Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data
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| Resource type: | Composite Resource | |
| Storage: | The size of this resource is 74.8 MB | |
| Created: | Feb 09, 2019 at 9:58 p.m. | |
| Last updated: | Feb 10, 2019 at 8:46 p.m.
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| DOI: | 10.4211/hs.3766845be72d45969fca21530a67bb2d | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Raster Content |
| Sharing Status: | Published |
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Abstract
Preferred citation:
Xu, T., Deines, J., Kendall, A., Basso, B., and Hyndman, DW. 2019. Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data. Remote Sensing.
We developed annual, 30-m resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). Please see Xu et al. 2019 for full details. The rasters are in UINT 8 format, with 0 indicates rainfed, 1 indicates irrigated, and 3 indicates masked (not row crops according to NLCD before 2007 and not corn or soybeans according to CDL since 2007).
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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