Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Abstract

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with “factory” radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their “factory” parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

“…Deng et al (2018) suggest that extra efforts are needed to calibrate UAS sensors and improve UAS data postprocessing for the robust use of UAS NDVI datasets. Mamaghani and Salvaggio (2019) proposed a general technique for characterizing and calibrating spectral sensors for UAS. Particular efforts are needed to calibrate multi-lens systems, which have single or multiple camera bodies, to reduce co-registration errors (Jhan et al 2018;Wierzbicki 2018) and radiometric errors (Guo et al 2019;Wierzbicki et al 2018).…”

A Review of Current and Potential Applications of Remote Sensing to Study the Water Status of Horticultural Crops

“…The normalised indices are relatively less influenced, although significant, by the change in illumination conditions which affect the entire spectrum proportionally [29,101]. In this regard, radiometric calibration of the multispectral camera has used a range of stringent to simplified, and vicarious approaches [123,125,171,173,[178][179][180]. Some multispectral cameras are equipped with a downwelling light sensor, which is aimed at correcting for variations in atmospheric transmittance.…”

Inherent Reflectance Variability of Vegetation

“…This model addresses the vignette/row-gradient issues that exist in the raw images and then converts digital counts into radiance. The conversion process is demonstrated in both: [17], and [18].…”