Luo Shanjun, Jiang Xueqin, Yang Kaili, Li Yuanjin, Fang Shenghui
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan, China.
Front Plant Sci. 2022 Aug 10;13:958106. doi: 10.3389/fpls.2022.958106. eCollection 2022.
As a promising method, unmanned aerial vehicle (UAV) multispectral remote sensing (RS) has been extensively studied in precision agriculture. However, there are numerous problems to be solved in the data acquisition and processing, which limit its application. In this study, the Micro-MCA12 camera was used to obtain images at different altitudes. The piecewise empirical line (PEL) method suitable for predicting the reflectance of different ground objects was proposed to accurately acquire the reflectance of multi-altitude images by comparing the performance of the conventional methods. Several commonly utilized vegetation indices (VIs) were computed to estimate the rice growth parameters and yield. Then the rice growth monitoring and yield prediction were implemented to verify and evaluate the effects of radiometric calibration methods (RCMs) and UAV flying altitudes (UAV-FAs). The results show that the variation trends of reflectance and VIs are significantly different due to the change in component proportion observed at different altitudes. Except for the milking stage, the reflectance and VIs in other periods fluctuated greatly in the first 100 m and remained stable thereafter. This phenomenon was determined by the field of view of the sensor and the characteristic of the ground object. The selection of an appropriate calibration method was essential as a result of the marked differences in the rice phenotypes estimation accuracy based on different RCMs. There were pronounced differences in the accuracy of rice growth monitoring and yield estimation based on the 50 and 100 m-based variables, and the altitudes above 100 m had no notable effect on the results. This study can provide a reference for the application of UAV RS technology in precision agriculture and the accurate acquisition of crop phenotypes.
作为一种很有前景的方法，无人机（UAV）多光谱遥感（RS）在精准农业中已得到广泛研究。然而，在数据采集和处理方面仍有许多问题有待解决，这限制了其应用。在本研究中，使用Micro-MCA12相机在不同高度获取图像。通过比较传统方法的性能，提出了适用于预测不同地面物体反射率的分段经验线（PEL）方法，以准确获取多高度图像的反射率。计算了几种常用的植被指数（VIs）来估计水稻生长参数和产量。然后进行水稻生长监测和产量预测，以验证和评估辐射定标方法（RCMs）和无人机飞行高度（UAV-FAs）的效果。结果表明，由于在不同高度观察到的成分比例变化，反射率和VIs的变化趋势存在显著差异。除了灌浆期，其他时期的反射率和VIs在前100米内波动较大，此后保持稳定。这种现象是由传感器的视场和地面物体的特性决定的。由于基于不同RCMs估计水稻表型的准确性存在显著差异，因此选择合适的定标方法至关重要。基于50米和100米高度的变量进行水稻生长监测和产量估计的准确性存在明显差异，而100米以上的高度对结果没有显著影响。本研究可为无人机遥感技术在精准农业中的应用以及作物表型的准确获取提供参考。
