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Estimating crop biomass in smallholder fields with very high resolution imagery

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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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Estimating crop biomass in smallholder fields with very high resolution imagery

  1. 1. P.S. Traore, S.S. Traore, K. Goita, W.M. Bostick, J. Koo Estimating crop biomass in smallholder fields with very high resolution imagery Remote Sensing – Beyond Images Workshop Mexico City – 15 Dec. 2013
  2. 2. Dryland Systems of West Africa
  3. 3. Possible intensification pathways Large cities and high rural densities ‘Bhoo Chetana intensification pathway’? Large cities and low rural densities ‘Fazenda intensification pathway’?
  4. 4. Opportunities in biomass production • • • • • NoFertNoResidue Human and animal population growth Changes in dietary preferences Crop-livestock integration C sequestration Bio-fuels 2009 2010 M9D3 Millet Biomas Yield s 1450 5000 1130 7900 STAM 59A Cotton Yield 1300 1500 Biomass 2000 2700 CSM388 Sorghum Biomas Yield s 1276 4880 1144 7680 PK + Residue Obatanpa Maize Yield 2100 1800 Biomass 3900 3150
  5. 5. Challenges of biomass estimation • • • • • • • • • Canopy height and optical signal saturation Tropical cloud cover Heterogeneous field size and geometries Mixed crops and trees in fields Spread of planting dates & phenologies Heterogeneous soil properties at sub-field scale & heterogenous stand conditions Lack of historical calibration data Lack of commercial seed systems Dynamic inter-annual land tenure / use & field boundaries
  6. 6. Smallholder systems metrics WBSs2 Dimabi Tolon NR Ghana 25NOV12 9,081 proto-plots extracted (~91/km2) © DigitalGlobe WorldView2 8-band 50cm PAN 200cm MUL
  7. 7. Smallholder systems metrics WBSt2 Nanposela Koutiala Sikasso Mali 26OCT12 7,399 proto-plots extracted (~38/km2) © DigitalGlobe WorldView2 8-band 50cm PAN 200cm MUL
  8. 8. Smallholder systems metrics WBSt1 Sukumba Koutiala Sikasso Mali 26OCT12 5,580 proto-plots extracted (~38/km2) © DigitalGlobe WorldView2 8-band 50cm PAN 200cm MUL
  9. 9. Smallholder systems metrics
  10. 10. Locally dominant crops – cotton belt, Mali
  11. 11. Land use survey, Aboveground biomass measurements Crop Age (Day) Number of fields Cotton 8 Maize 9 Millet 8 Sorghum 9 Total 34 Avg. 96 78 98 77 86 Stdev 3 7 4 6 11 Class Crop Biomass (d[DW] m-2) CV (%) Avg 3 110 9 143 4 181 8 114 13 136 Number of samples Bare Soil Cotton Grass + pasture + fallow Groundnut / legumes Maize Millet Rock Outcrops Sorghum Wetland + ponds Wild vegetation 10 154 32 32 51 104 2 51 15 21 total 472 Stdev 69 71 118 71 85 CV (%) 63 50 65 62 62
  12. 12. Biomass-NDVI relationship, crop & sensor-wise Co to n: bio masse=f(NDVI), n=1 2 400 R 2QB = 0.653 M aïs: bio masse=f(NDVI), n=9 400 r2QB = 0.366 R 2SP = 0.71 6 300 R 2AL = 0.763 r2SP = 0.31 6 300 r2AL = 0.303 R 2MD = 0.538 r2MD = 0.1 48 200 200 1 00 1 00 0 0 0.3 0.4 0.5 0.6 0.7 0.2 0.3 0.4 M il: bio masse=f(NDVI), n=1 1 400 R 2QB = 0.702 0.5 0.6 So rgho : bio masse=f(NDVI), n=9 300 R 2QB = 0.544 R 2SP = 0.389 R 2SP = 0.697 R 2AL = 0.204 R 2AL = 0.421 300 R 2MD = 0.440 R 2MD = 0.1 91 200 200 1 00 1 00 0 0 0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6
  13. 13. Aggregate biomass estimate (co 187, ml 132, mz 63, sg 88) Aggregated biomass estimate (metric tons) u=1, aucune connaissance de l’utilisation des terres a priori u=2, coton etu=1, no a séparées céréales priori knowledge of land use u=4, coton, maïs, mil, sorgho cereals separated u=2, cotton and séparés 1000 u=4, cotton, maize, millet, sorghum separated u=1 u=2 500 u=4 0 QuickBird SPOT ASTER MODIS
  14. 14. Measured and predicted crop biomass
  15. 15. Contour ridge tillage effects on yield, biomass
  16. 16. Contour ridge tillage effects on NDVI • 38 field pairs monitored (same catena class, same farmer, contiguous, trees removed) • Stdev(NDVI) differs in 82% of pairs (50% in CRT fields) • Mean NDVI differs in 87% of pairs (55% in CRT fields)
  17. 17. Learnings • • • • • • • Intra-specific variability in reflectance is larger than inter-specific variability (time-specific, with exceptions) Spatial uncertainty inherent to biomass predictions does not change significantly from 2 to 30m resolution (time-specific) RMSEP (DM) modestly decreases with model complexity Cloud cover remains a major constraint to peak biomass acquisitions Discriminating between cotton and cereals important for unbiased landscape-scale biomass estimates Tree management is independent of underlying crop type – tree mask required for crop recognition Stereoscopic (or lidar) monitoring of canopy height next quick & dirty improvement for biomass estimates
  18. 18. Thank you! ICRISAT is a member of the CGIAR Consortium

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