  Accurate maps of irrigation are essential for understanding and managing
water resources. We present a new method of mapping irrigation and demonstrate
its accuracy for the state of Montana from years 2000-2019. The method is based
off of an ensemble of convolutional neural networks that use reflectance
information from Landsat imagery to classify irrigated pixels, that we call
IrrMapper-U-Net. The methodology does not rely on extensive feature engineering
and does not condition the classification with land use information from
existing geospatial datasets. The ensemble does not need exhaustive
hyperparameter tuning and the analysis pipeline is lightweight enough to be
implemented on a personal computer. Furthermore, the proposed methodology
provides an estimate of the uncertainty associated with classification. We
evaluated our methodology and the resulting irrigation maps using a highly
accurate novel spatially-explicit ground truth data set, using county-scale
USDA surveys of irrigation extent, and using cadastral surveys. We found that
that our method outperforms other methods of mapping irrigation in Montana in
terms of overall accuracy and precision. We found that our method agrees better
statewide with the USDA National Agricultural Statistics Survey estimates of
irrigated area compared to other methods, and has far fewer errors of
commission in rainfed agriculture areas. The method learns to mask clouds and
ignore Landsat 7 scan-line failures without supervision, reducing the need for
preprocessing data. This methodology has the potential to be applied across the
entire United States and for the complete Landsat record.
