. Remote sensing of crop growth. In: Smith H. (Ed.) Plants and Daylight Spectrum, 133—144 (Acad. Press, London, 1981).
7. Prince S. D. A model of regional primary production for use with coarse resolution satellite data. Int. J. Remote Sensing, 6 (7), 1313—1330 (1991).
https://doi.org/10.1080/01431169108929728
https://doi.org/10.1080/01431169108929728
8. Rasmussen M. S. Operational yield forecast using AVHRR NDVI data reduction of environmental and inter annual variability. Int. J. Remote Sensing, 18 (5), 1059—1077 (1997).
https://doi.org/10.1080/014311697218575
https://doi.org/10.1080/014311697218575
9. 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. Remote Sensing, 19 (1), 97—117 (1998).
https://doi.org/10.1080/014311698216459
https://doi.org/10.1080/014311698216459
10. Rasmussen M. S. Developing simple, operational, consistent NDVI — vegetation models by applying environmental and climatic information. Part II: Crop yield assessment. Int. J. Remote Sensing, 19 (1), 119—139 (1998).
https://doi.org/10.1080/014311698216468
https://doi.org/10.1080/014311698216468
11. Ruimy M. S., Saugier B., 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
12. Steven M. D., Demetriades-Shah T. H. Spectral indices of crop productivity under condition of stress. Advances in Digital Image Processing: Int. J. of Remote Sensing Soc., 18, 593—601; 3431—3442 (1987).