Climate change, food insecurity and limited land and water resources strengthen the need for operational and spatially explicit information on vegetation condition and dynamics. The detection of vegetation condition as well as multiannual and seasonal changes using satellite remote sensing, however, depends on the choice of data including length and frequency of time series. Thus, this contribution focuses on the derivation of the optimal remotely sensed data for vegetation monitoring and extraction of relevant metrics. Time series of satellite data from Landsat-8, Sentinel-1/2, and MODIS were used to identify characteristics of vegetation at different spatiotemporal scales. We derived parameters, such as: maximum and amplitude based on vegetation index time series, as well as Land Surface Temperature (LST). Along with optical data, we used backscattering intensity over consecutive vegetation growing seasons. The analysis was carried out using Google Earth Engine, a cloud computing platform which allows to access various data archives and conduct data-intensive analysis. Taking advantage of this platform, we developed a web-based application named GreenLeaf. The application is computing metrics and plotting time series, based on parameters defined by the user. The derived vegetation condition parameters provide sufficient information to detect vegetation change. In addition, the images acquired from near-coincident dates provide similar information over continuous surfaces. The developed application contributes to the use of satellite data and the simplification of data access for users with limited remote sensing experience and/or restricted processing power. Aiming at providing this knowledge to stakeholders can further support decision making on multiple scales.