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Journal of Zhejiang University SCIENCE A 2002 Vol.3 No.4 P.461-466


Rice yield estimation using remote sensing and simulation model

Author(s):  HUANG Jing-feng, TANG Shu-chuan, Ousama Abou-Ismail, WANG Ren-chao

Affiliation(s):  Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   hjf@zju.edu.cn

Key Words:  Rice, Remote sensing, NOAA (National Oceanic and Atmospheric Adiministration) AVHRR (Advanced Very High Resolution Radiometer), Simulation model, LACC (Local Area coverage), GAC (Global Area Coverage)

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HUANG Jing-feng, TANG Shu-chuan, Ousama Abou-Ismail, WANG Ren-chao. Rice yield estimation using remote sensing and simulation model[J]. Journal of Zhejiang University Science A, 2002, 3(4): 461-466.

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publisher="Zhejiang University Press & Springer",

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%T Rice yield estimation using remote sensing and simulation model
%A HUANG Jing-feng
%A TANG Shu-chuan
%A Ousama Abou-Ismail
%A WANG Ren-chao
%J Journal of Zhejiang University SCIENCE A
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%P 461-466
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%D 2002
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2002.0461

T1 - Rice yield estimation using remote sensing and simulation model
A1 - HUANG Jing-feng
A1 - TANG Shu-chuan
A1 - Ousama Abou-Ismail
A1 - WANG Ren-chao
J0 - Journal of Zhejiang University Science A
VL - 3
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SP - 461
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%@ 1869-1951
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2002.0461

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%.

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