Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data
|Authors:||Tianfang Xu Jillian M Deines Anthony Kendall Bruno Basso David William Hyndman|
|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.
|Citation:||See how to cite this resource|
|Content types:||Geographic Raster Content|
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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