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Forecasting Wheat Yield and
Production for Punjab Province,
Pakistan from Satellite Image
Time Series
Jan Dempewolf, Inbal Becker-Reshef, Bernard
Adusei, Matt Hansen, Peter Potapov, Brian Barker,
Chris Justice
Department of Geographical Sciences
University of Maryland, United States
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Workshop at CIMMYT 2013 in Texcoco, Mexico 14-15 December 2013
Pakistan: Strengthening Provincial Capacity
(USDA funded, collaboration between USDA, FAO, SUPARCO, CRS Pakistan, & UMD)

Training Workshops
GLAM-Pakistan Agricultural Monitoring System
Food Crop Production in Pakistan
Winter Season (Rabi) % of Total
Vegetables
5%

Other
5%

Fruits
9%

Potatoe
11%

Wheat
70%

Data source: Crop Reporting Service of the Government of Punjab,
Pakistan, www.agripunjab.gov.pk
Total wheat dry matter and NDVI in Maryland, USA
(Tucker et al., 1981)

Tucker, C. J., B. N. Holben, J.
H. Elgin Jr, and J. E. McMurtrey
III. “Remote Sensing of Total
Dry-matter Accumulation in
Winter Wheat.” Remote
Sensing of Environment 11
(1981): 171–189.
Wheat yield and AVHRR-NDVI integrated over the
growing season in Montana, USA (Labus et al., 2002)

Labus, M. P., G. A. Nielsen,
R. L. Lawrence, R. Engel,
and D. S. Long. “Wheat Yield
Estimates Using Multitemporal NDVI Satellite
Imagery.” International
Journal of Remote Sensing
23, no. 20 (January 2002):
4169–4180.
Reported wheat yield and predicted yield from
MODIS-NDVI in Shandong, China (Ren et al., 2008)

Ren, J., Z. Chen, Q. Zhou, and
H. Tang. “Regional Yield
Estimation for Winter Wheat with
MODIS-NDVI Data in Shandong,
China.” International Journal of
Applied Earth Observation and
Geoinformation 10, no. 4
(December 2008): 403–413.
MODIS-NDVI and Wheat Yield in Kansas, USA
(Becker-Reshef et al., 2010)
Daily Normalized Difference Vegetation Index (NDVI
from MODIS) 2000-2008, Harper County
Blue numbers are Yield (MT/Ha)
Winter Wheat emergence
NDVI peak
2.35

Winter Wheat seasonal
NDVI peak
2.69

3.36

2.54

2.49

2.49

2.21

1.61

1.4
8

Year

Strong correlation between NDVI Peak and yield
Becker-Reshef, I., E. Vermote, M. Lindeman, and C. Justice. “A Generalized Regression-based Model for Forecasting Winter Wheat Yields in Kansas
and Ukraine Using MODIS Data.” Remote Sensing of Environment 114, no. 6 (2010): 1312–1323.
Wheat Mask and Area from 250 m MODIS
Multi-Temporal Landsat
1. Early growing season
2. Height of growing season
3. After harvest

Classify Landsat
•
•

Select training data visually
Bagged decision trees
Visual Interpretation of Wheat Areas
Early Season
(8. Feb. 2012)
Landsat-7 ETM
scene for
Punjab
Band
combination 45-3 (green
vegetation
appears red)
Visual Interpretation of Wheat Areas
Near Peak
(24. Feb. 2012)
Landsat-7 ETM
scene for
Punjab
Band
combination
4-5-3 (green
vegetation
appears red)
Visual Interpretation of Wheat Areas
Harvest
(4. Apr. 2012)
Landsat-7 ETM
scene for
Punjab
Band
combination 45-3 (green
vegetation
appears red)
Select Wheat Training Areas
Training
(12. Apr. 2012)
Landsat-7 ETM
scene for
Punjab
Band
combination
4-5-3 (green
vegetation
appears red)
Classify for Wheat Areas
Classification
(12. Apr. 2012)
Landsat-7 ETM
scene for
Punjab
Band
combination
4-5-3 (green
vegetation
appears red)
Wheat Mask
Classification
(Rabi 2012)
Landsat-7
ETM scene
for Punjab
Band
combination
4-5-3 (green
vegetation
appears red)
Landsat Training Scenes for Wheat Area
Pakistan

Landsat
training
scenes

Sindh

WRS2
Path/Row
Grid
Wheat Mask and Area from 250 m MODIS
Multi-Temporal Landsat
1. Early growing season
2. Height of growing season
3. After harvest

Classify Landsat
•
•

Select training data visually
Bagged decision trees

Aggregate to 250 m
resolution
Wheat Mask and Area from 250 m MODIS
Multi-Temporal Landsat
1. Early growing season
2. Height of growing season
3. After harvest

