目前, 无人机获取的多光谱遥感数据已被广泛应用于农业、 林业、 环境等领域的定量监测中。 然而, 现有的将多光谱遥感数据转换为地表反射率的方法, 仍然存在一定的缺陷, 如需要依赖地面参考板、 无法适应光照条件变化、 得到的结果不准确等, 从而影响了多光谱遥感数据定量化应用的效果。 为了解决该问题, 提出了一种可以利用无人机搭载的多光谱相机, 直接对地表反射率进行测量的新方法。 该方法具有非常强的适应能力, 即使在环境光照强度变化的条件下, 仍然能够得到准确的地表反射率。 其中, 如何利用倾斜状态下的光强传感器获取准确的太阳辐照度, 是需要解决的关键问题。 对此, 提出了一种利用两个或者更多朝向不同方向的光强传感器, 实现太阳直射和散射辐照度测量的新方法。 利用此方法即可将相机记录的数字量化(DN)值直接转换为地表反射率。 为了验证本方法的实际效果, 设计了具体的实验验证方案, 对不同日期不同光照条件下获取的无人机遥感数据进行验证。 实际测试结果表明: 利用该方法, 得到黑、 灰、 白三张参考板的反射率在5个多光谱(蓝、 绿、 红、 红边和近红外)波段中最大的平均绝对误差为3.34%, 其对应的标准差为2.11%; 三张参考板在所有波段中最大的平均绝对误差为2.94%, 其对应的标准差为1.84%。 由此可见, 在光照强度变化的条件下, 利用该方法实现地表反射率的准确测量是可行性的。 该方法极大地简化了无人机遥感数据转换为地表反射率的过程。 对多光谱无人机的设计, 以及无人机遥感数据的定量化应用, 都具有重要的参考价值。

At present, the multispectral remote sensing data obtained by UAV (Unmanned Aerial Vehicle), have been widely used in the quantitative monitoring of agriculture, forest, environment and other fields. However, the existing methods of converting the multispectral remote sensing data into land surface reflectance still have some defects, e.g. relying on reference boards, unable to adapt to varying solar illumination conditions, or the obtained results are inaccurate, etc., so they may affect the quantitative application effect of the remote sensing data. In order to solve this problem, a novel methodology for direct measurement of land surface reflectance with the multispectral camera is proposed in this study. The method has strong adaptability, and it can get accurate land surface reflectance even under the condition of variable illumination. The key problem that needs to be solved is how to use irradiance sensors to obtain accurate solar irradiance in a tilted state. A new method was proposed to separate the direct and scattering irradiance with two or more irradiance sensors oriented towards different directions to achieve this objective. Therefore, the digital numbers (DNs) recorded by the multispectral bands can be converted into accurate reflectance with the measured irradiance results. UAV-based remote sensing images obtained on the different dates under different illumination conditions were used to validate the actual effect through the validation scheme designed in this study. The experimental results showed that the maximum MAE (mean absolute error) and standard deviation of five multispectral bands (i.e. blue, green, red, red edge and near infrared) for all reference panels is 3.34% and 2.11%, respectively; and the maximum MAE and standard deviation of the three reference panels (i.e. black, gray and white) is 2.94% and 1.84%, respectively. Therefore, the proposed method can obtain accurate reflectance results, even under varying solar illumination conditions. It greatly simplifies the reflectance conversion process of UAV-based remote sensing images. The study results are significant to the UAV-based remote sensing system's design and the quantitative application of multispectral remote sensing data.