Li Yuanjin, Yuan Ningge, Luo Shanjun, Yang Kaili, Fang Shenghui, Peng Yi, Gong Yan
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
Front Plant Sci. 2023 Jul 28;14:1188216. doi: 10.3389/fpls.2023.1188216. eCollection 2023.
To stabilize the edible oil market, it is necessary to determine the oil yield in advance, so the accurate and fast technology of estimating rapeseed yield is of great significance in agricultural production activities. Due to the long flowering time of rapeseed and the characteristics of petal color that are obviously different from other crops, the flowering period can be carefully considered in crop classification and yield estimation.
A field experiment was conducted to obtain the unmanned aerial vehicle (UAV) multispectral images. Field measurements consisted of the reflectance of flowers, leaves, and soils at the flowering stage and rapeseed yield at physiological maturity. Moreover, GF-1 and Sentinel-2 satellite images were collected to compare the applicability of yield estimation methods. The abundance of different organs of rapeseed was extracted by the spectral mixture analysis (SMA) technology, which was multiplied by vegetation indices (VIs) respectively to estimate the yield.
For the UAV-scale, the product of VIs and leaf abundance (AbdLF) was closely related to rapeseed yield, which was better than the VIs models for yield estimation, with the coefficient of determination (R2) above 0.78. The yield estimation models of the product of normalized difference yellowness index (NDYI), enhanced vegetation index (EVI) and AbdLF had the highest accuracy, with the coefficients of variation (CVs) below 10%. For the satellite scale, most of the estimation models of the product of VIs and rapeseed AbdLF were also improved compared with the VIs models. The yield estimation models of the product of AbdLF and renormalized difference VI (RDVI) and EVI (RDVI×AbdLF and EVI×AbdLF) had the steady improvement, with CVs below 13.1%. Furthermore, the yield estimation models of the product of AbdLF and normalized difference VI (NDVI), visible atmospherically resistant index (VARI), RDVI, and EVI had consistent performance at both UAV and satellite scales.
The results showed that considering SMA could improve the limitation of using only VIs to retrieve rapeseed yield at the flowering stage. Our results indicate that the abundance of rapeseed leaves can be a potential indicator of yield prediction during the flowering stage.
为稳定食用油市场，有必要提前测定出油率，因此准确快速的油菜籽产量估算技术在农业生产活动中具有重要意义。由于油菜花期长且花瓣颜色特征与其他作物明显不同，在作物分类和产量估算中可充分考虑花期。
进行田间试验以获取无人机多光谱图像。田间测量包括花期花朵、叶片和土壤的反射率以及生理成熟期的油菜籽产量。此外，收集了高分一号（GF-1）和哨兵二号（Sentinel-2）卫星图像以比较产量估算方法的适用性。利用光谱混合分析（SMA）技术提取油菜不同器官的丰度，分别乘以植被指数（VIs）来估算产量。
在无人机尺度上，植被指数与叶片丰度（AbdLF）的乘积与油菜籽产量密切相关，在产量估算方面优于植被指数模型，决定系数（R2）高于0.78。归一化差异黄度指数（NDYI）、增强植被指数（EVI）与AbdLF乘积的产量估算模型精度最高，变异系数（CVs）低于10%。在卫星尺度上，与植被指数模型相比，大多数植被指数与油菜籽AbdLF乘积的估算模型也有所改进。AbdLF与重新归一化差异植被指数（RDVI）和EVI乘积（RDVI×AbdLF和EVI×AbdLF）的产量估算模型有稳步改进，CVs低于13.1%。此外，AbdLF与归一化差异植被指数（NDVI）、可见光大气阻抗指数（VARI）、RDVI和EVI乘积的产量估算模型在无人机和卫星尺度上表现一致。
结果表明，考虑光谱混合分析可以改善仅利用植被指数在花期反演油菜籽产量的局限性。我们的结果表明，油菜叶片丰度可能是花期产量预测的一个潜在指标。