Classify Landsat
•
•

Select training data visually
Bagged decision trees

Aggregate to 250 m
resolution

MODIS 250 m surface reflectance 8day composites time series bands 1,
2, 5, 7 (red, nir, swir, therm)
1. 1. Dec. – 26th Feb.
2. QA Filter (clouds, etc.)
3. Calculate NDVI
Wheat Mask and Area from 250 m MODIS
Multi-Temporal Landsat
1. Early growing season
2. Height of growing season
3. After harvest

MODIS 250 m surface reflectance 8day composites time series bands 1,
2, 5, 7 (red, nir, swir, therm)
1. 1. Dec. – 26th Feb.
2. QA Filter (clouds, etc.)
3. Calculate NDVI

Classify Landsat
•
•

Select training data visually
Bagged decision trees

Aggregate to 250 m
resolution

Convert to 588 metrics per season
•
•
•

0th, 10th, 25th, 50th, 75th, 90th, 100th
percentiles
Means of sequential percentiles and
their differences
Band values ranked by other bands
Wheat Mask and Area from 250 m MODIS
Multi-Temporal Landsat
1. Early growing season
2. Height of growing season
3. After harvest

MODIS 250 m surface reflectance 8day composites time series bands 1,
2, 5, 7 (red, nir, swir, therm)
1. 1. Dec. – 26th Feb.
2. QA Filter (clouds, etc.)
3. Calculate NDVI

Classify Landsat
•
•

Select training data visually
Bagged decision trees

Aggregate to 250 m
resolution

Classify MODIS time series
•

Bagged decision trees

Convert to 228 metrics per season
•
•
•

0th, 10th, 25th, 50th, 75th, 90th, 100th
percentiles
Means of sequential percentiles and
their differences
Band values ranked by other bands

Percent wheat per 250 m
pixel for Punjab Province
Percent Wheat
for Punjab
Province Rabi
Season
2010/11

Derived from
MODIS 250 m
8-day
composite
surface
reflectance
time series
Wheat Yield and Production Forecast
Percent wheat
per pixel

MODIS 8-day
composites

Select 20%
highest density
wheat pixels

Calculate spatial
average of NDVI,
weighted by
percent wheat

Regression
estimator of pixel
counts against
reported area

Multiply area
forecast with yield
forecast to obtain
production forecast

Historic reported
yield

Regression-based
wheat model yield
against 95th NDVI
percentile
Timing of Forecast and Number of Training Years for
Punjab Province, Pakistan, 2010/11 Rabi Season

R2, RMSE at the district level and deviation (D) at the province level of forecast
versus reported yield for the 2010/11 Rabi season.
Left: Changes through the cropping season. Right: Number of training years.
Performance of Vegetation Indices for Forecasting
Wheat Yield for the 2010/11 and 2011/12 Rabi Seasons
NDVI

VCI

WDRVI

SANDVI
Forecast Wheat Production per District for
Punjab Province, Pakistan, Seasons 2008/09 to 2011/12
2008/09

2010/11

2009/10

2011/12
Remote Sensing Applications for
Smallholder Farming Systems in Tanzania
(Proposed Project)
Explore feasible pathways to use remote sensing
tools for smallholder agriculture:







Improve crop condition monitoring by the National Food
Security Office (NFSO).
Produce current cropland extent core dataset.
Support agricultural extension through Sokoine University.
Monitor crop condition of smallholder agricultural areas.
Assess distribution of smallholder cropping systems and crop
types.
Primary Use-Case Challenges
1.

2.

3.

4.

5.

Whether, how, and with which datasets can we
produce national-scale cropland layers for
smallholder agriculture?
How can smallholder agricultural fields be
sampled and monitored through remote sensing?
How can agricultural areas be monitored at the
national scale in near-realtime?
How can we inform decision makers?
What are the pathways to reach smallholder
farmers?
Remote Sensing Systems
MODIS

Satellite Time
Series Pipeline
and Archive

Landsat
RapidEye/
PlanetLabs
UAV
Field Data
Test
Sites

(

)
Time Series
(one season)
Groundtruth landcover and
land-cover
dynamics

Rela ve NDVI /
Crop Condi on at
MODIS and Landsat
resolu on
Prototype of
Agricultural Areas Base
Map (Cropland Mask)
Methodologies for
classifying
• Cropland
• Maize produc on
systems
Thank You!

