High-Resolution Monthly Crop Water Demand Mapping
Abstract
The Department of Arequipa, in Peru, is a region with limited water resources making freshwater management critical and requiring the development of water-demand models, which can be valuable tools for policymakers. This study developed a monthly agricultural water-demand mapping algorithm for the agricultural districts surrounding the city of Arequipa. It was accomplished by: (1) developing a ground-reference data collection method; (2) creating a crop mapping algorithm, which incorporates supervised classification methods, as well as spatial- and temporal-consistency correction methods to create crop maps out of high resolution (~3 m) PlanetScope satellite images; (3) integrating a crop growth-stage prediction algorithm for the crop maps and; (4) applying an algorithm for the estimation of the agricultural-water-demand maps using the results of steps 2 and 3, local climate data, and an irrigation demand estimation tool. The crop mapping algorithm was shown to create maps with acceptable accuracy, with 5 out of 6 months with available data having mean monthly classification accuracies of 69% to 77% for those classes which had available data. Growth stage predictions had mean absolute prediction errors of 0.55 to 0.69 months in 5 out of 6 months. The 6th month (the first with ground reference data collection) had a mean absolute prediction error of 0.90 months because it lacked prior month information to correctly identify planting month. Water demand maps were produced with high spatial (3.0 m) and temporal (monthly) resolution, allowing for a detailed look at local agricultural water demands. This study provides a framework for future large-scale agricultural-water demand mapping for the Department of Arequipa and similar regions around the world.
Degree
M.Sc.
Advisors
Cherkauer, Purdue University.
Subject Area
Agriculture|Remote sensing|Aerospace engineering|Geographic information science|Information science|Information Technology|Limnology
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