Correction of in-flight luminosity variations in multispectral UAS images, using a luminosity sensor and camera pair for improved biomass estimation in precision agriculture

Abstract

Precision farming or agriculture (PA) is a concept where agricultural practices are modulated according to intra-field crop variability. Multispectral sensors have standing use in remote sensing, onboard aircraft and satellites for mapping biomass. With increased miniaturization of sensors, Unmanned Aerial Systems (UAS) become more widely used for multispectral imaging. UAS offer several advantages for PA, such as a relative insensitivity to weather conditions, especially to cloud cover. Most UAS images are acquired in cloudless conditions or with a complete cloud cover to reduce the impact of changing luminosity. This work quantifies the ability to correct luminosity variations on images from UAS flights under varying weather conditions. Measurements were performed with the Parrot Sequoia multispectral camera paired with its Sunshine sensor. Control ground measurements were repeated over two hours on a series of five targets of increasing gray levels. These measurements correlate with corresponding reference spectra from a Spectral Evolution SR-3500 field spectroradiometer. In a second experiment, the camera recorded images every thirty seconds in time-lapse mode, for over an hour, above a reference reflectance target, in order to analyze the evolution of the reflectance over time as a function of the variations of illumination. Finally two different types of UAS carried out several series of flights: a fixed-wing senseFly eBee and an Innovadrone hexacopter rotary wing. This paper presents data analysis with and without the Sunshine sensor correction to quantify the improvement in the quality of reflectance measurements and biomass estimates.