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Forecasting Wheat Yield and Production for Punjab Province, Pakistan from Satellite Image Time Series

  • 1. Forecasting Wheat Yield and Production for Punjab Province, Pakistan from Satellite Image Time Series Jan Dempewolf, Inbal Becker-Reshef, Bernard Adusei, Matt Hansen, Peter Potapov, Brian Barker, Chris Justice Department of Geographical Sciences University of Maryland, United States Beyond Diagnostics: Insights and Recommendations from Remote Sensing Workshop at CIMMYT 2013 in Texcoco, Mexico 14-15 December 2013
  • 2. Pakistan: Strengthening Provincial Capacity (USDA funded, collaboration between USDA, FAO, SUPARCO, CRS Pakistan, & UMD) Training Workshops
  • 4. Food Crop Production in Pakistan Winter Season (Rabi) % of Total Vegetables 5% Other 5% Fruits 9% Potatoe 11% Wheat 70% Data source: Crop Reporting Service of the Government of Punjab, Pakistan, www.agripunjab.gov.pk
  • 5. Total wheat dry matter and NDVI in Maryland, USA (Tucker et al., 1981) Tucker, C. J., B. N. Holben, J. H. Elgin Jr, and J. E. McMurtrey III. “Remote Sensing of Total Dry-matter Accumulation in Winter Wheat.” Remote Sensing of Environment 11 (1981): 171–189.
  • 6. Wheat yield and AVHRR-NDVI integrated over the growing season in Montana, USA (Labus et al., 2002) Labus, M. P., G. A. Nielsen, R. L. Lawrence, R. Engel, and D. S. Long. “Wheat Yield Estimates Using Multitemporal NDVI Satellite Imagery.” International Journal of Remote Sensing 23, no. 20 (January 2002): 4169–4180.
  • 7. Reported wheat yield and predicted yield from MODIS-NDVI in Shandong, China (Ren et al., 2008) Ren, J., Z. Chen, Q. Zhou, and H. Tang. “Regional Yield Estimation for Winter Wheat with MODIS-NDVI Data in Shandong, China.” International Journal of Applied Earth Observation and Geoinformation 10, no. 4 (December 2008): 403–413.
  • 8. MODIS-NDVI and Wheat Yield in Kansas, USA (Becker-Reshef et al., 2010) Daily Normalized Difference Vegetation Index (NDVI from MODIS) 2000-2008, Harper County Blue numbers are Yield (MT/Ha) Winter Wheat emergence NDVI peak 2.35 Winter Wheat seasonal NDVI peak 2.69 3.36 2.54 2.49 2.49 2.21 1.61 1.4 8 Year Strong correlation between NDVI Peak and yield Becker-Reshef, I., E. Vermote, M. Lindeman, and C. Justice. “A Generalized Regression-based Model for Forecasting Winter Wheat Yields in Kansas and Ukraine Using MODIS Data.” Remote Sensing of Environment 114, no. 6 (2010): 1312–1323.
  • 9. Wheat Mask and Area from 250 m MODIS Multi-Temporal Landsat 1. Early growing season 2. Height of growing season 3. After harvest Classify Landsat • • Select training data visually Bagged decision trees
  • 10. Visual Interpretation of Wheat Areas Early Season (8. Feb. 2012) Landsat-7 ETM scene for Punjab Band combination 45-3 (green vegetation appears red)
  • 11. Visual Interpretation of Wheat Areas Near Peak (24. Feb. 2012) Landsat-7 ETM scene for Punjab Band combination 4-5-3 (green vegetation appears red)
  • 12. Visual Interpretation of Wheat Areas Harvest (4. Apr. 2012) Landsat-7 ETM scene for Punjab Band combination 45-3 (green vegetation appears red)
  • 13. Select Wheat Training Areas Training (12. Apr. 2012) Landsat-7 ETM scene for Punjab Band combination 4-5-3 (green vegetation appears red)
  • 14. Classify for Wheat Areas Classification (12. Apr. 2012) Landsat-7 ETM scene for Punjab Band combination 4-5-3 (green vegetation appears red)
  • 15. Wheat Mask Classification (Rabi 2012) Landsat-7 ETM scene for Punjab Band combination 4-5-3 (green vegetation appears red)
  • 16. Landsat Training Scenes for Wheat Area Pakistan Landsat training scenes Sindh WRS2 Path/Row Grid
  • 17. Wheat Mask and Area from 250 m MODIS Multi-Temporal Landsat 1. Early growing season 2. Height of growing season 3. After harvest Classify Landsat • • Select training data visually Bagged decision trees Aggregate to 250 m resolution
  • 18. Wheat Mask and Area from 250 m MODIS Multi-Temporal Landsat 1. Early growing season 2. Height of growing season 3. After harvest Classify Landsat • • Select training data visually Bagged decision trees Aggregate to 250 m resolution MODIS 250 m surface reflectance 8day composites time series bands 1, 2, 5, 7 (red, nir, swir, therm) 1. 1. Dec. – 26th Feb. 2. QA Filter (clouds, etc.) 3. Calculate NDVI
