peer reviewed ; There is currently high interest in integrating data linked to remote sensing and methods from the machine-learning domain to develop tools to support pasture management. In this context, over the past two years, we have published models predicting the available compressed sward height (CSH) in pastures using Sentinel-1, Sentinel-2, and meteorological data. These scalable models could provide the basis of a decision support system (DSS) available for Walloon farmers. A platform performing the CSH prediction was developed and this paper aims to provide some insights in its prediction capabilities and tackle the challenge of using data acquired at different moments in time. Predictions were made from the beginning of January until the end of October 2021 using our most promising published models. After data cleaning, the coefficient of variation of CSH predictions, calculated for each studied date (n=35) and parcel (n=192,862), ranged from 0 to 986. This extreme variation suggests some prediction imperfections. Before the integration of the platform in a DSS, the main task to solve is the issue of missing or non-operational S1 or S2 data. Indeed, even if a gap filling method was applied, only 62% of potentially exploitable dates were usable. ; ROADSTEP