|Title||Spaceborne Imaging Spectroscopy for Sustainable Agriculture : Contributions and Challenges|
|Author(s)||Hank, Tobias B.; Berger, Katja; Bach, Heike; Clevers, Jan G.P.W.; Gitelson, Anatoly; Zarco-Tejada, Pablo; Mauser, Wolfram|
|Source||Surveys in Geophysics (2018). - ISSN 0169-3298|
Laboratory of Geo-information Science and Remote Sensing
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Agriculture - Biophysical and biochemical variables - Food security - Hyperspectral - Spaceborne imaging spectroscopy|
Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer–consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.