multi-source spectral approach for early …in.bgu.ac.il/en/epif/site...
Post on 09-May-2018
213 Views
Preview:
TRANSCRIPT
MULTI-SOURCE SPECTRAL APPROACH FOR
EARLY WATER-STRESS DETECTION IN
ACTUAL FIELD IRRIGATED CROPS
Maria Polinova1, Thomas Jarmer2, Anna Brook1 1Spectroscopy & Remote Sensing Laboratory Center for Spatial Analysis Research
(UHCSISR), Department of Geography and Environmental Studies, University of
Haifa, ISRAEL - polinovamaria88@gmail.com, abrook@geo.haifa.ac.il
Department of Geography and
Environmental Studies
2Remote Sensing Group, Institute of Computer Science,
University of Osnabrueck, Germany - thomas.jarmer@uos.de
Optimal irrigation planning
2
Inputs
Crop
Soil properties
Weather
Type of irrigation
etc.
Irrigation
schedule Analysis
Grant #41683 - Funded by Israeli Water Authority
Field feedback
3
Crop growth rate: height, LAI,
green biomass, etc.
Canopy scale Leaf scale
Photosynthesis rate, light use
efficiency, chlorophyll and
carotenoid level, etc.
Vegetation features by spectrum
4
Elowitz, M. R. (2013). What is Imaging Spectroscopy (Hyperspectral Imaging)?. Retrieved
November, 27, 2013.
Satellite spectral imagery
5
Benefits Disadvantages
Free or Mid-cost Low resolution (spatial, spectral and
temporal)
Wide spectral range Weather conditions
Big area cover No survey managing
Spectral camera (airborne, field)
6
Benefits Disadvantages
Big area cover Expensive
High spatial and spectral resolution Weather conditions
VIS-NIR-SWIR range
Survey manage
Field spectrometer
7
Benefits Disadvantages
Low-cost Point-by-point measurement
High spectral resolution Weather conditions
VIS-NIR-SWIR range
Direct sample measures
Autonomous surveys
Laboratory measures
Vegetation Indices (VIs)
8
VI Development
technologies
Type of
sensitivity Formula Application in agriculture Reference
Normalized Difference
Vegetation Index
(NDVI)
Satellite Green
biomass 𝑤880−𝑤680
𝑤880+𝑤680
Water content, physical parameters
(height, LAI, biomass, dry matter),
predicting yield
Rouse et al.,
1973
Green
Atmospherically
Resistant Index (GARI)
Satellite Chlorophyll
𝑤800 + (𝑤550 − 1.7(𝑤450 − 𝑤670))
Crop coefficient, chlorophyll level,
LAI, soil salinity, predicting yield
Gitelson,
Kaufman and
Merzylak, 1996
Modified Triangular
Vegetation Index
(MTVI)
Airborne LAI
2(𝑤800 − 𝑤550) − 2.5(𝑤670 − 𝑤550
Crop coefficient, chlorophyll level,
LAI, yield predicting
Haboudane et
al., 2004
Leaf area index for
cotton
Field
spectroscopy LAI exp(4.78ln
𝑤900−𝑤680
𝑤900+𝑤680+ 1.55)
Crop coefficient, green LAI,
predicting yield Gutierrez, 2012
Pigment-specific
simple ratio
algorithms
Field
spectroscopy Chlorophyll 19.53
𝑤810
𝑤676
1.48+ 6.65
𝑤810
𝑤676
1.61
Nitrogen and water concentration,
chlorophyll level, pests detecting Blackburn, 1998
Dynamics of VIs temporal behavior
13
1. Calculate average means
2. Estimate variability of VIs by coefficient of variability
Cv = s /| x̄ |
where Cv is a coefficient of variation, s is the standard deviation, and x̄ is the
average mean
Tomato
14
May/2015 Jun/2015
Jul/2015
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
5/16/2015 6/5/2015 6/25/2015 7/15/2015 8/4/2015
Plant water concentration
Total water amount=506mm
0
10
20
30
40
50
60
3/15/2015 4/15/2015 5/15/2015 6/15/2015 7/15/2015
Weekly irrigation amount
Tomato
15
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
3/17/2015 5/6/2015 6/25/2015 8/14/2015
NDVI
RapidEye OLI8 Field_spec
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
3/17/2015 5/6/2015 6/25/2015 8/14/2015
GARI
RapidEye OLI8 Field_spec
0
0.2
0.4
0.6
0.8
1
1.2
