## 5 Development of the EnMAP Science Plan  
One of the key advantages of imaging spectroscopy is its adaptability to a wide range of research fields and environmental applications. These include, among others, terrestrial and aquatic ecosystems, natural resource management, hazards and risks and atmospheric research. The reader is referred to existing reviews on imaging spectroscopy (e.g., [3, 4]) for a comprehensive overview of those topics. Particular aspects of those general topics are currently being developed in the framework of the EnMAP preparatory phase by the EnSAG [55], as well as in other activities, such as PhD projects funded by the EnMAP science program. Specific research tasks currently under development by the EnSAG towards the consolidation of the EnMAP science plan comprise the following:  
* Agriculture: EnMAP will open new perspectives for remote sensing in agriculture. This central branch of the economy is challenged by supplying enough food and bio-materials for a growing and wealthier global population and by making agricultural production sustainable. Increasing resource use efficiency, namely water, energy, fertilizers and soil, by turning farm management into an information and knowledge business will allow both. \"Smart farming\" eventually aims at using crop-growth models and remote sensing data to most efficiently manage each location on each field on the globe by choosing the right crop and the right time for sowing, for applying the right amount of fertilizer and pesticides and for harvesting [56]. Today empirical relations translate multi-spectral image data into crop parameters, which enter crop-growth models. This procedure is region and crop specific, expensive and inflexible and hampers the introduction of remote sensing in smart farming. Therefore, research within the EnMAP preparatory program focuses on developing new ways to derive crop parameters, like leaf-area index, biomass, chlorophyll and nitrogen content, directly from hyperspectral EnMAP simulator data using first order physical and physiological principles. The approach uses a tightly-coupled system of crop growth and crop radiative transfer models to derive spatio-temporal fields of crop parameters through iterative ensemble analysis [57]. A model for the realistic simulation of the hyperspectral and multiangular reflectance response of virtual 3D cereal canopies has also been implemented in the framework of EnMAP [58]. * Forest: Forests provide innumerable ecological, societal and climatological benefits, but have also been exposed to increasing pressure from environmental changes, such as global warming and human population growth. The most important processes occurring in forests (e.g., carbon exchange, photosynthesis and respiration, evapotranspiration, nutrient cycling) are difficult to measure. Ecosystem models have been developed to simulate the fluxes of matter and energy and to increase the understanding of scale- and time-dependency in these processes. However, remote sensing-derived leaf and canopy variables are needed to constrain such models, and it has been suggested that hyperspectral imaging systems allow more detailed and accurate retrieval of these properties [59]. Forest ecosystems are particularly vulnerable to drought and temperature extremes. Climate-driven forest die-off from drought and heat stress has already occurred around the world and is expected to further increase with climate change. Hyperspectral imaging from space has a great potential to become one of the most important information sources for identifying critical, drought-affected forest sites, particularly in synergy with higher spatial resolution data acquired at increased repetition rates (e.g., Sentinel-2 and -3). Research priorities are focusing on testing efficient spectral indicators, alongside the refinement of algorithms for forest-type mapping, the detection of structural changes and gradients in forest ecosystems (e.g., [60]) and forward and inverse radiative transfer modeling.