基于无人机多光谱数据的农田土壤水分遥感监测

为了提高农田精准管理效率,基于无人机(Unmanned Aerial Vehicle,UAV)实时获取和传输的遥感数据设计了一种快速监测农田土壤水分的方法:首先,利用UAV飞行采集农田的多光谱数据,在农田选取一个代表性的重点观测区域进行随机样点土壤水分探测;然后,利用垂直干旱指数(Perpendicular Drought Index,PDI),结合样点土壤水分数据快速构建农田土壤水分反演模型,进而获得大范围的农田土壤水分监测结果.并通过6个时相获取的UAV数据和样点土壤水分数据,进行方法实验和模型精度分析,结果表明利用该方法进行农田土壤水分监测的精度较高:6个时相土壤水分反演结果的决定系数R2均在0.8以上,其中5个时相的均方根误差RMSE和系统误差SE值均小于0.1.这证明了基于UAV数据设计的农田土壤水分监测方法的有效性和可行性,可以为大范围农田土壤水分的快速监测提供方法参考.

Monitoring of Farmland Soil Moisture Based on Unmanned Aerial Vehicle Multispectral Data

In order to improve the efficiency of precision farmland management, a method to quickly monitor the soil moisture content of farmland is designed, based on remote sensing data transmitted and obtained in real time with unmanned aerial vehicle (UAV). Firstly, multispectral images of farmland were obtained through UAV and a representative area of farmland was selected to detect the soil moisture content of random samples. Then, the perpendicular drought index (PDI) method, combined with the soil moisture data of samples, were used to construct an inversion model of soil moisture, with which the data of the soil moisture of a wide range of farmland were finally obtained. UAV images and soil moisture data in 6 different periods were used for the inversion accuracy analysis and the method validation. The results showed that this method is of high precision in soil moisture monitoring, with the coefficient of determination (R2) being more than 0.8 in 6 periods and the root mean square error (RMSE) and the systematic error (SE) being both less than 0.1 in 5 periods. This method of soil moisture monitoring based on UAV data had its validity and feasibility proved and could provide reference for rapid monitoring of large-scale farmland soil moisture.