PHENOLOGICAL SPECTRAL INDEX TIME SERIES -...

PHENOLOGICAL SPECTRAL INDEX TIME SERIESfor the dynamic derivation of soil coverage information

Markus Moller

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DOY

P

1 21 51 81 111 141 171 201 231 261 291 321 351

10121518192124AHMartin Luther University Halle-Wittenberg · Farm Management Group · Germany

Markus Moller PhenoSITS JRC | 21 March 2017 1 / 12

Outline

1 Motivation

2 WorkflowCrop-specific phenological windowsPhenological NDVI time series

3 Conclusion


Motivation

Monitoring of soil erosion patterns on agricultural land

Up-to-date information about parcel-and phase-specific crop coverage

Phenological spectral index time series

Fused satellite imagery of high spatial (e.g. Landsat) and hightemporal resolution (e.g. MODIS) ⇒ spectral index time series

Interpolated phenological observations ⇒ phenological phases


Workflow

Phenologial spectral index time series

Phenologicalwindows

raster1 × 1 km

Phenologicalobservations

DWDpoint

ElevationUSGSraster90 × 90 m

PH

AS

E

Spectral indextime series

raster30 × 30 m

LandsatUSGSraster30 × 30 m

MODISUSGSraster250 × 250 m

STA

RF

MParcel

PHENOLOGICAL INDEX TIME SERIES

Y EAR

DOY

V I

2013

170

V I2013,170

b

b

INTRA- and INTER-ANNUAL INDEX DYNAMIC

Gao, F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M., Prueger, J.H.

(2017): Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing ofEnvironment 188, 9–25.

Gerstmann, H., Doktor, D., Glaßer, C. & Moller, M. (2016): PHASE: A geostatistical model for the Kriging-based spatial

prediction of crop phenology using public phenological and climatological observations. Computers and Electronics inAgriculture 127, 726–738.

Moller, M., Gerstmann, H., Dahms, T.C., Gao, F. & Forster, M. (2017): Coupling of phenological information and

simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk.CATENA 150, 192–205.


Workflow Crop-specific phenological windows

Interpolated phenological events of Winter Wheat in Germany for 2011

55°N

50°N

5°E 10°E 15°E

93

121

DOY

55°N

50°N

5°E 10°E 15°E

5°E 10°E 15°E

180

134

222

195

Phase 1555°N

50°N

5°E 10°E 15°E

DOY

DOY

158

5°E 10°E 15°E

194

200

249DO

Y

Phase 21

Phase 1855°N

50°N

5°E 10°E 15°E

DOY

273

291

Phase 19

DOY

Phase 24 Phase 12

Study site

Study site

Study site Study site

Study site Study site

12 – emerging | 15 – shooting | 18 – beginning of ear | 19 – milk ripeness | 21 – yellow ripeness | 24 – harvest


Workflow Crop-specific phenological windows

Winter Wheat (top) and Maize (bottom)0.

00.

20.

40.

60.

81.

0

DOY1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361

Phases

10121518192124AH

0.0

0.2

0.4

0.6

0.8

1.0

DOY1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361

Phases

10126756519202124AH

5 – begin of flowering | 10 – tilling, sowing, drilling | 12 – emergence | 15, 67 – shooting/growth in height | 18 – beginning ofear | 19 – milk ripeness | 20 – wax-ripe stage | 21 – yellow ripeness | 24 – harvest | 65 – tassel emergence


Workflow Phenological NDVI time series

Winter Wheat (top) and Maize (bottom)0.

00.

20.

40.

60.

81.

0

DOY

ND

VI

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361

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Imagery

LSRE

●

simulated NDVI

x~

x25−75

Phases

10121518192124AH

0.0

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0.8

1.0

DOY

ND

VI

1 21 41 61 81 101 121 141 161 181 201 221 241 261 281 301 321 341 361

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Imagery

LSRE

●

simulated NDVI

x~

x25−75

Phases

10126756519202124AH

5 – begin of flowering | 10 – tilling, sowing, drilling | 12 – emergence | 15, 67 – shooting/growth in height | 18 – beginning ofear | 19 – milk ripeness | 20 – wax-ripe stage | 21 – yellow ripeness | 24 – harvest | 65 – tassel emergence



Test site-specific and spatial NDVI variation for DOY = 66

(a)

0.0 0.2 0.4 0.6

02

46

NDVI

Den

sity

●

●●●●●●●●●●

12

34

0.0 0.2 0.4 0.6

NDVIcl

uste

r

(b)

Figure 9: Classification result of parcel-specific NDV I medians for Winter Wheat in 2011and DOY 66 (a), corresponding cluster and NDV I median distribution (b).

53



Parcel-specific (left) and phase-specific NDVI median variations (right)

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●

0.0 0.2 0.4 0.6 0.8 1.0

050

100

150

Scatter plot

x~(NDVI)

DO

Y

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●

●

●

Phases

121518

DOY 66

0.0 0.2 0.4 0.6 0.8 1.0

05

1015

Density plot

x~(NDVI)

Den

sity

0.36 0.66 0.78

Median

Daily or phase-specific soil coverage

empirical models

regression models based on classified vertical photographs andcorresponding satellite imagery


Conclusion

Phenological NDVI time series

Dynamic parametrization of soil erosion models

Definition of phenological windows for the prediction of fractionalvegetation coverage, crop residue coverage or bare soil

Derivation of short- and long-term parcel-specific soil coverageinformation

Temporal and geometric disaggregation of hotspot areas (monitoring)

Limitation

The quality of the spectral index time series depends on the distanceand the phenological representativity of MODIS-Landsat-pairs.

The fusion quality is expected to be improved by using Sentinel-2imagery.


Conclusion

Satellite imagery: coverage and resolution

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

SPECTRAL RANGE [nm]

SE

NS

OR

S

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

1

2

3

4

5

6

7

1 · Sentinel 2 · 10 m2 · 5 days



4 · RapidEye · 5 m2 · max. 5 days

5 · Landsat 8 · 30 m2 · 16 days

6 · MODIS · 250 m2 · 1-2 days

7 · MODIS · 500 m2 · 1-2 days


Conclusion

Questions?

Markus Moller | [email protected]

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

DOY

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1 21 51 81 111 141 171 201 231 261 291 321 351

10121518192124AHThis study was funded by the German Ministry of Economics and Energy and managed by the

German Aerospace Center (DLR), contract no. 50 EE 1262 and 50 EE 1230.


[email protected]

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