radar-crop-monitor · christiane schmullius, linara arslanova, nesrin salepci, felix cremer,...
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Radar-Crop-Monitor
Extraktion landwirtschaftlicher Parameter mit Sentinel-1 Daten
Christiane Schmullius, Linara Arslanova, Nesrin Salepci, Felix Cremer, Clémence Dubois, Marcel
Urban, Carsten Pathe – Friedrich-Schiller-Universität Jena
Marcel Foelsch, Friedemann Scheibler – CLAAS E-Systems GmbH
Förderkennzeichen 50EE1901, Laufzeit 01.06.2019 – 31.05.2021
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Outline
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
• Motivation & Objectives
• Data sets and Study area
• Methodology
• Preliminary results
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Motivation 1
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Martin Faber, 2016 : „Schadscan- Beurteilung von Schäden im Pflanzenbau“ Einsatz der Drohnentechnologie in der Land- und Forstwirtschaft, TLUG, 18.Mai.2016
Schwarzwild-Schäden im Umfeld des NLP Hainich, Fotos: P. Schmidt (BEAG), A. Klamm (NLP-Verwaltung)
Beispiele von Wildschweinschäden
NDVI (Optisch)
Abweichungskarte (Radar)
Störungskarten, basierend auf der
Abweichung des Pixelwertes vom
Feldmittelwert
Räumlich-zeitliche Analyse des RVI* und NDVI bei Feldstörungen * Kim et al., GERS, 2012
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Motivation 2
Table 1. Amount of Sentinel-1 and Sentinel-2 Images for site Friensted
Sentinel-1 A + D
(VV/VH)
Sentinel-2 (<30% cloud cover)
2017 118 + 119 = 237 9
2018 116 + 121 = 237 25
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Motivation 3
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Objective 1: Investigate impacting factors on radar backscatter Objective 2: Supplement optical time series
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
Meteorological Sensor specific Geographical
- Precipitation (dew, rainfall,
snow)
- Temperature
- Wind speed
- Soil composition/texture
- Spatial plant growth
distribution
- Incidence angle associated with
each beam mode
- Wavelength C-band/ penetration
depth
- Acquisition time (A/D)
- Polarization (VV/VH) - Soil moisture
- Dielectric constant of the target
- Local incident angle
Vegetation related
- Plant structure/crop
morphology
- Plant vitality
- Surface roughness
- Plant row direction
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Study areas A Demmin, Mecklenburg-Vorpommern
B Frienstedt, Thuringia
C Markneukirchen, Saxonia
Data
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
• Sentinel-1, Sentinel-2 data => ESA Copernicus Open Access Hub Portal
(https://scihub.copernicus.eu/)
• Meteorological data => DWD Temperature, Precipitation (qualitative and quantitative)
• Phenological data => DWD for 6 crop types: winter wheat, winter barley, spring barley, rapeseed,
corn, sugar beet
• Observational data from individual farmers: planting dates, fertilization schedules, harvest and yield
• CLAAS CropView – 365FarmNet
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Schmullius et al., Radar-Crop-Monitor, 13. November 2019
Methodologie
2019.04.18
I
2019.05.06
II
2019.06.13
II
2019.07.07
III
NDVI 2018 (mean)
NDVI 2018 (standard deviation)
Preliminary Results 1
NDVI 2017 (mean) NDVI 2018 (mean)
2019.05.06
I
2019.06.13
II
2019.07.07
III
9 - 15% moderate – strong slope 2019.05.06, field id = 36, winter barley
NDVI 2018 (standard deviation)
Preliminary Results 2
Winter Wheat
• Slope classes
• VV 2017
• A/D
East North South West
Preliminary Results 3
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To do …
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Name des Referenten, Funktion
SB 2019.07.29 RA 2019.05.16
WB 2019.05.19 SB 2019.05.19 WW 2019.06.13 CR 2019.07.29
Investigate
thoroughly
effect of interception
in different crop
canopies
• Select images with similar weather conditions (exclude days with any kind precipitation)
• maximum day difference is 1 day
• consider each phenological stage
1 day 1 day 6 days 5 days 1 day 1 day
Analysis of effects of local incidence angles (33A/42D)
harvest 21 Jul – 01 Aug I. steam elongation II. III.
III.
Tab.1: Amount of fields with different row directions for
ascending/descending acquisitions
classes asc des
1 0 - 15° 166 - 180° 0 5 5
2 16 - 30° 151 - 165° 2 1 3
3 31 - 45° 136 - 150° 5 0 5
4 46 - 60° 121 - 135° 6 0 6
5 61 - 75° 106 - 120° 13 1 14
6 76 - 90° 91 - 105° 0 19 19
Total: 26 26 52
2017
classes asc des
1 0 - 15° 166 - 180° 0 10 10
2 16 - 30° 151 - 165° 3 2 5
3 31 - 45° 136 - 150° 13 0 13
4 46 - 60° 121 - 135° 1 0 1
5 61 - 75° 106 - 120° 14 1 15
6 76 - 90° 91 - 105° 0 18 18
Total: 31 31 62
2018
Analysis of effects of row orientation
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General observations for Frienstedt (KO+5)
Schmullius, Arslanova, Salepci et al., Radar-Crop-Monitor, 13. November 2019
Slopes matter crop-dependent, BUT through
phenology
A/D acquisition times matters (aspect effects could
not be found)
Water films (interception on the plant canopy, dew,
melted snow) => backscatter increases
Heavy precipitation => radar backscatter
decreases ..sometimes.. Hence, radar signals
gathered from fields with varying types of
wetnesses do not allow signal differentiation
between classes
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Schmullius, Arslanova, Salepci et al., Radar-Crop-Monitor, 13. November 2019
Thank you for your attention !
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