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Proceedings Paper

Satellite-based monitoring of grassland: assessment of harvest dates and frequency using SAR
Author(s): R. Siegmund; K. Grant; M. Wagner; S. Hartmann
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Paper Abstract

Grasslands are among the largest ecosystems worldwide and according to the FAO they contribute to the livelihoods of more than 800 million people. Harvest dates and frequency can be utilised for an improved estimation of grassland yields.

In the presented project a highly automatised methodology for detecting harvest dates and frequency using SARamplitude data was developed based on an amplitude change detection techniques. This was achieved by evaluating spatial statistics over field boundaries provided by the European Integrated Administration and Control System (IACS) to identify changes between pre- and post-harvest acquisitions. The combination of this method with a grassland yield model will result in more reliable and regional-wide numbers of grassland yields. In our contribution we will focus on SAR-remote sensing for monitoring harvest frequencies, discuss the requirements concerning the acquisition system, present the technical approach and analyse the verified results.

In terms of the acquisition system a high temporal acquisition rate is required, which is generally met by using SARsatellite constellations providing a revisit time of few days. COSMO-SkyMed data were utilised for the pilot study for developing and prototyping a monitoring system. Subsequently the approach was adapted to the use of the C-Band system Sentinel-1A becoming fully operational with the availability of Sentinal-1B.

The study area is situated northeast of Munich, Germany, extending to an area of approx. 40km to 40km and covering major verification sites and in-situ data provided by research farms or continuously surveyed in-situ campaigns. An extended time series of SAR data was collected during the cultivation and vegetation cycles between March 2014 and March 2016. All data were processed and harmonised in a GIS database to be analysed and verified according to corresponding in-situ data.

Paper Details

Date Published: 25 October 2016
PDF: 17 pages
Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999803 (25 October 2016); doi: 10.1117/12.2240947
Show Author Affiliations
R. Siegmund, GAF AG (Germany)
K. Grant, Institute for Crop Science and Plant Breeding (Germany)
M. Wagner, GAF AG (Germany)
S. Hartmann, Institute for Crop Science and Plant Breeding (Germany)


Published in SPIE Proceedings Vol. 9998:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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