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

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Remote sensing –Beyond images ...

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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  • Many authors have found significant correlations between NDVI and wheat yield in the past.
  • Positive linear trend
  • 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.
  • Positive linear trend
  • Positive linear trend
  • Positive linear trend
  • Positive linear trend
  • Positive linear trend
  • Positive linear trend
  • - Area adjustment using standard approach based on regression estimator
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.
  • Area: fields are small and complex, MODIS resolution is inadequate, moving to finer resolution.For now using area numbers from the CRS.

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

  • 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!