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Remote sensing techniques have the potential to provide information on agricultural crops quantitatively, instantaneously
and above all nondestructively over large areas. Crop simulation models describe the relationship between physiological processes
in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model
is an important trend for yield estimation and prediction, since remote sensing can provide information on the actual status
of the agricultural crop. In this study, a new model (Rice-SRS) was developed based mainly on ORYZA1 model and modified to
accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR(LAC)-NDVI,
NOAA AVHRR(GAC)-NDVI and radiometric measurements-NDVI. The integration between NOAA AVHRR (LAC) data and simulation model
as applied to Rice-SRS resulted in accurate estimates for rice yield in the Shaoxing area, reduced the estimating error to
1.027%, 0.794% and (−0.787%) for early, single, and late season respectively. Utilizing NDVI data derived from NOAA AVHRR
(GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (−7.43%). Testing the new model
for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less
than 1%.
Project supported by the National Defense Scientific and Technological Committee of China(No. Y97#14-6-2) 相似文献
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