## 1 Introduction  
The impact of environmental changes and human activities has increased the need for monitoring the Earth surface. The recognition of this need has stimulated an exponential increase in Earth Observation (EO) satellites, in particular Cubesat nano-satellites for optical remote sensing. Due to their low cost, a great number of Cubesats has been launched over the last six years by a variety of private companies, universities and non-conventional actors in the space industry, enabling unprecedented high spatial and temporal resolution optical images [1]. Planet Labs has been a leading actor in this area. Since 2014, Planet Labs has launched over than 280 Cubesats which image the entire Earth surface every day [2]. Further deployments are planned by Planet Labs and other operators in the upcoming years [1,3]. For instance, SatRevolution is planning to launch 1024 6U Cubesats by 2026, which will offer the possibility of a Real-Time Earth Observation constellation [4]. The capability to acquire multi-temporal data with improved spatial, spectral, radiometric and temporal resolution should enhance our ability to monitor geomorphic processes (e.g. landslides, coastal erosion, Aeolian processes), ground deformation due to earthquakes or landslides, mountain glaciers and ice caps disaster damages, and human activities (e.g., urbanization, infrastructure development, mining operations). These applications require a good knowledge of the geometry of the images to allow for the calculation of accurate Digital Elevation Models (DEM) and for precise georeferencing of the images, ideally with a sub-pixel precision. Recall that a good-quality DEM is generally required for a precise georeferencing, as even nominally nadir-looking images are affected to some degree by stereoscopic effects due to the telescope aperture and its finite elevation above the ground. Good quality DEMs and precise ortho-rectification are therefore paramount to most monitoring applications with optical remote sensing. Nano-satellites offer in principle the potential to generate multi temporal DEMs for any region in the world at any time, but the calculation of DEMs from Cubesat data has been a challenge [5,6]. The traditional approach consists of using images close enough in time so that the topography can be assumed unchanged and with view angles large enough for measurable stereoscopic effects. DEMs can then be extracted based on a Rigorous Sensor Modeling (RSM) of the image geometry, taking into account both the internal (optical distortions, CCD misalignments) and external (telescope position and orientation) parameters. The implementation of such an approach is specific to the particular sensor used in the data acquisition and requires the knowledge of technical characteristics that are rarely provided by the operators. As a standardized substitute to the rigorous sensor model, the geometry of optical images is now commonly represented using a Rational Function Model (RFM) [7,8]. The methods used to generated RFMs are, however, varied and often yield rather imprecise geometric models. The development of methods for RFM optimization and automatic DEM extraction from conventional satellites (i.e., stereo or tri-stereo push-broom high resolution satellite imagery) has therefore been an area of active research for the last few years (e.g., [9, 10, 11, 12, 13]). This issue is relevant to Cubesat images in particular as the RFM standard has been widely adopted in the nanosatellite industry. In this study, we present a method for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images. We test and assess the performance of our method to extract digital elevation models and quantify topographic changes using SkySat data from the Merenci Mine, Arizona, which is the largest copper mine in the United States, and multi-date PlanetScope DOVE-C images from a surging glacier located in the Karakoram, Pakistan.