  • 19. Wheat Mask and Area from 250 m MODIS Multi-Temporal Landsat 1. Early growing season 2. Height of growing season 3. After harvest MODIS 250 m surface reflectance 8day composites time series bands 1, 2, 5, 7 (red, nir, swir, therm) 1. 1. Dec. – 26th Feb. 2. QA Filter (clouds, etc.) 3. Calculate NDVI Classify Landsat • • Select training data visually Bagged decision trees Aggregate to 250 m resolution Convert to 588 metrics per season • • • 0th, 10th, 25th, 50th, 75th, 90th, 100th percentiles Means of sequential percentiles and their differences Band values ranked by other bands
  • 20. Wheat Mask and Area from 250 m MODIS Multi-Temporal Landsat 1. Early growing season 2. Height of growing season 3. After harvest MODIS 250 m surface reflectance 8day composites time series bands 1, 2, 5, 7 (red, nir, swir, therm) 1. 1. Dec. – 26th Feb. 2. QA Filter (clouds, etc.) 3. Calculate NDVI Classify Landsat • • Select training data visually Bagged decision trees Aggregate to 250 m resolution Classify MODIS time series • Bagged decision trees Convert to 228 metrics per season • • • 0th, 10th, 25th, 50th, 75th, 90th, 100th percentiles Means of sequential percentiles and their differences Band values ranked by other bands Percent wheat per 250 m pixel for Punjab Province
  • 21. Percent Wheat for Punjab Province Rabi Season 2010/11 Derived from MODIS 250 m 8-day composite surface reflectance time series
  • 22. Wheat Yield and Production Forecast Percent wheat per pixel MODIS 8-day composites Select 20% highest density wheat pixels Calculate spatial average of NDVI, weighted by percent wheat Regression estimator of pixel counts against reported area Multiply area forecast with yield forecast to obtain production forecast Historic reported yield Regression-based wheat model yield against 95th NDVI percentile
  • 23. Timing of Forecast and Number of Training Years for Punjab Province, Pakistan, 2010/11 Rabi Season R2, RMSE at the district level and deviation (D) at the province level of forecast versus reported yield for the 2010/11 Rabi season. Left: Changes through the cropping season. Right: Number of training years.
  • 24. Performance of Vegetation Indices for Forecasting Wheat Yield for the 2010/11 and 2011/12 Rabi Seasons NDVI VCI WDRVI SANDVI
  • 25. Forecast Wheat Production per District for Punjab Province, Pakistan, Seasons 2008/09 to 2011/12 2008/09 2010/11 2009/10 2011/12
  • 26. Remote Sensing Applications for Smallholder Farming Systems in Tanzania (Proposed Project) Explore feasible pathways to use remote sensing tools for smallholder agriculture:      Improve crop condition monitoring by the National Food Security Office (NFSO). Produce current cropland extent core dataset. Support agricultural extension through Sokoine University. Monitor crop condition of smallholder agricultural areas. Assess distribution of smallholder cropping systems and crop types.
  • 27. Primary Use-Case Challenges 1. 2. 3. 4. 5. Whether, how, and with which datasets can we produce national-scale cropland layers for smallholder agriculture? How can smallholder agricultural fields be sampled and monitored through remote sensing? How can agricultural areas be monitored at the national scale in near-realtime? How can we inform decision makers? What are the pathways to reach smallholder farmers?
  • 28. Remote Sensing Systems MODIS Satellite Time Series Pipeline and Archive Landsat RapidEye/ PlanetLabs UAV Field Data Test Sites ( ) Time Series (one season) Groundtruth landcover and land-cover dynamics Rela ve NDVI / Crop Condi on at MODIS and Landsat resolu on Prototype of Agricultural Areas Base Map (Cropland Mask) Methodologies for classifying • Cropland • Maize produc on systems

Hinweis der Redaktion

  1. Many authors have found significant correlations between NDVI and wheat yield in the past.
  2. Positive linear trend
  3. Examined seasonal growth pro􏰜 les developed from AVHRR-NDVI for estimating wheat yield at regional and farm scales in Montana for the years 1989–1997.
  4. Positive linear trend
  5. Positive linear trend
  6. Positive linear trend
  7. Positive linear trend
  8. Positive linear trend
  9. Positive linear trend
  10. - Area adjustment using standard approach based on regression estimator
  11. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  12. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  13. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  14. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  15. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  16. Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.