Estimating the winter wheat harvest index with canopy hyperspectral remote sensing data based on the dynamic fraction of post-anthesis phase biomass accumulation

Zhang, Ningdan; Liu, Xingren; Ren, Jianqiang; Wu, Shangrong; Li, Fangjie

doi:10.1080/01431161.2022.2054297

Cited by 5 publications

(1 citation statement)

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“…Although the model is relatively complex, it involves agronomic parameters, and has become a research hotspot in recent years. Zhang et al [17] used the dynamic fraction of post-anthesis phase biomass accumulation to estimate the winter wheat harvest index, and then used this to obtain an estimated yield. Huang et al [18] developed a data assimilation framework coupling remote sensing information with the WOFOST-PROSAIL model to estimate wheat yields in the North China Plain, and the results show that the method improved crop yields at the regional scale.…”

Section: Introductionmentioning

confidence: 99%

Winter Wheat Yield Estimation with Color Index Fusion Texture Feature

Yang,

Liu,

Yan

et al. 2024

Agriculture

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The rapid and accurate estimation of crop yield is of great importance for large-scale agricultural production and national food security. Using winter wheat as the research object, the effects of color indexes, texture feature and fusion index on yield estimation were investigated based on unmanned aerial vehicle (UAV) high-definition digital images, which can provide a reliable technical means for the high-precision yield estimation of winter wheat. In total, 22 visible color indexes were extracted using UAV high-resolution digital images, and a total of 24 texture features in red, green, and blue bands extracted by ENVI 5.3 were correlated with yield, while color indexes and texture features with high correlation and fusion indexes were selected to establish yield estimation models for flagging, flowering and filling stages using partial least squares regression (PLSR) and random forest (RF). The yield estimation model constructed with color indexes at the flagging and flowering stages, along with texture characteristics and fusion indexes at the filling stage, had the best accuracy, with R2 values of 0.70, 0.71 and 0.76 and RMSE values of 808.95 kg/hm2, 794.77 kg/hm2 and 728.85 kg/hm2, respectively. The accuracy of winter wheat yield estimation using PLSR at the flagging, flowering, and filling stages was better than that of RF winter wheat estimation, and the accuracy of winter wheat yield estimation using the fusion feature index was better than that of color and texture feature indexes; the distribution maps of yield results are in good agreement with those of the actual test fields. Thus, this study can provide a scientific reference for estimating winter wheat yield based on UAV digital images and provide a reference for agricultural farm management.

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Section: Introductionmentioning

confidence: 99%