Sun/Shade Separation in Optical and Thermal UAV Images for Assessing the Impact of Agricultural Practices.

Unmanned aerial vehicles (UAVs) provide images at decametric spatial resolutions. Their flexibility, efficiency, and low cost make it possible to apply UAV remote sensing to multisensor data acquisition. In this frame, the present study aims at employing RGB UAV images (at a 3 cm resolution) and multispectral images (at a 16 cm resolution) with related vegetation indices (VIs) for mapping surfaces according to their illumination. The aim is to map land cover in order to access temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. The method, which is based on a linear discriminant analysis, is validated at different periods during the phenological cycle of the crops in place. A model based on a given date is evaluated, as well as the use of a generic model. The method provides a good capacity of separation between four classes: vegetation, no-vegetation, shade, and sun (average kappa of 0.93). The effects of agricultural practices on two adjacent plots of maize respectively submitted to conventional and conservation farming are assessed. The transition from shade to sun increases the brightness temperature by 2.4 °C and reduces the NDVI by 26% for non-vegetated surfaces. The conservation farming plot is found to be 1.9 °C warmer on the 11th of July 2019, with no significant difference between vegetation in the sun or shade. The results also indicate that the NDVI of non-vegetated areas is increased by the presence of crop residues on the conservation agriculture plot and by the effect of shade on the conventional plot which is different for MTVI2. [ABSTRACT FROM AUTHOR]