Grasslands are the world’s most extensive terrestrial ecosystem, and are a major feed source for livestock. Meeting increasing demand for meat and other dairy products in a sustainable manner is a big challenge. At a field scale, Global Positioning System and ground-based sensor technologies provide promising tools for grassland and herd management with high precision. With the growth in availability of spaceborne remote sensing data, it is therefore important to revisit the relevant methods and applications that can exploit this imagery. In this article, we have reviewed the (i) current status of grassland monitoring/observation methods and applications based on satellite remote sensing data, (ii) the technological and methodological developments to retrieve different grassland biophysical parameters and management characteristics (i.e. degradation, grazing intensity) and (iii) identified the key remaining challenges and some new upcoming trends for future development. The retrieval of grassland biophysical parameters have evolved in recent years from classical regression analysis to more complex, efficient and robust modeling approaches, driven by satellite data, and are likely to continue to be the most robust method for deriving grassland information, however these require more high quality calibration and validation data. We found that the hypertemporal satellite data are widely used for time series generation, and particularly to overcome cloud contamination issues, but the current low spatial resolution of these instruments precludes their use for field-scale application in many countries. This trend may change with the current rise in launch of satellite constellations, such as RapidEye, Sentinel-2 and even the microsatellites such as those operated by Skybox Imaging. Microwave imagery has not been widely used for grassland applications, and a better understanding of the backscatter behaviour from different phenological stages is needed for more reliable products in cloudy regions. The development of hyperspectral satellite instrumentation and analytical methods will help for more detailed discrimination of habitat types, and the development of tools for greater end-user operation.

Spatial monitoring tools are necessary to respond to the threat of global biodiversity loss. At the European scale, remote sensing tools for NATURA 2000 habitat monitoring have been requested by the European Commission to fulfill the obligations of the EU Habitats Directive. This paper introduces a method by which swath events in semi-natural grasslands can be detected from multi-temporal TerraSAR-X data. The investigated study sites represent rare and endangered habitats (NATURA 2000 codes 6410, 6510), located in the Döberitzer Heide nature conservation area west of Berlin. We analyzed a time series of 11 stripmap images (HH-polarization) covering the vegetation period affected by swath (June to September 2010) at a constant 11-day acquisition rate. A swath detection rule was established to extract the swath events for the NATURA 2000 habitats as well as for six contrasting pasture sites not affected by swath. All swath events observed in the field were correctly allocated. The results indicate the potential to allocate semi-natural grassland swath events to 11-day-periods using TerraSAR-X time series. Since the conservation of semi-natural grassland habitats requires compliance with specific swath management rules, the detection of swath events may thus provide new parameters for the monitoring of NATURA 2000 grassland habitats.

We found a mistake in the swath detection rule in Section 2.4 [1]. Specifically, the percent deviation calculation in the definition of the signal changes D₁ and D₂ and axiom A₂ are altered. The correct version shall be: Consequently, the proposed rule for the detection of swath events consists of two axioms (A₁ and A₂) that need to be satisfied. For the signal backscatter (σ°) at a specific acquisition order number (k) of the acquired scene in the time series (N), the positive or negative signal changes in percent deviation for the first (D₁) and second (D₂) acquisition after a potential swath event are considered as: