DEMMIN - Test Site for Remote Sensing in Agricultural Application Borg, E., Fichtelmann, B., Schiller, C., Kuenlenz, S., Renke, F., Jahncke, D., Wloczyk, C. German Aerospace Center (DLR) German Remote Sensing Data Center (DFD) Joint Experiment for Crop Assessment and Monitoring (JECAM) Agriculture and Agri-Food Canada Ottawa, Canada, 21-23 July 2014 Research Areas of DLR • Aeronautics • Space Research and Technology • Transport • Energy • Defence and Security (interdisciplinary topic) • Space Administration • Project Management Agency Locations and employees 7700 employees across 32 institutes and facilities at 16 sites. Stade Hamburg Neustrelitz Bremen Trauen Offices in Brussels, Paris, Berlin Tokyo and Washington. Braunschweig Goettingen Juelich Cologne Bonn Lampoldshausen Stuttgart Augsburg Oberpfaffenhofen Weilheim DLR Neustrelitz • Satellite stations of DLR institutes and facilities: • German Remote Sensing Data Center (DFD) • DLR Remote Sensing Technology Institute • DLR Institute of Communications and Navigation • DLR Technology Marketing • DLR_Campus Neustrelitz • DLR Location Neustrelitz: 65 – 70 scientists and technicians • Companies: euromap, HeJoe German Remote Sensing Data Center (Prof. Dr. S. Dech) Department National Ground Segment (H. Maass – holger.maass<@>dlr.de) The department handles the reception, processing and interim archiving of payload data for e.g. ERS-2, IRS-1C/D, IRS-P3, Landsat-7, CHAMP, GRACE, TerraSAR missions. The work is partly carried out as a national undertaking and partly on behalf of ESA, private industry, and in cooperation with international space agencies. • Permanent receiving station for small remote sensing and science satellites • Automatic, operational-quality processing and archiving center activities for missions, including NRT (near-real-time) processing and data dissemination • Development of hardware components and software tools for receiving, processing and archiving satellite data. Why do we need a calibration and validation test site for Earth observation? plinary isci Mo tid n ul ito Remote Sensing includes r Sendeantenne Berlin M in l g a t I Regenmesser n n e f o r m m n a a o o r r i i t t i diverse e.g. platforms, sen- i o Windgeschwindigkeit o v v n n n und –richtung n E E N (Hohe: 2 m) N e Pyranometer e e e l l t t b b w Solarstrahlung w a Deutschland a r r o Lufttemperatur und o u u r (0,305 – 2,800 µm) r k k D sors, methods for interpre- -feuchte D Energieversorgung * Steuerung Blattflächenfeuchte Pyrgeometer tation Thermalstrahlung (4,500 – 42,00 µm) Bodenfeuchte : 0, 5, 10,20, 50 cm Tiefe There is an urgent require- Bodentemperatur : 0, 5, 10,20, 50 cm Tiefe ment for in-situ-data for vali- dation of value added data Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) Cal-val of remote sensing requires numerous environ- • Cooperation with Farmers (approx. mental parameters 30,000 ha) • Size of test-site: 50 km * 50 km Requirement for operationally Borg, E., Lippert, K., Zabel, E., Löpmeier, F.J., Fichtelmann, B., Jahncke, D., Maass, measured cost- and labour- H. (2009): DEMMIN – Teststandort zur Kalibrierung und Validierung von Fernerkundungsmissionen.- In: 15 Jahre Studiengang Vermessungswe sen – effective in-situ-data Geodätisches Fachforum und Festakt, Neubrandenburg, Eigenverlag (Hrsg.: Rebenstorf, R.W.).- 16.-17.01.2009.- S. 401-419. Landscape Zones Vorpommersches Flachland - flat country / end moraine LegendeLegend 0a – Beltsee 0b – Arkonasee 1 – Ostseekü stenland 2 – Vorpommersches FlachFlachland 3 – Rü ckland der Mecklenburgischen Seenplatte 4 – Hö henr ü cken und Mecklenburgischen Seenplatte 5 – Vorland der Mecklenburgischen Seenplatte 6 – Elbetal Festland Grenze des Testf eldes DEMMIN © LUNG Rückland der Mecklenburgischen Seenplatte - hilly country / ground moraine Formation of observatory DEMMIN with respect to landscape zones (http://www.umweltkarten.mv-regierung.de/script/) Hydrology and Soil Cover Hydrological Characterization: • diffuse, undeveloped water network, • innumerable lakes and water filled hollows (germ: Sölle) • Peat bogs along the rivers Rivers: Trebel, Tollense, Peene Lakes: Kummerower lake - 0.2 m above sea level Baltic See Malchiner lake - 0.6 m above sea level Baltic See Soil types of DEMMIN 35% 32% Peene: approx. depth 2 - 3 m; approx. slope 0.03% 30% 25% 23% 20% 15% 15% 12% 13% 10% Pedological Characterization: 5% 4% 0% 0% 0% 0% • Sand to sandy-loam soils S Sl lS SL sL L LT T Mo • Heterogeneous soil cover Borg et al. (2009) Relief View in the Tollense valley near the village Buchholz Height (m) Height Distance (km) Altitude profile along the view in the Tollense valley. The red pointer assigns the river bed of the Tollense river. Borg et al. (2009) Available Remote Sensing Data (Exemplarily) Available