Daily and ten-day Normalized Difference Vegetation Index( NDVI)of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDV1 profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.
This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The key techniques are described systematically about winter wheat yield estimation system, including automatically extracting wheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI (Normal Division Vegetation Index) profiles were produced from 8-12 times AVHRR data during wheat growth season dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature and greenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which eonsists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system. The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.
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