## 1 Introduction
### Background  
The processing of large datasets that cover wide areas and which need to be acquired by several UAV missions is still a challenging task. As for photogrammetric projects, these types of datasets, composed of various sub-blocks, require a careful assessment of the accuracy of the final products, from both geometric and radiometric points of view. This is even more true when time-series are analyzed; the consistency between data is mandatory in these cases. Regarding geometric issues, the recent evolution of UAVs has provided low-cost systems with direct georeferencing (DG) capability. DG has several advantages: it allows flights in remote areas where access could be difficult or impossible [3], and reduces mission time and costs, since no ground control points (GCPs) need to be installed and measured. Unfortunately, navigation-grade GPS/GNSS receivers (Global Positioning System/Global Navigation Satellite System), such as the one integrated with the Parrot Sequoia sensor [4], are not of sufficient quality in the solution position for georeferencing of images. Some authors investigated this topic [3, 5, 6], obtaining metric errors. GCPs are traditionally suggested for georeferencing purposes. The number and distribution of GCPs have been explored by several authors [7, 8, 9, 10]. Independently from the extent of the surveyed area, they state that a small number of GCPs is useful when they are only needed to perform datum transformation, while a larger number is necessary when camera self-calibration must be performed. For this second aim, their spatial distribution is important too, as ground points must cover the whole area of interest. However, GCPs cannot always be guaranteed in some applications, such as in precision agriculture, where inaccessibility is a frequent condition due to crops' stages of growth. In this case, other ground information can be useful, such as pre-existing orthophotos [11]. Independently from the strategy used for images orientation, the geometric quality of the results must be assessed both in terms of accuracy and consistency. Such analysis can be focused on exterior orientation parameters (EOPs) or on the photogrammetric products, such as dense point clouds or orthophotos. EOPs are traditionally evaluated by using a set of check points (CPs), which are considered during the bundle block adjustment as simple tie points; residuals between the photogrammetrically obtained object coordinates of markers and those preliminarily determined by surveying are then evaluated [12, 13, 14]. Photogrammetric products are assessed using additional information acquired by alternative systems, such as GNSS receivers and total stations [15, 16] or light detection and ranging (LIDAR) [17, 18, 19]. Data quality can also be assessed in terms of consistency. This term means the agreement between different (partially overlapping) datasets acquired at the same time or at different times. Consistency can be assessed on various photogrammetric products, such as point clouds or orthophotos. The mentioned criterion is particularly significant when time-series are processed or when different processing strategies are tested, as in our case. Within this framework, [20] evaluated volumetric changes of a landslide areas using point clouds over a time-series, while [21] assessed the consistency of UAV-derived point clouds in relation to the focal length and target set. While geometry is almost always considered when quality assessment is performed, radiometry is less often investigated but plays a key role in several applications, such as precision agriculture and environmental pollution detection. Regarding radiometry, some critical issues remain unsolved, such as which corrections must be considered and modelled, and several authors recently started to investigate these aspects. [PERSON] et al. [22] studied and assessed a processing methodology for biomass estimation in agriculture with a lightweight UAV spectral camera under varying illumination conditions.