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|
|Owners:||David Hyndman · Tianfang Xu|
|DOI:||10.4211/hs.3766845be72d45969fca21530a67bb2d How to Cite|
|Resource type:||Composite Resource|
|Created:||Feb 09, 2019 at 9:58 p.m.|
|Last updated:||Feb 11, 2019 at midnight by Tianfang Xu|
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).
irrigation mapping,random forest,subhumid region,remote sensing
How to cite
This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/
|Coordinate System/Geographic Projection:||WGS 84 EPSG:4326|
|Coordinate Units:||Decimal degrees|
|Jillian M Deines||Michigan State University||WA, US||5132907489|
|Anthony Kendall||Michigan State University|
|Bruno Basso||Michigan State University|
|David William Hyndman||Michigan State University||Michigan, US||5172823665|
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