基于无人机图像混合像元分解模型提高小麦基本苗数的反演精度

及时、精确地获取小麦基本苗数在田块内部的空间差异信息,有利于实施精准变量追施氮肥,实现化肥减量增效。传统的无人机农业遥感仅关注植被与土壤2类特征而忽略混合像元的影响,导致小麦基本苗数反演精度差、可靠性低。为解决上述问题,该研究利用大疆Mini 无人机获取麦田图像,基于不变目标法完成图像的相对辐射标定,并利用像元纯净指数提取植被端元与土壤端元。根据端元光谱特性建立混合像元的线性分解模型,求解混合像元中植被组分的丰度,基于像元统计法计算植被覆盖度,进而建立植被覆盖度与小麦基本苗数地面真值的线性回归模型。该研究方法获得的模型,决定系数R2为0.87,均方根误差为1.97株/m2。而基于传统植被指数法分别利用可见光波段差分植被指数、绿红差分指数、绿红比值指数获取的相应植被覆盖度与小麦基本苗数地面真值的线性回归模型决定系数R2及均方根误差分别为0.79、0.56、0.47及6.06、7.04、4.43株/m2。由此可知,基于混合像元分解模型定量反演小麦基本苗数的方法具有较高的精度,研究成果可为小麦精准减量追施氮肥作业提供数据支持。

Improving the accuracy of wheat basic seedling number inversion based on the mixed pixel decomposition model for remote sensing image of drone

Wheat basic seedling number is one of the most important sources of the total number of wheat ears. In turn, the leading factor can also dominate the wheat yield per unit area. It is an essential prerequisite for the timely and accurate acquisition of the within-field spatial difference information of the wheat basic seedling number. The variable-rate topdressing of nitrogen fertilizer can then be implemented in the manner of precision agriculture. The population density of wheat tillers can often be regulated to realize the fertilizer reduction with a better yield. Unmanned Aerial Vehicle (UAV) remote sensing imagery can be efficiently obtained at the field level in recent years. However, the vegetation and background features can be only processed without considering the influence of mixed pixels of the imagery in the traditional agricultural UAV remote sensing applications. The accuracy and reliability of wheat basic seedling number inversion cannot fully meet the large-scale production in smart agriculture. In this study, the quantitative inversion accuracy of wheat basic seedling numbers was improved using the mixed pixel decomposition model of UAV remote sensing imagery. Firstly, the UAV remote sensing imagery was acquired with a spatial resolution of about 2.5 cm using DJI Mini drone. The relative radiometric calibration was then completed using the invariant target method. Furthermore, the endmembers of vegetation and soil, as well as the mixed pixels were extracted from the reflectance image, which accounted for 2.23%, 0.28%, and 97.49% of the pixels, respectively. The spectral signatures were acquired for the endmembers of vegetation and soil using the reflectance values. Consequently, the decomposition model was established using mixed pixels of UAV remote-sensing images. The linear decomposition was used to divide each mixed pixel into 2 components of vegetation and soil. The abundance data was acquired for each component. The vegetation abundance model was used to calculate the Fractional Vegetation Coverage (FVC) of the experimental field. The proportions of vegetation endmember and abundance were then evaluated over the total area of "1m and 2 rows". Finally, a linear regression model was established between the FVC and the ground truth data of 15 sets of wheat basic seedling numbers. The determination coefficient R2 was 0.87. Besides, the regression model was verified using 3 other ground truth data of wheat basic seedling numbers. The verification results show that the Root Mean Square Error (RMSE) was 1.97 seedlings/m2. The higher inversing accuracy was achieved in this case, compared with the average wheat basic seedling number of 217.442 seedlings/m2 for the wheat field. A comparative experiment was performed on the FVC thematic maps. The traditional vegetation index method was used, including the Visible-band Difference Vegetation Index (VDVI), Green Red Difference Index (GRDI), and Green Red Ratio Index (GRRI). The linear regression models were then established between each FVC of VDVI, GRDI, GRRI, and ground truth data of wheat basic seedling number. The determination coefficient R2 and RMSE were calculated as 0.79, 0.56, 0.47, and 6.06, 7.04 and 4.43 stalks/m2, respectively. Therefore, better performance was achieved in the quantitative inversion model of the wheat basic seedling number using the mixed pixels decomposition of UAV remote sensing images. The findings can provide data support for the precise variable topdressing of nitrogen fertilizer at the tillering stage of wheat.