Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.

9Vegetation plays a key role in catchment's water balance, particularly in semi-arid 10 regions that are generally water-controlled ecosystems. Nowadays, many of the 11 available dynamic vegetation models are quite complex and they have high 12 parametrical requirements. However, in operational applications the available 13 information is quite limited. Therefore parsimonious models together with 14 available satellite information can be valuable tools to predict vegetation 15 dynamics. In this work, we focus on a parsimonious model aimed to simulate 16 vegetation and hydrological dynamics, using both field measurements and 17 satellite information to implement it. The results suggest that the model is able to 18 adequately reproduce the dynamics of vegetation as well as the soil moisture 19 variations. In other words, it has been shown that a parsimonious model with 20 simple equations can achieve good results in general terms and it is possible to 21 assimilate satellite and field observations for the model implementation.

Boreal forests are warming faster than the rest of the planet. Do the benefits of higher temperatures and longer growing seasons for forest productivity exceed the negative effects of more frequent dry spells and heat waves, shifting precipitation patterns, and higher evaporative demands? And are the effects uniformly distributed geographically? To answer to these questions, the relationship between climatic variables and NDVI-a proxy of forest productivity at regional scale-was explored via Partial Least Square (PLS) regression analyses. We focused on Northern Europe, where contrasting findings on the effects of warming have been reported and that has so far been overlooked by systematic large-scale explorations of the linkages between boreal forest productivity and climatic conditions. The results show that the effects of warmer temperatures on boreal forest productivity are not uniformly positive and that water stress is already negatively affecting these forests. Indeed, increased temperatures appear beneficial in northern and wetter regions, while warmer temperatures mostly reduce forest productivity in southern and drier areas. These results are suggestive of already existing limitations due to water availability and warm temperatures, even in mesic regions like Northern Europe. These conditions are expected to become more frequent and intense in the future, potentially reducing the ability of boreal forests to provide their essential ecosystem services unless forest management practices are adapted to the new conditions.

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Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatiotemporal information that could potentially be incorporated into model calibration and validation frameworks.The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment -the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and datascarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.