Remote Sensing Data TerraSAR-X, TanDEM-X, RapidEye of high temporal repetition rate 50 40 30 HyMap / Hyspex 20 TET TerraSAR-X 10 TanDEM-X RapidEye 0 LANDSAT 5 2000 LANDSAT 7 2001 2002 2003 2004 LANDSAT 8 2005 2006 2007 2008 IRS-1D, LISS-III.P 2009 2010 2011 2012 IRS-1C,LISS-III.P 2013 2014 Available data: • Hyper-spectral data (e.g. HyMap, Hyspex) • Multi-spectral data (e.g. IRS, RapidEye) • Thermal data (e.g. LANDSAT, TET) • RADAR data (e.g. TerraSAR-X, Tandem-X) Available Environmental and Agricultural Data Data Set Period of Time Yield Maps 2000 – 2008 Crop Maps 2000 – 2013 Measurement Data 2004 - 2014 Available agronomic process data (e.g. yield and crop maps) and in-situ-data of automated environmental measurement network (e.g. agro-meteorological data) Mean Size of fields is 80 ha and in maximum 300 ha. Lysimeter Station: Context TERENO SoilCAN • Automated lysimeter station Rustow – • 6 metal cylinder filled with undamaged soil monoliths placed on a balance Von Unold, G. (2011): http://www.ums-muc.de/lysimeter_systeme/lysimeter/ meteo_lysimeter.html (last access: 18.08.2013) Environmental Measurement Network Environmental Measurement Station German Aerospace Center Geo-Research Center, Potsdam N NW NO W O SW SO S 0 5 10 km • 40 environmental stations, • Measurement interval 15 minutes- slot = 900 sec, 15 samples, • Data transfer via telemetry transfer, • Web-data access on data server • plus approx. 70 soil moisture probes Environmental Measurement Network - Station Remote Telemetry Unit & Transmitter WIND Speed / WIND Direction 3m Energy Supply / Solar Set (9V 460mAh) Air Temperature / Air Moisture WET Leaf Wetness 2m Incident and reflected solar radiation Incident and emitted thermal radiation Kipp&Zonen Pyranometer CMP 3 (310-2800 nm) Kipp&Zonen Pyrgeometer CGR 3 (450-42000 nm) 1m Barometric Pressure (500-1500 mbar) Rain Gauge 0.2 mm Resolution Soil Temperature 05, 10, 20, 30, 50, 100 cm Depth Soil Moisture 10, 20, 30, 40, 50, 60, 70, 80, 90cm Depth Operative Processing Chain for In-situ-Data Borg, E., Schiller, C., Daedelow, H., Fichtelmann, B., Jahncke, D., Renke, F., Asche, H. (2014): Automated Derivation of Value Added Information Products on Basis of In-Situ-Data for Validation of Remote Sensing Data.- 12th International Conference on Computational Science and Applications (ICCSA 2013), Portugal.- in press. In-situ-Data Browse Products a) b) c) d) e) f) g) h) i) Sample products showing parameter distribution of a) air temperature, b) air pressure, c) relative humidity, d) shortwave, e) longwave radiation, f) leave wetness, g) soil temperature – 5 cm, h) soil moisture – 10 cm, i) soil moisture – 100 cm (http://demminweb.dlr.de) In-situ-Data Processor: Evapotranspiration Calibration Legend L* Heat of vaporization Identification of AOI Wind Direction s slope of the saturation vapor Wind Speed pressure curve Air Temperature Air Moisture Rn Net radiation Geo-Correction Leave Moisture Ground heat flux Rain Amount G Shortwave Irradiation ρ Density of air PROCESSING - Shortwave Back Radiation Atmospheric Correction cp Specific heat of air Long-wave Irradiation Long-wave Back Radiation ra Aerodynamic Resistance Soil Temperature PRE e (T)-e Saturation deficit, f=(T, e) Classification Soil Moisture s Air Pressure γ Psychrometer constant rs Stomata resistance Thematic Processing T Air temperature e Vapour pressur Product/Map-Generation Dissemination Remote Sensing: Evapotranspiration Calibration Legend eS TS NDVI a RG eS surface emissivity Identification of AOI a albedo TA TS surface temperature TA air temperature Geo-Correction RG incident (or global) solar radiation RS RA RSW RS emitted surface radiation RA atmospheric longwave radiation PROCESSING - Atmospheric Correction RN net radiation R N RSW shortwave net radiation H sensible heat flux PRE Classification G G ground heat flux H LE latent heat flux RET actual evapotranspiration NDVI normalized difference vegetation index Thematic Processing LE Basic Parameters RET Radiation Components Product/Map-Generation Components of Energy Balance Dissemination Richter, R. (2003): Value Adding Products derived from the ATCOR Models (Version 5.5, January 2003).- p. 28. http://www.rese.ch/pdf/atcor_value_adding.pdf Wloczyk, C. (2007): Entwicklung und Validierung einer Methodik zur Ermittlung der realen Evapotranspiration anhand von Fernerkundungsdaten in Mecklenburg-Vorpommern. Dissertation, S. 143, ISBN: 978-3-86009-010-7 Remote Sensing: Evapotranspiration DEMMIN 14.08.2000, 12:00 LT Estimated hourly -Demmin evapotranspiration, based on one instantaneous value Cloudless sky Water surfaces masked (Baltic Sea, lakes) © C. Wloczyk 2008 Results of the Experimental RealET-Processor Accuracy: • surface temperature approx. +/-2 K, • air temperature