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

Xu, Tianfang; Deines, Jillian M.; Kendall, Anthony D.; Basso, Bruno; Hyndman, David W.

doi:10.3390/rs11030370

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

^1,2,*,

^2,3

^2,4

and

David W. Hyndman

^2,*

Utah Water Research Laboratory, Civil and Environmental Engineering, Utah State University, Logan, UT 84321, USA

Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48824, USA

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.

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

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

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Supplementary File 1:
PDF-Document (PDF, 154 KiB)
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Doi: 10.4211/hs.3766845be72d45969fca21530a67bb2d
Link: https://doi.org/10.4211/hs.3766845be72d45969fca21530a67bb2d
Description: In addition to the supplementary file uploaded, we published the dataset products from this paper at the above link.

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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. https://doi.org/10.3390/rs11030370

AMA Style

Xu T, Deines JM, Kendall AD, Basso B, Hyndman DW. Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data. Remote Sensing. 2019; 11(3):370. https://doi.org/10.3390/rs11030370

Chicago/Turabian Style

Xu, Tianfang, Jillian M. Deines, Anthony D. Kendall, Bruno Basso, and David W. Hyndman. 2019. "Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data" Remote Sensing 11, no. 3: 370. https://doi.org/10.3390/rs11030370

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