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SAND proposes a space-borne imaging spectrometer (hyperspectral) mission measuring bio-geochemical/biogeophysical variables to determine land degradation processes in semi-arid geographical areas.

Arid and semi-arid lands cover approximately one third of the continental surface of the Earth. They include the deserts and their semi-arid and sub-humid dry margins, and the subtropical Mediterranean latitudes. The semi-arid ecosystems provide important land resources for adapted agricultural production and grazing systems. The importance of thoroughly monitoring the state of the environment in areas considered at risk, in the context of global climatic change and worldwide desertification dynamics, has long been recognised. These facts have led to the ratification of the UN Convention to Combat Desertification (UNCCD) by almost 200 nations worldwide. This agreement emphasises the need to assess land degradation and desertification processes.

The status of dryland systems is controlled to a large extent by a fragile equilibrium between soil, vegetation and water resources. Precipitation rates are generally low and rainfall events are known to be irregular and of high intensity. Natural ecosystems and traditional land use practices have largely adapted to these conditions over time, and thus, assure an optimum use of available resources. Dryland degradation may be triggered by (global) climatic change and/or human mismanagement. While the former may result in longer and more frequent drought events, the latter one mainly consists of inappropriate land use practices. Both may induce changes in surface properties, impacting the type and density of the vegetation cover.

Many of these changes can be detected remotely by measuring the spectral characteristics of the land surfaces. Accordingly, a high-spectral resolution remote sensing system such as the proposed SAND substantially increases these detection capabilities.

This paper presents a sensor concept providing global coverage at a reasonable spatial and very high spectral resolution for a quantitative derivation of land surface variables. This includes vegetation parameters and soil-related information such as foliage chemistry, water content, fractional cover, chemical composition, organic matter content and information about the lithological background for selected targets.