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Remote Sens. 2019, 11(3), 370; https://doi.org/10.3390/rs11030370

Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data

1
Utah Water Research Laboratory, Civil and Environmental Engineering, Utah State University, Logan, UT 84321, USA
2
Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824, USA
3
Department of Earth Systems Science and Center for Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
4
W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI 48824, USA
*
Authors to whom correspondence should be addressed.
Received: 23 December 2018 / Revised: 3 February 2019 / Accepted: 3 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Remote Sensing of the Terrestrial Hydrologic Cycle)
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Abstract

High-resolution mapping of irrigated fields is needed to better estimate water and nutrient fluxes in the landscape, food production, and local to regional climate. However, this remains a challenge in humid to subhumid regions, where irrigation has been expanding into what was largely rainfed agriculture due to trends in climate, crop prices, technologies and practices. One such region is southwestern Michigan, USA, where groundwater is the main source of irrigation water for row crops (primarily corn and soybeans). Remote sensing of irrigated areas can be difficult in these regions as rainfed areas have similar characteristics. We present methods to address this challenge and enhance the contrast between neighboring rainfed and irrigated areas, including weather-sensitive scene selection, applying recently developed composite indices and calculating spatial anomalies. We create annual, 30m-resolution maps of irrigated corn and soybeans for southwestern Michigan from 2001 to 2016 using a machine learning method (random forest). The irrigation maps reasonably capture the spatial and temporal pattern of irrigation, with accuracies that exceed available products. Analysis of the irrigation maps showed that the irrigated area in southwestern Michigan tripled in the last 16 years. We also discuss the remaining challenges for irrigation mapping in humid to subhumid areas. View Full-Text
Keywords: irrigation mapping; remote sensing; random forest; subhumid region irrigation mapping; remote sensing; random forest; subhumid region
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Xu, T.; Deines, J.M.; Kendall, A.D.; Basso, B.; Hyndman, D.W. Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data. Remote Sens. 2019, 11, 370.

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