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

Training Workshops

Pakistan: Strengthening Provincial Capacity (USDA funded, collaboration between USDA, FAO, SUPARCO, CRS Pakistan, & UMD)

GLAM-Pakistan Agricultural Monitoring System

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)

Scatter Plot

YrDoy (Doy)

ND

VI

55 263 106 314 157 365 208 51 259 102 310 153 361 204 47

2000 2001 2002 2003 2004 2005 2006 2007 2008

Scatter Plot

YrDoy (Doy)

ND

VI

0.8

0.75

0.7

0.65

0.6

0.55

0.5

0.45

0.4

0.35

0.3

0.25

Harper

Logan

Data limited by:Marking:

Marking

Marker by(Row Number)

2.35 2.54

2.21

3.362.49

2.69

1.611.48

2.49

Winter Wheat emergence NDVI peak

Winter Wheat seasonal NDVI peak

Strong correlation between NDVI Peak and yield

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,

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 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,

2, 5, 7 (red, nir, swir, therm)1. 1. Dec. – 26th Feb.2. QA Filter (clouds, etc.)3. Calculate NDVI

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 MODISMulti-Temporal Landsat

1. Early growing season2. Height of growing season3. After harvest

Classify Landsat• Select training data visually• Bagged decision trees

MODIS 250 m surface reflectance 8-day 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

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

farmers?

Remote Sensing Systems

Thank You!