## 1 Introduction
Similarly, the fast-paced change observed for the cryosphere including the significant trends in both snow and sea-ice cover [20; 21; 22] means that we need an improved account of surface thermodynamic processes at the relevant scales in interaction with the diurnal cycle and synoptic variability. Observing and simulating the response of land biophysical variables and the cryosphere to extreme events is a major scientific challenge in Numerical Weather Prediction (NWP) and environmental applications, which is relevant also in the context of climate change adaptation. The modeling of terrestrial variables can be improved through the integration of observations. Integrating observations into models covers several aspects: (1) the dynamic integration of observations into models through data assimilation techniques, (2) the use of observations for model validation and evolution and (3) the mapping of the model parameters used to characterize the representation of land properties within the model (e.g., soil properties, land cover). Although the assimilation from satellite data is becoming widely adopted in Earth system modelling also at the surface [23], limited use tends to be made of the wealth of Earth observations (EO) data available for model validation, evolution and parameterisation. There is a thus a great opportunity to improve upon current development strategies. For example, it is well known that the use of indirect observations such as the near surface temperatures to constrain model soil moisture [24] can lead to compensating errors. Remote sensing can also greatly support model development for the cold processes associated with seasonal snow cover. Snow is a key component of the Earth system due to the unique surface \"shielding\" properties and its presence over large parts of the Earth's surface. The radiative (albedo), thermal (insulation) and mechanic (roughness) characteristics of a snow covered surface differ greatly from those of a snow-free surface. While the particular microphysical characteristics of snow are challenging to represent in models, they provide unique radiative signatures that can be explored by EO data. Remote sensing observations are particularly useful in this context because they are now available for all parts of the globe. However, most snow remote sensing products greatly benefit from in situ observations [25]. Many satellite-derived products relevant to the hydrological and vegetation cycles are already available and the use of combined observing platforms is increasingly adopted in order to enable the move towards the kilometer-scale and support global Earth surface modelling. The focus of this review is to cover relevant successful examples, inspired by the National Aeronautics and Space Administration (NASA) decadal survey [26] and by the World Meteorological Organisation (WMO) Global Climate Observing System (GCOS) essential climate variable observing capabilities [27; 28], without aiming to be exhaustive. We present recent use of Earth Observations data pertinent to constraining the surface energy, water and carbon cycles in models. We discuss their relevance for the next generation of surface models for Earth system monitoring and forecasting applications, and we present options for closer collaboration and more rapid information uptake between data providers and model developers. In Section 2, satellite and in situ observations of the earth surface are listed with the relevant instruments information. Section 3 presents the Earth surface model development examples over land and ocean. Section 4 presents some of the pathways in which EO data guide enhanced global-scale local relevant monitoring and forecasting applications. Section 5 discusses how to increase EO data uptake in earth system modelling moving towards global km-scale, including international coordination activities. Section 6 presented a summary and the outlook.