3/17/2015 5/6/2015 6/25/2015 8/14/2015
MTVI
RapidEye OLI8 Field_spec
0
500
1000
1500
2000
2500
3000
3500
4000
4500
3/17/2015 5/6/2015 6/25/2015 8/14/2015
PSSR
RapidEye OLI8 Field_spec
35% 48%
20%
0
0.5
1
1.5
2
2.5
3
3.5
3/17/2015 5/6/2015 6/25/2015 8/14/2015
LAIc
RapidEye OLI8 Field_spec
17% 19% 25%
25%
54% 30%
68%
127%
Cotton
16
May/2
015
Jun/2
015
Jul/2
015
A
ug/2
015
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
4/26/2015 5/26/2015 6/25/2015 7/25/2015 8/24/2015 9/23/2015
Plant water concentration
Stressed (dry leave)
0
5
10
15
20
25
30
35
40
45
50
5/22/2015 6/22/2015 7/22/2015 8/22/2015
Weekly irrigation amount
Total water amount=495mm
Cotton
17
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
3/27/2015 5/16/2015 7/5/2015 8/24/2015
NDVI
RapidEye OLI8 Field_spec Field_stress
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
3/27/2015 5/16/2015 7/5/2015 8/24/2015
GARI
RapidEye OLI8 Field_spec Field_stress
0
0.5
1
1.5
2
2.5
3
3/27/2015 5/16/2015 7/5/2015 8/24/2015
LAIc
RapidEye OLI8 Field_spec Field_stress
0
500
1000
1500
2000
2500
3/27/2015 5/16/2015 7/5/2015 8/24/2015
PSSR
RapidEye OLI8 Field_spec Field_stress
19%
0
0.2
0.4
0.6
0.8
1
1.2
1.4
3/27/2015 5/16/2015 7/5/2015 8/24/2015
MTVI
RapidEye OLI8 Field_spec Field_stress
16% 24% 24% 21%
17% 21%
25% 16%
20%
35% 18%
44%
42%
43%
101% 75%
15%
124%
102%
Chickpea
18
Jan/2
015
M
ar/2
01
5
Apr/2
015
M
ay/2
015
Jun/2
015
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1/16/2015 2/15/2015 3/17/2015 4/16/2015 5/16/2015 6/15/2015
Plant water concentration
0
5
10
15
20
25
30
35
5/18/2015 5/25/2015 6/1/2015
mm
Daily irrigation amount
Total water amount=240mm
Chickpea
19
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1/6/2015 3/7/2015 5/6/2015 7/5/2015
NDVI
RapidEye OLI8 Field_spec
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1/6/2015 3/7/2015 5/6/2015 7/5/2015
GARI
RapidEye OLI8 Field_spec
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1/6/2015 3/7/2015 5/6/2015 7/5/2015
MTVI
RapidEye OLI8 Field_spec
0
0.5
1
1.5
2
2.5
1/6/2015 3/7/2015 5/6/2015 7/5/2015
LAIc
RapidEye OLI8 Field_spec
0
200
400
600
800
1000
1200
1400
1600
1/6/2015 3/7/2015 5/6/2015 7/5/2015
PSSR
RapidEye OLI8 Field_spec
30%
15% 18% 20%
47% 26%
34%
30% 30%
18%
26% 35% 80%
19%
30% 23%
37%
25%
58% 88%
35%
38% 75% 43% 24%
50%
Assessment of variability level
20
Test datasets
Cotton Tomato Chickpea Stressed
cotton Chickpea on
22.05.15
30 samples
22.05.15
(flowering)
07.06.15
(flowering/fr
uit
formation)
28.07.15
(ripening)
40 samples
22.05.15
(vegetative)
07.06.15
(vegetative)
28.07.15
(flowering)
31.08.15
(fruit
formation)
30 samples
22.01.15
(emergence)
15.03.15
(vegetative)
19.04.15
(flowering)
18 samples
22.05.15
(vegetative,
with stress
signs)
10 samples
22.05.15
(flowering/
fruit
formation)
Variability in health and stressed crops
21
0%
20%
40%
60%
80%
100%
PSSR
0%
20%
40%
60%
80%
100%
LAIc
0%
20%
40%
60%
80%
100%
GARI
0%
20%
40%
60%
80%
100%
NDVI
0%
20%
40%
60%
80%
100%
MTVI
Cotton
Tomato
Chickpea
Stressed
cotton
Chickpea on
22.05.15
Water-stress detection
22
Satellite imagery Field spectroscopy
Estimation growth rate
(LAI, green biomass,
crop coefficient)
Compression with
reference values
Calculation VIs
(photosynthesis rate,
pigmentation) and their
variability
Assessment the
absolute values
23
Irrigation optimization by spectral data
23
Inputs
Crop
Soil properties
Weather
Type of irrigation
etc.
Irrigation
schedule Analysis
Field feedback
by spectral data
Detected stress by field spectroscopy
Water requirement by spectral image
top related