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[1]BAI Xuejiao,WANG Pengxin,ZHANG Shuyu,et al.Time-Scale Transform of Multi-Temporal Vegetation Temperature Condition Index Based on the Genetic Algorithm[J].Research of Soil and Water Conservation,2018,25(01):190-196.

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Time-Scale Transform of Multi-Temporal Vegetation Temperature Condition Index Based on the Genetic Algorithm

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 25 Number of periods: 2018 01 Page number: 190-196 Column: Public date: 2018-02-28

Title:: Time-Scale Transform of Multi-Temporal Vegetation Temperature Condition Index Based on the Genetic Algorithm

Author(s):: BAI Xuejiao¹, WANG Pengxin¹, ZHANG Shuyu², LI Li¹, JING Yigang², LIU Junming¹; 1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
2. Shaanxi Provincial Meteorological Bureau, Xi’an 710014, China

Keywords:: vegetation temperature condition index; exhaustive attack method; genetic algorithm; Guanzhong Plain; temporal scale transform; drought impact assessment

CLC:: S127;TP79

DOI:: -

Abstract:: Vegetation temperature condition index (VTCI) is a remotely sensed drought indicator, and has been applied to drought monitoring, predication and impact assessment. Multi-temporal VTCIs can cover more drought information related with crop yields, and the drought occurred at different crop growth stages and its degrees lead to diverse yield reduction rate. Therefore, it is of great significance to explore how to integrate useful information from multi-temporal remote sensing data to improve the precision of drought impact assessment. The modeling for temporal scale transformation of VTCIs at the main growth stages of winter wheat in the Guanzhong Plain was carried out by using the normalized combination of factor weight sorting method and entropy method (CAFE), the exhaustive attack method (EA) and the genetic algorithm (GA). The results showed that the weights of impact of droughts at the main growth stages on wheat yields determined by the CAFE had the large differences from the optimal weights obtained by the EA, while the weights determined by the GA were in agreement with the optimal weights. The genetic algorithm was superior to the CAFE in the regression analyses between the weighted VTCIs and the yields, and greatly improved the efficiency and precision of the drought impact. Meanwhile, the GA had the same performance of the GA, but the computation time of the GA was significantly lower than that of the CA. These results indicated that the weight at each growth stage of winter wheat in the Guanzhong Plain determined by the genetic algorithm was quite reasonable, and could more accurately reflect the drought information of the stage, and the GA was more suitable for the study on drought impact assessment.

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