Development of a Radiometric Calibration Method for Multispectral Images of Croplands Obtained with a Remote-Controlled Aerial System.

A remote sensing (RS) platform consisting of a remote-controlled aerial vehicle (RAV) can be used to monitor crop, environmental conditions, and productivity in agricultural areas. However, the current methods for the calibration of RAV-acquired images are cumbersome. Thus, a calibration method must be incorporated into RAV RS systems for practical and advanced applications. Here, we aimed to develop a standalone RAV RS-based calibration system without the need for calibration tarpaulins (tarps) by quantifying the sensor responses of a multispectral camera, which varies with light intensities. To develop the standalone RAV-based RS calibration system, we used a quadcopter with four propellers, with a rotor-to-rotor length of 46 cm and height of 25 cm. The quadcopter equipped with a multispectral camera with green, red, and near-infrared filters was used to acquire spectral images for formulating the RAV RS-based standardization system. To perform the calibration study process, libraries of sensor responses were constructed using pseudo-invariant tarps according to the light intensities to determine the relationship equations between the two factors. The calibrated images were then validated using the reflectance measured in crop fields. Finally, we evaluated the outcomes of the formulated RAV RS-based calibration system. The results of this study suggest that the standalone RAV RS system would be helpful in the processing of RAV RS-acquired images.