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Monitoring and Forecasting Winter Wheat Freeze Injury and Yield from Multi-Temporal Remotely Sensed Data



Remote-sensing techniques provide crop growth information economically, rapidly, and objectively on a large scale. Remote sensing has been widely used to monitor crop growth and forecast yield. In this study, three Huanjing satellite (HJ) charge-coupled device (CCD) images of winter wheat at growth-wintering (December 2, 2009; pre-freeze injury), regreening (April 2, 2010; post-freeze injury), and jointing stages (April 23, 2010) were acquired for the Gaocheng area in Hebei Province. According to the change characteristics of the normalized difference vegetation index (NDVI) of field samplings of post-freeze injury, we built a multi-line-progress model between the NDVI difference (0394NDVI) and field samplings, which correspond with field investigation data. The damage levels (uninjured, mild, moderate, and serious) and the growth levels (better, good, bad, and worse) were also specified in the model. As a result, the coefficient of determination (R2) of this model reached 0.6001; 20 sampling points were used to validate the model and R2 reached 0.5255. This study demonstrates the feasibility of using early growth stage model to predict yield and provides a tentative prediction of the yield in the Hebei area using HJ-CCD images of China.



Total Pages: 6
Pages: 255-260


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Volume: 22
Issue: 2
Year: 2015

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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