Post on 09-Apr-2019
On using the Landsat archive to map crop cover history across the United States
David M. Johnsondave.johnson@nass.usda.gov
Landsat Science Team Summer MeetingUniversity of Colorado, Boulder
August 8th, 2018
The Findings and Conclusions in This Preliminary Presentation Have Not Been Formally Disseminated by the U. S. Department of Agriculture and Should Not Be Construed to Represent
Any Agency Determination or Policy.
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CORN SOYBEANS WHEAT
Crop Area Recent History for the United States
Planted Area Equivalency
1984 2017
US Cropland Maps Back through Time?
1984: no 2017: yes
The last decade does exist via the NASS Cropland Data Layer
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2013
2010
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2008
Landsat History
30m CDL history
30m history yet to exploit
4 years of overlap with CDL history and Landsat 5!
But what did crop cover look like 1984 - 2007?
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Historical Crop Cover Mapping Methodology• Perform all steps in Google Earth Engine• Run classifications at county-level• Leverage the Landsat Surface Reflectance Data as available within Earth Engine• Create annually 4 cloud-free, median-value composites from Landsat imagery
• Roughly winter, spring, summer, fall• Sequential 64-day “windows” starting early march each year• Landsat 5 is the primary source, but Landsat 7 integrated for those years available, a few 4
images might show up too
• “Stack” 4 seasonal composites together to create annual imagery dataset• Extract training samples by intersection of the 2008, 2009, 2010, 2011 CDLs with
the respective annual dataset stacks• Combine those sample into one training set of a few thousand points
• Some assumption that 2008 – 2011 is representing the variability that can exist any year
• Derive decision trees using Earth Engine implementation of CART.• Apply the decision trees to the annually stacked data for each year 1984 - 2007• Calculate corn, soybean, and winter wheat areas from each year’s classification
Expected normal US NDVI crop phenology against implemented Landsat 64-day composite time windows
winter spring summer fall
Winter
Summer
Summer
Fall
2007 – Landsat 5 and 7 surface reflectance composites
Winter
Summer
Summer
Fall
1984 – Landsat 5 surface reflectance composites
Fill any missing with nearby +/- 2 years
2007PredictedCornCover
178,527.6 acres
1984PredictedCornCover
158,631.9 acres
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Minnehaha
CORN SOYBEANS WHEAT
Or better, NASS does have historical county-level crop area statistics
Thus, all maps generated and compared to NASS area statistics
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Minnehaha County Corn Area Statistics by Year
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NASS
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GEE
Minnehaha County Corn NASS to GEE Area Relationship
y = 1.3774x - 74757R² = 0.523
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Minnehaha County Soybeans Validation
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GEE
y = 0.9984x + 3000.8R² = 0.4228
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NASS
Boulder County Example
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2007
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Boulder County Crop Area Statistics
Boulder County Corn Area Validation Results
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y = 0.2792x + 1604.3R² = 0.5536
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Boulder County Winter Wheat Area Validation Results
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GEE
y = 0.0167x + 3204.7R² = 0.0016
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Beyond the Example Counties - 75 counties Randomly Sampled (25 each for corn, soybeans, wheat)
Area Correlation Results of the 25 Corn Counties Sampled
corn 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ave
R2 0.20 0.06 0.25 0.24 0.31 0.15 0.14 0.09 0.07 0.12 0.26 0.38 0.02 0.03 0.27 0.00 0.29 0.19 0.38 0.15 0.00 0.30 0.53 0.02 0.30 0.19
slope 0.63 -0.27 0.91 0.70 0.85 0.64 1.01 0.36 0.71 0.38 0.92 0.84 0.32 0.20 0.74 0.12 0.19 0.55 0.62 0.54 0.03 0.67 0.96 -0.22 0.63 0.52
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Area Correlation Results of the 25 Soybean Counties Sampled
soy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ave
R2 0.15 0.10 0.00 0.00 0.24 0.27 0.00 0.31 0.09 0.00 0.07 0.20 0.04 0.25 0.01 0.83 0.23 0.16 0.45 0.27 0.06 0.16 0.29 0.00 0.07 0.17
slope 0.20 -1.29 -0.07 -0.02 0.66 0.56 0.02 0.75 0.57 -0.07 0.29 0.66 0.28 0.56 0.11 0.64 0.32 0.93 -1.35 0.10 0.34 0.45 0.30 -0.02 0.19 0.20
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Area Correlation Results of the 25 Wheat Counties Sampled
wheat 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 ave
R2 0.31 0.02 0.01 0.13 0.11 0.29 0.17 0.00 0.11 0.19 0.27 0.10 0.06 0.08 0.17 0.34 0.52 0.00 0.31 0.12 0.00 0.13 0.05 0.33 0.27 0.16
slope 0.50 0.10 0.25 0.48 0.46 0.28 0.33 0.07 0.48 0.16 0.54 0.31 0.46 0.20 0.98 0.22 0.36 0.12 0.68 0.50 -0.02 0.50 0.04 0.31 0.64 0.36
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Closer Examination of 30 Rapidly Changing counties
1980 1990 2000 2010 2020
Area expansion
1980 1990 2000 2010 2020
Area Contraction
30 Rapidly Changing Counties GEE vs NASS area Results
corn expanding contracting
R2 0.75 0.02 0.77 0.00 0.65 0.62 0.35 0.43 0.24 0.18
slope 0.66 0.08 0.59 0.26 0.54 0.25 0.28 0.10 0.26 0.16
Soy
R2 0.12 0.40 0.02 0.38 0.35 0.61 0.46 0.62 0.02 0.07
Slope 0.34 0.51 0.18 0.48 0.37 0.19 0.16 0.55 0.03 0.04
Wheat
R2 0.43 0.07 0.28 0.15 0.00 0.23 0.44 0.67 0.23 0.20
Slope 0.51 1.54 0.45 0.53 -0.09 0.21 0.32 0.50 0.09 0.15
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The good The bad
Summary• Ability to retroactively generate land cover datasets in now possible
• The convergence of GEE and Landsat Surface Reflectance data make this pragmatic
• GEE still has computational limitations• But easy to forget just how much imagery is being used
• Quantitative results were marginal when assessed against area statistics• Qualitatively though many years looked reasonable and useful
• There are years with lacking imagery mostly due to persistent clouds
• Years with Landsat 7 alongside Landsat 5 did not perform better
• Composite windowing is subjective, one size does not fit all crops or regions.