Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops

- A weighted photon spread (WPS) model for RT simulation was developed.
- Sources of error causing the bias of field BRF measurements were identified.
- Well-planned field measurements can effectively characterize the BRF of row crops.
- The choice of sensor FOV and distance was proposed for field BRF measurement.

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 spectro-radiometers 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.