# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 1 Introduction  
Multi-temporal consistency of satellite-based Earth Observation (EO) time series is a necessary condition to investigate inter-annual changes and trends of bio-geophysical parameters. Consistency in this context means stability of the estimated geophysical products' uncertainties and this requirement is notably paramount for climate-related applications [1]. Several sources of uncertainties, of both random and systematic nature, may impact such stability requirements, ranging from uncorrected sensor ageing effects, issues in calibration and processing methodologies, or harmonization inconsistencies when merging multi-source satellite data for building long-term time series. One of the potential sources of uncertainties is the instability of the orbital parameters during the satellite lifetime, such as orbit attitude, altitude or inclination. This instability can cause temporal variations of the space-based observation conditions, with resulting spurious trends in the considered time series. A typical example is the precession of the orbital plane, which, if not properly compensated with periodic in-orbit maneuvers, causes a natural drift of the equatorial overpass time, meaning that the same target on-ground is observed under varying illumination conditions throughout the mission. The drift of the overpass time is known to impact the multi-temporal consistency of satellite-based EO products, as demonstrated for the Advanced Very High-ResolutionRadiometer (AVHRR), aboard the National Oceanic and Atmospheric Administration (NOAA) series of polar-orbiting satellites [2; 3; 4; 5; 6; 7; 8; 9]. Within these studies, it has been shown that the orbital decay poses serious challenges in the harmonization of at-sensor radiances [3] and introduces statistically significant trends in the inter-annual analysis of vegetation indices [4; 5; 6; 7], Land and Sea Surface Temperature [4; 8] and active fires [9]. Yet, the cited AVHRR studies mostly focused on investigating the relationship between the temporal changes of the considered geophysical variable and the inter-annual drift (increase) of the sun zenith angle, while less attention was paid to the potential impact of the temporally-varying sun-viewing relative azimuth angle. This parameter is, however, critical in regulating the signal received at the satellite, most notably for polar-orbiting large field-of-view sensors. For those sensors, the azimuthal conditions can strongly vary while moving across-track along the swath. In particular, two azimuthal regions will be sensed: one looking away from the sun, in the backscattering direction, the other looking towards the sun, in the forward scattering direction. Considering the highly anisotropic nature of terrestrial surfaces, such as the strong retro-reflectance peak (hot-spot) in the backscattering direction, time series observed in the two azimuthal conditions might show very different temporal behaviors. The importance of carefully taking into account sun-viewing azimuthal conditions when dealing with time series of satellite-based geophysical products was pointed out in phenological studies over forest sites [10; 11; 12] based on the use of Moderate Resolution Imaging Spectroradiometer (MODIS) data. In particular, in [10] it was stressed that a potential imbalance in backward and forward scattering observations in composite products can eventually induce non-biophysical trends in the time series of vegetation indices, notably when considering the Enhanced Vegetation Index (EVI). The purpose of this paper is to quantitatively evaluate, for the first time, the impact of the orbital drift on the time series of Proba-V [13; 14] surface reflectances at 1 km resolution. The land surface anisotropy is characterized, within this study, using the semi-empirical Ross-Thick Li-Sparse Reciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model [15]. The predicted temporal changes along the mission are firstly evaluated using simulated data over a sample vegetated site.