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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 …

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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 !