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1 Survey Report_Riska_230717.pptx
1. CASSAVA YIELD PREDICTION
USING VEGETATION INDICES
FROM SENTINEL DATA
SATELLITE
Riska Ayu Purnamasari(1), Tofael Ahamed(2) and Ryozo Noguchi(2)
(1)Graduate School of Life and Environmental Science, University of
Tsukuba
(2)Faculty of Life and Environmental Science, University of Tsukuba
2.
3. Cassava Yield Prediction
■ Early crop yield forecasting is of vital importance
and may help policy makers and farmer in planning
and management.
■ Analysis of vegetation and detection of changes in
vegetation patterns are important monitoring
method to observe health and productivity can be
reflect from condition of crop canopy, soil, and yield.
■ Data on cultivated surfaces and yields are an
essential prerequisite for a good agricultural
production forecast
■ Predicting crop yield using remote sensing data
products often depends on an empirical approach
that relates VIs alone or in combination with remote-
sensing–derived meteorological variables to the
reported crop yields.
4. Yield Prediction Parameter
Biophysical
Parameter
■ Chlorophyll Content: Leaf
chlorophyll content is an
important variable for agricultural
remote sensing because of its
close relationship to leaf nitrogen
content
■ Leaf Area Index (LAI): Leaf Area
Index (LAI) is the total one-sided
(or one half of the total all-sided)
green leaf area per unit ground-
surface area.
■ Normalized Difference
Vegetation Index : Combination
of red and NIR
Vegetation Index
■ Soil-Adjusted Vegetation Index:
The SAVI is structured similar to
the NDVI but with the addition of a
“soil brightness correction factor
7. LOCAL FOOD GROWING SEASONS IN BANTEN
PROVINCE, INDONESIA
(Farmer Survey, 2016)
8. Sentinel-2
■ Carries a Multi-Spectral Imager (MSI) with a swath of 290 km
■ Provides a set of 13 spectral bands spanning from the visible and near
infrared to the shortwave infrared
■ Featuring four bands at 10 m, six bands at 20 m and three bands at 60 m
spatial resolution
■ S2 satellites will deliver data taken over all land surfaces and coastal zones
every five days under cloud-free conditions, and typically every 15–30 days
considering the presence of clouds
10. Survey for Cassava Field Availability
(22-23 July 2017)
Lebak,
Banten
Serang,
Banten
11. Sampling Strategy for In-situ Cassava
Yield
• One fields considered as sample fields for this study that harvest around August
2017
• In each of the four study fields, stratified random sampling-based 18 points (10 for
model generation and 8 for validation) determined for in-situ cassava yield data
collection
• Intensive fieldwork from July 4th until 8th , 2017 carried out 2 to 3 days prior to the
harvest time of each field to assure the steadiness of crop status.
• In-situ collection of cassava yield (actual yield) achieved by harvesting cassava over
a 10 m2 area at each sampling point (considered with Sentinel-2 datasets pixel)
12. Field Survey and Data Collection
With surveyors and farmer
Measuring for 10 m area
13. Field Survey and Data Collection
Measuring Actual Yield Cassava Harvesting
27. Time series of sentinel-2 datasets reflectance use NDVI (a) and LAI (b) during the growing
season of Cassava
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
26 96 176 191 248
NDVI
Day After Planting (DAP)
0
0.5
1
1.5
2
2.5
3
26 96 176 191 248
LAI
Days After Planting (DAP)
Cassava Growing Season
28. Index
Days After Planting (DAP)
26 96 176 191 248
NDVI -0.35 0.57 0.47 0.28 0.47
LAI -0.18 0.15 0.09 0.33 0.44
Correlation coefficients calculated between hyperspectral indices and within-field yield data for all
images acquired during the growing season.
29. Correlation between actual and predicted
yield
y = 0.3268x + 35.64
R² = 0.57
0
10
20
30
40
50
60
70
0 20 40 60 80 100
PREDICTED
YIELD
ACTUAL YIELD
NDVI Based Equation
y = 0.1927x + 42.73
R² = 0.43
0
10
20
30
40
50
60
70
0 20 40 60 80 100
PREDICTED
YIELD
ACTUAL YIELD
LAI Based Equation
y = 0.441x + 29.599
R² = 0.67
0
10
20
30
40
50
60
70
80
0 20 40 60 80 100
PREDICTED
YIELD
ACTUAL YIELD
NDVI&LAI Based Equation
The best fit equations used for the prediction of cassava yield
Single Regression R2 Multiple Regression R2
NDVI y = 385.88NDVI - 89.694 0.57
y = 342.41NDVI +41.47LAI - 167.08 0.65
LAI y = 52.914LAI - 66.31 0.44
Validation Prediction Model
30. Next Plan
■ Calculate SAVI
■ Calculate Chlorophyll content
■ Build Growth Model with DSSAT
