Estimating crop biomass in smallholder fields with very high resolution imagery

Remote Sensing – Beyond Images WorkshopMexico City – 15 Dec. 2013

P.S. Traore, S.S. Traore, K. Goita, W.M. Bostick, J. Koo

Dryland Systems of West Africa

Possible intensification pathways

Large cities andhigh rural densities

‘Bhoo Chetanaintensification

pathway’?

Large cities andlow rural densities‘Fazendaintensificationpathway’?

• Human and animal population growth• Changes in dietary preferences• Crop-livestock integration• C sequestration• Bio-fuels

Opportunities in biomass productionNoFertNoResidue PK + Residue

M9D3 STAM 59A CSM388 ObatanpaMillet Cotton Sorghum Maize

Yield Biomass Yield Biomass Yield Biomass Yield Biomass2009 1450 5000 1300 2000 1276 4880 2100 39002010 1130 7900 1500 2700 1144 7680 1800 31502011 1150 9900 1300 2550 2112 8100 2600 3450

• 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

Challenges of biomass estimation

WBSs2DimabiTolonNRGhana

25NOV12

9,081proto-plotsextracted(~91/km2)

Smallholder systems metrics

© DigitalGlobeWorldView28-band50cm PAN200cm MUL

WBSt2NanposelaKoutialaSikassoMali

26OCT12

7,399proto-plots extracted(~38/km2)

Smallholder systems metrics

© DigitalGlobeWorldView28-band50cm PAN200cm MUL

WBSt1SukumbaKoutialaSikassoMali

26OCT12

5,580proto-plots extracted(~38/km2)

Smallholder systems metrics

© DigitalGlobeWorldView28-band50cm PAN200cm MUL

Smallholder systems metrics

Locally dominant crops – cotton belt, Mali

Land use survey, Aboveground biomass measurements

Class Number of samples

Bare Soil 10Cotton 154Grass + pasture + fallow 32Groundnut / legumes 32Maize 51Millet 104Rock Outcrops 2Sorghum 51Wetland + ponds 15Wild vegetation 21

total 472

Number of fields

Crop Age (Day) Crop Biomass (d[DW] m-2)

Avg. Stdev CV (%) Avg Stdev CV (%)Cotton 8 96 3 3 110 69 63Maize 9 78 7 9 143 71 50Millet 8 98 4 4 181 118 65

Sorghum 9 77 6 8 114 71 62Total 34 86 11 13 136 85 62

Biomass-NDVI relationship, crop & sensor-wiseCoton: biomasse=f(NDVI), n=12

R2QB = 0.653

R2SP = 0.716

R2AL = 0.763

R2MD = 0.538

0

100

200

300

400

0.3 0.4 0.5 0.6 0.7

Maïs: biomasse=f(NDVI), n=9

r2QB = 0.366

r2SP = 0.316

r2AL = 0.303

r2MD = 0.148

0

100

200

300

400

0.2 0.3 0.4 0.5 0.6

Sorgho: biomasse=f(NDVI), n=9

R2QB = 0.544

R2SP = 0.389

R2AL = 0.204

R2MD = 0.191

0

100

200

300

0.2 0.3 0.4 0.5 0.6

Mil: biomasse=f(NDVI), n=11

R2QB = 0.702

R2SP = 0.697

R2AL = 0.421

R2MD = 0.440

0

100

200

300

400

0.2 0.3 0.4 0.5 0.6

1000

500

0

QuickBird SPOT ASTER MODISAggr

egat

ed b

iom

ass

estim

ate

(met

ric to

ns)

u=1, aucune connaissance de l’utilisation des terres a prioriu=2, coton et céréales séparéesu=4, coton, maïs, mil, sorgho séparés

u=1

u=2

u=4

Aggregate biomass estimate(co 187, ml 132, mz 63, sg 88)

u=1, no a priori knowledge of land useu=2, cotton and cereals separatedu=4, cotton, maize, millet, sorghum separated

Measured and predicted crop biomass

Contour ridgetillage effectson yield, biomass

Contour ridge tillage effects on NDVI• 38 field pairsmonitored (samecatena class, samefarmer, contiguous,trees removed)• Stdev(NDVI) differsin 82% of pairs (50% inCRT fields)• Mean NDVI differs in87% of pairs (55% in CRTfields)

• 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

Learnings

ICRISAT is a member of the CGIAR Consortium

Thank you!