Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

RemoteRemote SensingSensing of Chlorophyll and of Chlorophyll and NitrogenNitrogen in in

CottonCotton Fields in Khorezm, Fields in Khorezm, UzbekistanUzbekistan

Gerd Ruecker1, Wouter Dorigo1, John Lamers2, Nazirbay Ibragimov3, Kirsten Kienzler2, Guenter Strunz1, Paul Vlek2

1German Aerospace Center (DLR), Oberpfaffenhofen, Germany2Center for Development Research (ZEF), Bonn, Germany

3Uzbekistan National Cotton Growing Research Institute, Tashkent, Uzbekistan

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

Contents1. Background & Objective 2. Study Area3. Methods & Activities in 20054. Results

4.1 Biochemical parameter variation & SPAD calibration forcotton leaves

4.2 Relationships between VI & leaf chlorophyll / nitrogen incotton canopies

4.3 Estimation of cotton leaf chlorophyll / nitrogen status using CHRIS/Proba data

5. Conclusions & Outlook

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

1. Background and Long-term Objectives

▶ ZEF/UNESCO Project: Economic and Ecological Restructuring of Land- and Water Use in Khorezm, Uzbekistan

▶ Long-term scientific cooperation (2002-2011)▶ Human capacity building of young Usbek students▶ Interdisciplinary integration of science▶ Science-based restructuring concept (www.khorezm-uni-bonn.de)

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

ZEF/UNESCO Project: GIS-Center Khorezm

▶ Interdisciplinary data are processed for easy use by many users▶ Central GIS-Databases is backbone for long-term project▶ Internet-Metainformation Database for information retrieval

▶ GIS-Modelling of • land suitability• optimized land use• soil salinity pattern• groundwater pattern

▶ Remote sensing of• land use distribution • cotton yield • evapotranspiration• leaf chlorophyll

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter1. Background & Specific Objective

Background: ▶ Different nitrogen (N) status of cotton in fields within a region

▶ Need for N status estimation for targeted fertilizer application

Objective: ▶ Estimation of cotton leaf chlorophyll / N status in fields of

Khorezm

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

2. Study Area

Study area: 14 km2, Khorezm region, Amu Darya Basin, UzbekistanLand uses: Mainly cottonField sizes: 1 – 25 haCalibration field: N-trial (Khorezm-127 cotton variety)Validation field: 7.5 ha (same variety)

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

3. Methods & Activities in 2005

Conceptual framework:

Leaf Leaf

CanopyCanopy

Field / Field / RegionRegion

SPAD Chlorophyll meter SPAD calibration

Reflectance measurement VI evaluationSPAD measurement

CHRIS image datapreprocessing

CHRIS VI calculation

CHRIS VI evaluation

Chlorophyll extraction

VIs calculation

Chl. estimation

ScaleScale MeasurementsMeasurements / / PreprocessingPreprocessing CalculationsCalculations ResultsResults

Regression

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

Field data & laboratory analyses (100 leaves):

▶ Calibration field: 0, 150, 200, 250 kg ha-1 N on cotton▶ Measurements: SPAD▶ Laboratory analyses: Chlorophyll a, b, total N

(Lichtenthaler, 1987; Nelson and Sommers, 1972)

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

Field data & analyses

▶ Validation field: 250 kg ha-1 N on cotton▶ Measurements: SPAD (4 upper leaves, 4 leaves along vertical profile)

Reflectance (183 plants at 20m x 20m grid)

▶ Analyses: Spatial patterns of chlorophyll, N

21

2

1

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

CHRIS image, ground truthing, image proc.

▶ CHRIS/Proba image acquisition: Mode 1 on 24th August 2005

▶ Reflectance measurements: Bright, dark targets with ASD-field spectr.

▶ GPS-measurements: 31 ground control points by Garmin GPS

▶ Image processing: destripinggeoreferencingradiometric calibrationatmospheric correction(ATCOR 6.3)

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

Spectral data processing & selection of VI

▶ Spectra spectralon correction, deviations of white ref.processing: VNIR/SWIR1/SWIR2 spectrometer jumps, outliers

averaging, resampling to CHRIS-wavelengths

▶ VI selection: 1. Broad band VIsNDVI, RVI

2. Hybrid VIsSAVI, SAVI2, MSAVI, OSAVI, TSAVI, RDVI, MTVI1, MTVI2

3. Narrow band chlorophyll VIsTCARI, MCARI, MCARI1, MCARI2

4. Derivates VIsDGVI1, DGVI2

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

4. Results4.1 Biochemical parameter variation & SPAD calibration

for cotton leaves

Parameter Minimum Maximum Mean STD

SPADCal._field () 3.3 70.2 45.2 15.5 Chlorophyll a+bLab (µg cm-2) 1.52 64.6 34.4 16.9 Total NitrogenLab (%) 0.01 1.35 0.72 0.36

