IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.
Abstract
:1. Introduction
2. Data and Methods
2.1. Methodological Overview
2.2. Study Area
2.3. Landsat and Aerial Imagery
2.4. Meteorology and Climate Data
2.5. Terrain and Land Use Data
2.6. Training Data
2.7. Model Training and Classification
2.8. Model Cross Validation
2.9. Comparison with National Agricultural Statistics Service Data
2.10. Calculation of Irrigated Area Change
3. Results
3.1. Model Accuracy
3.2. Variable Importance
3.3. Comparison with NASS Data
3.4. Trends in Irrigation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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State | Source | Irr. Inspected | Coverage | Irr. | Dry. | Uncult. a,b | Wet. c |
---|---|---|---|---|---|---|---|
AZ | USGS d | 2001, 2003, 2004, 2007, 2016 | Features | 133 | 1843 | 437 | 4711 |
Hand-drawn | Area (km) | 49.949 | 49 | 29,301 | 289 | ||
CA | CACASA e | 1995, 1998, 2000, 2007, 2014, 2016 | Features | 6022 | 0 | 5812 | 20,822 |
DRI f | Area (km) | 3676 | 0 | 5876 | 472 | ||
CO | CO DWR g | 1998, 2003, 2006, 2013, 2016 | Features | 23,919 | 3793 | 414 | 9012 |
USGS d | Area (km) | 4009 | 7468 | 29,204 | 200 | ||
CLU h | |||||||
Hand-drawn | |||||||
ID | ID DWR i | 1986, 1988, 1997, 1998, 2001, 2002, 2006, 2008 | Features | 4196 | 82 | 8168 | 5004 |
CLU h | Area (km) | 2355 | 73 | 105,838 | 82 | ||
Hand-drawn | |||||||
MT | MT DNRC j | 2008, 2009, 2010, 2011, 2012, 2013 | Features | 4112 | 15,120 | 10,401 | 10,611 |
Hand-drawn | Area (km) | 628 | 47,656 | 85,573 | 64 | ||
NM | USGS d | 1987, 1988, 1989, 1994, 2001, 2002, 2004, 2009, 2010, 2014, 2016 | Features | 3563 | 615 | 455 | 6004 |
NM WRRI k | Area (km) | 353 | 28 | 24,636 | 42 | ||
Hand-drawn | |||||||
NV | DRI e | 2001, 2002, 2003, 2005, 2006, 2007, 2008, 2009 | Features | 2346 | 0 | 1769 | 9496 |
Area (km) | 518 | 0 | 122,591 | 442 | |||
OR | OR DWR l | 1994, 1996, 1997, 2001, 2011, 2013 | Features | 1009 | 0 | 612 | 9923 |
CLU h | Area (km) | 333 | 0 | 34,348 | 393 | ||
Hand-drawn | |||||||
UT | UT DWR m | 1998, 2003, 2006, 2013, 2016 | Features | 2323 | 5327 | 726 | 5399 |
Area (km) | 518 | 1175 | 47,196 | 147 | |||
WA | WSDA n | 1988, 1996, 1997, 1998, 2001, 2006 | Features | 4828 | 16,960 | 10,067 | 9764 |
Area (km) | 1833 | 14,225 | 15,239 | 167 | |||
WY | WY WDO o | 1998, 2003, 2006, 2013, 2016 | Features | 916 | 77 | 529 | 9553 |
Hand-drawn | Area (km) | 387 | 21 | 38,331 | 139 |
Predicted | |||||
---|---|---|---|---|---|
Irrigated | Dryland | Uncultivated | Wetland | ||
Actual | Irrigated | 9893 | 24 | 15 | 68 |
Dryland | 149 | 9660 | 68 | 123 | |
Uncultivated | 76 | 131 | 9058 | 733 | |
Wetland | 555 | 432 | 1304 | 7708 |
Predicted | |||
---|---|---|---|
Irrigated | Unirrigated | ||
Actual | Irrigated | 183 | 2 |
Unirrigated | 136 | 9679 |
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Ketchum, D.; Jencso, K.; Maneta, M.P.; Melton, F.; Jones, M.O.; Huntington, J. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sens. 2020, 12, 2328. https://doi.org/10.3390/rs12142328
Ketchum D, Jencso K, Maneta MP, Melton F, Jones MO, Huntington J. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing. 2020; 12(14):2328. https://doi.org/10.3390/rs12142328
Chicago/Turabian StyleKetchum, David, Kelsey Jencso, Marco P. Maneta, Forrest Melton, Matthew O. Jones, and Justin Huntington. 2020. "IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S." Remote Sensing 12, no. 14: 2328. https://doi.org/10.3390/rs12142328