| Winter wheat yield estimation based on the assimilated leaf area index and vegetation temperature condition index |
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| DOI：10.7606/j.issn.1000-7601.2017.06.38 |
| Key Words: winter wheat yield estimation remote sensing information crop growth model leaf area index vegetation temperature condition index |
| Author Name | Affiliation | | ZHANG Shu-yu | Shaanxi Provincial Meteorological Bureau, Xi’an, Shaanxi 710014, China | | SUN Hui-tao | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China | | WANG Peng-xin | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China | | JING Yi-gang | Shaanxi Provincial Meteorological Bureau, Xi’an, Shaanxi 710014, China | | LI Li | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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| Abstract: |
| Recently, the combination of remote sensing and crop growth models using data assimilation methods becomes an important approach for estimating regional crop yields. A wheat yield estimation study was carried out in the Guanzhong Plain of Shaanxi Province, China, in the years from 2008 to 2014 after the reviving of winter wheat, and the leaf area index (LAI) and vegetation temperature condition index (VTCI) were chosen as the key parameters. A particle filter algorithm with the sequential important sampling procedure was applied to assimilate the LAI and VTCI retrieved from MODIS data and those simulated by using the CERES-Wheat model. Winter wheat yield estimation models were developed using the observed LAI and VTCI or the assimilated LAI and VTCI, respectively. The results showed that the assimilated LAIs had good temporal and spatial continuity, and the peak and seasonal trend of the assimilated LAIs were more in line with the winter wheat actual grow and development in the Plain. The assimilated VTCIs were better correlated with precipitation, suggesting the assimilated VTCI was a better indictor for crop water stress of winter wheat. Compared with the yield estimation models using the LAI or VTCI a single variable (the coefficient of determination, R2=0.279 or 0.339), the models based on the double variables had better accuracy (R2=0.402). Compared with the estimation models developed by using the observed LAI and VTCI (R2=0.402), the models developed by using the assimilated ones had better accuracy (R2=0.547), indicating that the yield estimation accuracy was further improved by using the assimilated LAI and VTCI. |
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