1. Corn and Soybean Differentiation Using Multi-Spectral Landsat Data
Freeborn County Minnesota,
Matt McGuire and Andrew Munsch

2. Importance of Corn and Soy
Minnesota’s Leading Agricultural Commodities
Ethanol Subsidies
Increasing Worldwide Soy Demand

3. Minnesota’s Top Corn Counties

4. How to Differentiate Corn and Soy
Spectral Properties Change During Growth

5. Recent Trends in Corn and Soy
Use Pre-Classed CDL’s (Crop Data Layers)
Matrix Union
Can Detect Total Field Area of Both
Can Detect Crop Rotation

6. Sample CDL Image

7. Corn and Soy Field Areas

8. Corn and Soy Rotation

9. Study Area: Freeborn County MN
Landsat 5 Image: Landsat 8 Image:2013
Both Images Taken On July 16, July 14

10. “Quick and Dirty” 2-Class Unsupervised Classification of 2008 Landsat 5 Image
Create a “Corn and Soy” only mask using the 2008 CDL (recode)
Stack and Extract Study Area From 2008 Landsat 5 Image
Run Simple K-Means Unsupervised Classification With 2 Output Classes

11. Results of 2-Class Unsupervised

12. Comparison of Classification With 2008 CDL Image
Use Matrix Union
Pixels Classified As:
Reference Class
Corn
Soy
Total
Producer's Acc
777038
59587
836625
92.88%
81344
555095
636439
87.22%
858382
614682
User Accuracy
90.52%
90.31%
90.43% Total Accuracy

13. Landsat NDVI Vs 2008 CDL

14. Signature Mean Plot of 2008 Classification

15. Results of 8-Class Unsupervised Classification of 2013 Landsat

18. 2013 Initial Crop Estimates
Pixels (Classified Image)
Estimated Acres
Actual Acreage Planted
Error%
Corn
772472
13.20%
Soy
714161
88.50%

20. 2013 Initial Crop Estimates
Pixels (Classified Image)
Estimated Acres
Actual Acreage Planted
Error%
Corn
772472
13.20%
Soy
501850
32.50%

21. Final Re-Classed Image

22. Issues and Future Improvements
Use multi-temporal data
Use elevation data for field flooding
Temporal Resolution Issues
Cloud Cover