Crop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview. Werner Schneider
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1 Crop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview Werner Schneider Institut of Surveying, Remote Sensing and Land Information Department of Landscape, Spatial and Infrastructure Sciences
2 Project Partners Werner Schneider, Georg Kaiser, Franz Suppan BOKU, Department of Landscape, Spatial and Infrastructure Sciences Institut of Surveying, Remote Sensing and Land Information Josef Eitzinger, Philipp Weihs, Katja Richter, Pablo Rischbeck, Sabina Thaler BOKU, Department of Water, Atmosphere and Environment Institut of Meteorology Wolfgang Postl, Judith Haumann, Rita Linke University of Vienna Department of Ecophysiology and Functional Anatomy of Plants Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 2
3 Aim and Focus of Project overall goal: adaptation and advancement of remote sensing based methods of drought stress detection and monitoring on agricultural crops exploiting the potentials of present-day satellite-based optical sensors methods to be developed and tested for selected cultivars of wheat and maize in Austria and in Germany ground truth data on drought stress conditions from biophysical measurements in connection with crop growth modelling image analysis methods for drought stress classification based on physical models of canopy reflectance and on statistical models of canopy thermal emission image data from different airborne (research) and spaceborne (operational aim) sensors mapping from satellite image data also in areas of fine-structured agricultural land use patterns use of sub-pixel methods and image fusion techniques Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 3
4 LY WP6 ATMOSPH. PLANT, YIELD 4
5 LY WP6 ATMOSPH. PLANT, YIELD 5
6 LY WP6 ATMOSPH. PLANT, YIELD 6
7 LY WP6 ATMOSPH. PLANT, YIELD 7
8 LY WP6 ATMOSPH. PLANT, YIELD SPECCIO? 8
9 LY WP6 ATMOSPH. PLANT, YIELD 9
10 LY WP6 ATMOSPH. PLANT, YIELD 10
11 WP6 LY Hymap: 126 bands, 4 m pixel size, Hyperion: 220 bands, 30 m pixel size, ATMOSPH. ALI: 7 bands, 30 m pixel size, Chris-PROBA: MODIS: 18 bands ( µm), 5 directions, 17 m pixel size, 2 dates: , bands, 250 m to 1000 m pixel size, ~ 50 dates per vegetation period PLANT, YIELD 11
12 Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 12
13 Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 14
14 Cloudfree CHRIS PROBA image segments Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 15
15 LY WP6 ATMOSPH. PLANT, YIELD 16
16 Super-Sensor with highest resolution in all dimensions? pixel size 1 m spectral bandwidth 2 nm revisit every day 0,5 µm: 142 photons per pixel, S/N < 12 0,8 µm: 128 photons per pixel, S/N < 11 2,2 µm: 20 photons per pixel, S/N < TB per day Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 17
17 LY requirements of high spatial, ATMOSPH. high spectral und high temporal resolution - fusion of Landsat-type images with MODIS, WP6 based on spectral unmixing PLANT, YIELD 18
18 Conclusion We have a lot to discuss today! Enjoy this workshop! Institut für Vermessung, Fernerkundung und Landinformation Werner Schneider 19
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