SPADVal._field () 26.9 59.6 39.2 5.70 Chlorophyll a+bVal.field (µg cm-2) 16.3 52.2 29.8 7.16 Total NitrogenVal. field (µg cm-2) 0.35 0.99 0.60 0.11

▶ Calibration field: Wide SPAD, chlorophyll a+b, N range ▶ Validation field: Small variation of biochemical parameter

Note: STD=Standard deviation, Cal. field = Calibration field, Val. field = validation field; Lab = laboratory analyses; N= 100 samples from Cal. field; N= 183 samples from Val. field.

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

4.1 Biochemical parameter variation & SPAD calibration for cotton leaves of Khorezm-127 variety

SPAD Chlorophyll a+b SPAD Total N Chlorophyll Total N

▶ Good calibration of SPAD chlorophyll meter

▶ Use of SPAD for quick, easy & non-destructive estimation ofleaf chlorophyll & N in cotton in Uzbekistan

y = 1.0403x - 12.752R2 = 0.89

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80

SPAD ( )

Chlo

roph

yll a

+b (µ

g cm

-2) y = 0.0196x - 0.1724

R2 = 0.75

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60 70 80

SPAD ()

Tota

l Nitr

ogen

(%)

y = 0.0194x + 0.0494R2 = 0.89

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60 70

Chlorophyll a+b (µg cm-2)

Tota

l Nitr

ogen

(%)

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

4.2 Relationships between VI & leaf chlorophyll a+b in cotton plants

18 20 22 24 26 28 30 32 34 36 38Mean Chlorophyll a+b (µg cm -2)

in upper 4 leaves of plants

02468

1012

Freq

uenc

y

18 22 26 30 34 38 42 46Mean Chlorophylla+b (µg cm-2) of 4 vertical profile leaves of plants

0

1

2

3

4

5

6

7

8

9

Freq

uenc

y

▶ Identification of representative leaves for measuring chlorophyll - to spatially match with reflectance measurements - to estimate leaf chlorophyll for whole plant

20

25

30

35

40

45

0 5 10 15 20 25 30 35 40 45 50 55 60Cotton plant #C

hlor

ophy

ll a+

b (µ

g cm

-2)

upper leaves leaves along vertical profile

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

4.2 Relationships between VI & chlorophyll a+b in cotton plants

▶ Poor performance of broad band, hybrid VIs▶ Moderate performance of narrow band Chl. VIs (TCARI/MCARI)

▶ Soil background, plant & row structure, LAI not captured▶ Upper & vertical leaves sampling to capture representative leaves

0.00

0.10

0.20

0.30

0.40

0.50

NDVIRVISAVISAVI2MSAVIOSAVITS

AVIRDVI

TVI

MTVI1

MTVI2

TCARI

TCARI/O

SAVIMCARIMCARI1MCARI2

DGVI1DGVI2

R2

upper leaves upper l. +1 profile l. upper l. +1-2 profile l.

upper l. +1-3 profile l. upper l. + 1-4 profile l.

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

4.3 Estimation of cotton leaf chlorophyll by CHRIS-Proba mode 1 data

Estimated chlorophyll pattern byTCARI applied to CHRIS image

▶ Correspondence betw. CHRIS VI & measured chlorophyll▶ Lower values in CHRIS VI chlorophyll patterns ▶ Farmer found map useful for rethinking fertilizer application

Measured chlorophyll pattern bySPAD based on 20m x 20m grid

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

ter

5. Conclusions & Outlook

Conclusions▶ Successful calibration of SPAD▶ TCARI best VI, but only moderate relationship with Chl.▶ CHRIS TCARI pattern generally fair, but Chl. underestimated

CHRIS promising for Chlorophyll estimation in Khorezm

Outlook▶ CHRIS data acquistion during prime fertilization stages▶ Chl. estimation using PROSPECT/SAIL model▶ Transfer of Chl. patterns with other

data into fertilizer recommendations

Ger

man

Rem

ote

Sen

sin

g D

ata

Cen

terThank You for Your Attention !

Thanks to: ▶ ESA PROBA mission team (esp. Peter F., Bianca H.), Surrey Sat. Techn. Ltd▶ ZEF/UNESCO Khorezm project leader (Dr. Martius)▶ Field assistants, farmers▶ Uzhydromet, Tashkent (Mrs. Smolkova) ▶ German Ministry for Education and Research