Radar-Crop-Monitor Extraktion landwirtschaftlicher Parameter mit Sentinel-1 Daten
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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 1 / 15
Outline
• Motivation & Objectives
• Data sets and Study area
• Methodology
• Preliminary results
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Motivation 1
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Beispiele von Wildschweinschäden
Schwarzwild-Schäden im Umfeld des NLP Hainich, Fotos: P. Schmidt (BEAG), A. Klamm (NLP-Verwaltung)
Martin Faber, 2016 : „Schadscan- Beurteilung von Schäden im Pflanzenbau“
Einsatz der Drohnentechnologie in der Land- und Forstwirtschaft, TLUG, 18.Mai.2016
4
Räumlich-zeitliche Analyse des RVI* und NDVI bei Feldstörungen
* Kim et al., GERS, 2012
NDVI Abweichungskarte
(Optisch) (Radar)
Störungskarten, basierend auf der
Abweichung des Pixelwertes vom
Feldmittelwert
Motivation 2
Table 1. Amount of Sentinel-1 and Sentinel-2 Images for site Friensted
Sentinel-1 A + D Sentinel-2 (
Motivation 3
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Objective 1: Investigate impacting factors on radar backscatter
Objective 2: Supplement optical time series
Vegetation
Meteorological Geographical Sensor specific
related
- Precipitation (dew, rainfall, - Soil composition/texture - Incidence angle associated with - Plant structure/crop
snow) - Spatial plant growth each beam mode morphology
- Temperature distribution - Wavelength C-band/ penetration - Plant vitality
- Wind speed depth
- Acquisition time (A/D)
- Polarization (VV/VH)
- Soil moisture
- Local incident angle - Plant row direction
- Surface roughness
- Dielectric constant of the target
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
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Study areas
A Demmin, Mecklenburg-Vorpommern
B Frienstedt, Thuringia
C Markneukirchen, Saxonia
Data
• 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
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
10 / 20Methodologie
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
11 / 20Preliminary Results 1
NDVI 2018 (mean)
NDVI 2018 (standard deviation)
I II II III
2019.04.18 2019.05.06 2019.06.13 2019.07.07Preliminary Results 2
9 - 15% moderate – strong slope
2019.05.06, field id = 36, winter barley
NDVI 2018 (mean) NDVI 2017 (mean)
NDVI 2018 (standard deviation) I II III
2019.05.06 2019.06.13 2019.07.07Preliminary Results 3 Winter Wheat • Slope classes • VV 2017 • A/D East North South West
To do …
Schmullius et al., Radar-Crop-Monitor, 13. November 2019
16 / 20Investigate
thoroughly
effect of interception
in different crop
canopies SB 2019.05.19 WW 2019.06.13 WB 2019.05.19 CR 2019.07.29
RA 2019.05.16 SB 2019.07.29
Name des Referenten, Funktion
17 / 20Analysis of effects of local incidence angles (33A/42D)
• Select images with similar weather conditions (exclude days with any kind precipitation)
• maximum day difference is 1 day
• consider each phenological stage
III.
II. III. harvest 21 Jul – 01 Aug
I. steam elongation
1 day 1 day 6 days 5 days 1 day 1 dayAnalysis of effects of row orientation
Tab.1: Amount of fields with different row directions for
ascending/descending acquisitions
2017
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
2018 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 62General observations for Frienstedt (KO+5)
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
Schmullius, Arslanova, Salepci et al., Radar-Crop-Monitor, 13. November 2019
20 / 20Thank you for your
attention !
Schmullius, Arslanova, Salepci et al., Radar-Crop-Monitor, 13. November 2019
21 / 20You can also read
