## 1 Introduction
The APS, caused by the atmospheric pressure, temperature, and water vapor variations between two SAR acquisitions, may cause artifacts on the surface deformation results [48]. Moreover, SAR images acquired in winter periods are highly affected by snow cover, which causes temporal coherence loss [47; 49; 50]. All these factors have to be carefully taken into account for SAR data processing to thoughtfully interpret the ground deformation in high mountain regions. In several cases, however, the complexity of SAR data processing, as well as the large number of attempts to be performed before obtaining reliable results, hinders the achievement of surface deformation results. Currently, the application and the treatment of SAR data in high mountain regions requires specific background knowledge of the user and demand the application of complex processing algorithms and software. Moreover, the increasing amount of SAR data available from different satellites missions leads to rising needs of storage and computing resources. Recently, a relevant service was released within the ESA GRID-based operational environment [51], i.e., the unsupervised implementation of the Parallel-SBAS (P-SBAS) algorithm [52]. GPOD is coupled with high-performance and sizeable computing resources managed by GRID technologies, and it provides flexibility for building an application virtual environment with quick accessibility to data, computing resources, and results. The users access various services useful in the EO applications through a web interface, which guides the users from the creation of a new task until the results publication, passing through the data selection and the job monitoring. The P-SBAS algorithm has been implemented to exploit the GPOD resources, and to process the SAR data archived by ESA to perform the full SBAS-DInSAR processing chain in an unsupervised fashion from the Level 0 (raw) SAR data to the generation of surface velocity maps and displacement time series [53]. Since the average processing time is smaller than 24 h, several processing attempts can be performed in a reasonable time-span to optimize the final results on a specific target area. The main advantage of this service, hereafter referred to as GPDD-SBAS, is that users with different backgrounds, more interested in the analysis of the surface deformation due to different natural and/or anthropic processes than in the details of the SAR data processing, can apply a reliable and validated state of the art algorithm without downloading large amounts of input data, nor owning costly processing hardware or learning specific software [53]. In this study, we define best practices to obtain surface displacement time series in high mountain regions by using the GPDD-SBAS service. Our work derives from a specific need: even though the unsupervised GPDD-SBAS service provides a great advance and allows a larger number of users to carry out advanced DInSAR analyses, obtaining reliable results in mountain areas is not a trivial task. The proposed methodology is applied to the Valle d'Aosta (VDA) region, Northern Italy, an alpine area characterized by high altitudes, complex morphology, and susceptibility to different mass wasting phenomena (e.g., landslides, rock glaciers, and glaciers), in some cases threatening urban areas and human activities, and it can be easily borrowed in similar areas. The paper is organized as follows. We first introduce the area of study by highlighting the geomorphological, environmental, and climatic characteristics. Then, we describe the method applied to analyze, through the use of the GPDD-SBAS service, the data acquired by ERS-1/2 and ASAR-Envisat during the 1992-2000 and 2002-2010 periods, respectively, in ascending orbit for ERS-1/2, and in ascending and descending orbit for ASAR-Envisat. Finally, we present the results, discuss our findings, and propose a number of suggestions and best practices to be considered to retrieve effective and reliable information while approaching the analysis of SAR data in high mountain areas.