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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops
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Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops

机译:传感器视场和距离对大田作物双向反射系数的野外测量的模拟影响

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It is well established that a natural surface exhibits anisotropic reflectance properties that depend on the characteristics of the surface. Spectral measurements of the bidirectional reflectance factor (BRF) at ground level provide us a method to capture the directional characteristics of the observed surface. Various spectroradiometers with different field of views (FOVs) were used under different mounting conditions to measure crop reflectance. The impact and uncertainty of sensor FOV and distance from the target have rarely been considered. The issue can be compounded with the characteristic reflectance of heterogeneous row crops. Because of the difficulty of accurately obtaining field measurements of crop reflectance under natural environments, a method of computer simulation was proposed to study the impact of sensor FOV and distance on field measured BRFs. A Monte Carlo model was built to combine the photon spread method and the weight reduction concept to develop the weighted photon spread (WPS) model to simulate radiation transfer in architecturally realistic canopies. Comparisons of the Monte Carlo model with both field BRF measurements and the RAMI Online Model Checker (ROMC) showed good agreement. BRFs were then simulated for a range of sensor FOV and distance combinations and compared with the reference values (distance at infinity) for two typical row canopy scenes. Sensors with a finite FOV and distance from the target approximate the reflectance anisotropy and yield average values over FOV. Moreover, the perspective projection of the sensor causes a proportional distortion in the sensor FOV from the ideal directional observations. Though such factors inducing the measurement error exist, it was found that the BRF can be obtained with a tolerable bias on ground level with a proper combination of sensor FOV and distance, except for the hotspot direction and the directions around it Recommendations for the choice of sensor FOV and distance are also made to reduce the bias from the real angular signatures in field BRF measurement for row crops. (C) 2014 Elsevier Inc. All rights reserved.
机译:公认的是,天然表面表现出取决于表面特性的各向异性反射特性。在地面上的双向反射系数(BRF)的光谱测量为我们提供了一种捕获观察到的表面的方向特征的方法。在不同的安装条件下使用具有不同视场(FOV)的各种光谱仪测量作物的反射率。很少考虑传感器视场的影响和不确定性以及距目标的距离。异种行作物的特征反射可能使问题更加复杂。由于难以在自然环境下准确获得农作物反射率的实地测量值,因此提出了一种计算机仿真方法来研究传感器视场和距离对实地测得的BRF的影响。建立了蒙特卡洛模型,以结合光子扩散方法和减重概念来开发加权光子扩散(WPS)模型,以模拟在实际建筑冠层中的辐射传输。蒙特卡洛模型与现场BRF测量和RAMI在线模型检查器(ROMC)的比较显示出很好的一致性。然后针对一系列传感器FOV和距离组合对BRF进行仿真,并将其与两个典型行顶篷场景的参考值(无穷远距离)进行比较。视场距离有限且与目标的距离有限的传感器近似于反射率各向异性并在视场上产生平均值。此外,传感器的透视投影根据理想的方向观察会导致传感器FOV中出现比例失真。尽管存在引起测量误差的因素,但发现BRF可以在地面上具有可容忍的偏差的情况下获得,并且可以适当组合传感器FOV和距离,但热点方向及其周围的方向除外。传感器FOV和距离也可以减少行作物的田间BRF测量中真实角度信号的偏差。 (C)2014 Elsevier Inc.保留所有权利。

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