**1. Introduction**

Agriculture is the sector with the largest consumptive use of water across the globe. While crop water demand is largely met by irrigation in arid to semiarid regions, farmers in humid regions traditionally rely on rainfall. However, irrigation has become more common in humid to subhumid regions [1], driven by the growth of demand for corn grain bioethanol, the need to increase yield given current low prices of corns and soybeans [2], the ready availability of more water and energy efficient irrigation technologies, and increasing climate variability.

The rapid expansion of irrigation has important implications for terrestrial water balances, food production, and local to regional climate [3–6]. Land surface models have been increasingly used as quantitative tools to estimate the effects of land use change and other human activities on terrestrial water and energy cycles. However, these models require high-resolution observations at the model scale to fully vet the irrigated area [7,8]. Thus, detailed depiction of spatiotemporal patterns of irrigation is needed for modelers and decision makers [9]. However, accurate monitoring of irrigated area can be difficult in humid to subhumid regions (hereafter humid regions), primarily because of the similarity of signals from rainfed and irrigated areas in such regions [10].

Remote sensing provides valuable information to delineate irrigated areas. Within the U.S., the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US) national irrigation dataset was developed by the U.S. Geological Survey (USGS) by integrating U.S. Department of Agriculture (USDA) county statistics, MODIS satellite imagery and a national land cover map [11]. The MIrAD-US product has 250-m resolution and is available in 2002, 2007 and 2012. MIrAD-US revealed significant temporal variability and suggests the need for regular periodic mapping of irrigated areas [12]. Later studies have used higher resolution imagery (10–30 m) from Landsat and Sentinel-1 satellites to develop more detailed irrigation maps for local to regional studies [13–15]. In particular, annual irrigation maps were developed for the Republican River Basin from 1999 to 2016 (AIM-RRB), leveraging recent advances in cloud computing, machine learning, and increasingly accessible Landsat data [13].

In southwestern Michigan, a subhumid region in the midwestern U.S., water consumption by agriculture has rapidly increased over the past two decades. Irrigation of row crops (primarily corn and soybean) was once practiced only on a small fraction of the total crop land across the upper Midwest. However, in the last two decades there has been a dramatic expansion in irrigation use [2], mostly from groundwater pumping [16]. Large acreages of fields in southwestern Michigan are devoted to producing seed corn, commercial corn, and soybeans [16]. The prevailing sandy soils [17] and shallow depths to groundwater [18] in this region allows adoption of central pivot irrigation systems with limited operation costs. Given the strong connection between groundwater and surface water, irrigation in southwestern Michigan has the potential to reduce the health of some local surface water ecosystems [19].

Remote sensing methods are able to map irrigated fields in arid and semi-arid environments with satisfactory accuracy, however the accuracy of satellite-based irrigation mapping techniques in more humid regions is still unknown [11]. The objective of this study was to create high-resolution, annual maps of irrigated fields in a sub-humid region by integrating remote sensing imagery with climate and land surface modeling data. We identified three methods to increase remote sensing accuracy: (1) use weather-sensitive selection of imagery timing, (2) test the transferability of recently-developed composite indices for detecting irrigation in arid areas [13] to humid regions, and (3) calculate spatial anomaly indices. We demonstrated this approach in southwestern Michigan (SW MI) where corn and soybeans are the two principal irrigated crops. We also evaluated the accuracy of irrigation mapping under various climate conditions in this region, which provided insights into the applicability to other humid regions.

#### **2. Materials and Methods**
