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
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Forecasting Wheat Yield and Production for Punjab Province, Pakistan from Satellite Image Time Series
Remote sensing –Beyond images Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
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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 SciencesUniversity of Maryland, United States
Beyond Diagnostics: Insights and Recommendations from Remote Sensing Workshop at CIMMYT 2013 in Texcoco, Mexico 14-15 December 2013
Food Crop Production in PakistanWinter Season (Rabi) % of Total
Wheat
70%
Potatoe11%
Fruits9%
Vegeta-bles5%
Other5%
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 Multi-temporal 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)
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.
Year
Wheat Mask and Area from 250 m MODISMulti-Temporal Landsat
1. Early growing season2. Height of growing season3. 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 4-5-3 (green vegetation appears red)
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 4-5-3 (green vegetation appears red)
Visual Interpretation of Wheat Areas
Training(12. Apr. 2012)Landsat-7 ETM scene for Punjab
Band combination 4-5-3 (green vegetation appears red)
Select Wheat Training Areas
Classification(12. Apr. 2012)Landsat-7 ETM scene for Punjab
Band combination 4-5-3 (green vegetation appears red)
Classify for Wheat Areas
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
Pun
jab
Sindh
Landsat training scenes
WRS2 Path/Row Grid
Wheat Mask and Area from 250 m MODISMulti-Temporal Landsat
1. Early growing season2. Height of growing season3. After harvest
Classify Landsat• Select training data visually• Bagged decision trees
Aggregate to 250 m resolution
Wheat Mask and Area from 250 m MODISMulti-Temporal Landsat
1. Early growing season2. Height of growing season3. After harvest
Classify Landsat• Select training data visually• Bagged decision trees
Aggregate to 250 m resolution
MODIS 250 m surface reflectance 8-day composites time series bands 1,
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
Classify MODIS time series• Bagged decision trees
Percent wheat per 250 m pixel for Punjab Province
Aggregate to 250 m resolution
Percent Wheat for Punjab
Province Rabi Season
2010/11
Derived from MODIS 250 m 8-day composite surface reflectance time series
Percent wheatper pixel
MODIS 8-day composites
Wheat Yield and Production Forecast
Calculate spatial average of NDVI, weighted by percent wheat
Historic reported yield
Regression-based wheat model yield against 95th NDVI percentile
Regression estimator of pixel counts against reported area
Select 20% highest density wheat pixels
Multiply area forecast with yield forecast to obtain production forecast
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
SANDVI
VCI
WDRVI
Forecast Wheat Production per District for Punjab Province, Pakistan, Seasons 2008/09 to 2011/12
2008/09 2009/10
2010/11 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. Whether, how, and with which datasets can we produce national-scale cropland layers for smallholder agriculture?
2. How can smallholder agricultural fields be sampled and monitored through remote sensing?
3. How can agricultural areas be monitored at the national scale in near-realtime?
4. How can we inform decision makers?5. What are the pathways to reach smallholder