A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support...

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A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support Analysis Data @ 30 m (GFSAD30) 6/25/14

Transcript of A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support...

Page 1: A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support Analysis Data @ 30 m (GFSAD30) 6/25/14.

A 30-m approach to cropland extent

Cristina MilesiFourth Workshop

OnGlobal Food Security-Support Analysis Data @ 30 m

(GFSAD30)

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Page 2: A 30-m approach to cropland extent Cristina Milesi Fourth Workshop On Global Food Security-Support Analysis Data @ 30 m (GFSAD30) 6/25/14.

The Linear Spectral Mixture Model• The integrated reflectance of a spectrally

heterogeneous surface can be described as a linear combination of spectral endmembers

• Inverting a standardized linear mixture model converts the Landsat reflectance to subpixel fractions estimates of Substrate (S), Vegetation (V) and Dark (D) materials

• Seasonal variability of the fractions is a physically based approach for characterizing surfaces

D

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The Landsat ETM+ Global Mixing Space• 100 Environments• 100,000,000 spectra• 3 dimensions contain > 98%

of the variance

Small and Milesi, 2013 RSE6/25/14

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Validation with Ground Observations

• 74 ground measurements of Fc• 19 seasonal and perennial crops• 11 dates between April and

October 2008 coincidental with Landsat acquisitions

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Approach– Multi-season characterization of the

landscape through ~4-7 images within 1-2 yrs (peak of growing season, pre-harvest, post-harvest)

– Unmixing to SVD (Substrate/Vegetation/Dark)– Standard deviation of seasonal SVD

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Multi-seasonal TOA TM/ETM

Unmixed multi-seasonal SVD TM/ETM

Standard Deviation of Unmixed SVD TM/ETM

t1t2t3…tn

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Main Combinations

• Cropland: high vegetation/soil variability• Fallow: high substrate with low variability

(vegetation fraction close to zero)• Rangeland: low vegetation and low substrate

fractions, low variability• Forest, wetlands: high vegetation, high dark

fractions, low variability

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Proof of Concept

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Cropland

Substrate

Vege

tatio

n

15-Nov-1017-Dec-107-Mar-118-Apr-1113-Jul-11

15-Sep-11

Landsat TM 5 Dates

Central Tunisia

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Natural Vegetation/Cropland

15-Nov-1017-Dec-107-Mar-118-Apr-1113-Jul-11

15-Sep-11

Landsat TM 5 Dates

Central Tunisia

Substrate

Vege

tatio

n

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Natural Vegetation/Cropland

15-Nov-1017-Dec-107-Mar-118-Apr-1113-Jul-11

15-Sep-11

Landsat TM 5 Dates

Central Tunisia

Substrate

Vege

tatio

n

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26-Oct-08 21-May-09 16-Jan-10 5-Mar-10 2-Dec-10 9-Apr-11 10-Jun-13

Cropland

Landsat ETM Dates

Western Nigeria

Substrate

Vege

tatio

n

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26-Oct-08 21-May-09 16-Jan-10 5-Mar-10 2-Dec-10 9-Apr-11 10-Jun-13

Natural Vegetation

Landsat ETM Dates

Western Nigeria