当前位置: X-MOL 学术Agric. Water Manag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region
Agricultural Water Management ( IF 6.7 ) Pub Date : 2022-06-15 , DOI: 10.1016/j.agwat.2022.107764
Steven K. Filippelli, Matthew R. Sloggy, Jody C. Vogeler, Dale T. Manning, Christopher Goemans, Gabriel B. Senay

For agricultural areas facing water scarcity, sustainable water use policy relies on irrigation information that is timely and at a high resolution, but existing publicly available water use data are often insufficient for monitoring compliance or understanding the influence of policy on individual farmer decisions. This study attempts to fill this data gap by using remote sensing to map annual irrigation quantity at the field-scale within the central Ogallala aquifer region of the United States. We compiled in situ annual irrigation volume data at the field scale in the Republican River Basin of Colorado for 2015–2018 and at the Public Land Survey System (PLSS) section scale in western Kansas for 2000–2016, which served as reference data in random forest models that relied on Landsat-based actual evapotranspiration from the Operational Simplified Surface Energy Balance model (SSEBop) along with maps of irrigated area, Landsat spectral indices, climate, soils, and derived hydrologic variables. The models explained 87% of the variability in irrigation volume in Colorado and 75% in Kansas, but accuracy declined when transferring the models in spatial cross-validation (Colorado R2 =0.81; Kansas R2 =0.51) and temporal cross-validation (Colorado R2 =0.82; Kansas R2 =0.68). Predicted annual totals of irrigation volume in western Kansas had a mean absolute error of 11.9%, which was slightly higher than the average annual change of 11%. Use of predicted irrigation maps also lead to an underestimated effect size for a water use restriction policy in Kansas. These results indicate that field- and section-scale irrigation can be mapped with reasonable accuracy within a region and time period that has adequate sample data, but that methods may need to be improved for applying the models more broadly in areas that lack extensive in situ irrigation data to support further research on water use and aid in structuring policy.



中文翻译:

奥加拉拉中部含水层地区田间规模灌溉取水的遥感

对于面临缺水的农业地区,可持续用水政策依赖于及时和高分辨率的灌溉信息,但现有的公开可用用水数据通常不足以监测合规性或了解政策对个体农民决策的影响。本研究试图通过使用遥感来绘制美国中部奥加拉拉含水层区域内田间尺度的年度灌溉量图来填补这一数据空白。我们编制了 2015-2018 年科罗拉多州共和党河流域田间规模和 2000-2016 年堪萨斯州西部公共土地调查系统 (PLSS) 剖面规模的现场年度灌溉量数据,作为随机参考数据基于 Landsat 实际的森林模型来自操作简化地表能量平衡模型 (SSEBop) 的蒸散量以及灌溉面积图、 Landsat 光谱指数、气候、土壤和衍生的水文变量。这些模型解释了科罗拉多州灌溉量 87% 和堪萨斯州 75% 的可变性,但在空间交叉验证 (Colorado R 2=0.81; Kansas R2=0.51) 和时间交叉验证 (科罗拉多州 R2=0.82;堪萨斯州 R2=0.68)。堪萨斯州西部年度总灌溉量预测的平均绝对误差为 11.9%,略高于 11% 的年均变化。使用预测的灌溉地图也会导致堪萨斯州用水限制政策的效应量被低估。这些结果表明,可以在具有足够样本数据的区域和时间段内以合理的精度绘制田间和分段尺度灌溉,但可能需要改进方法,以便在缺乏广泛原位的地区更广泛地应用模型灌溉数据,以支持对用水的进一步研究并帮助制定政策。

更新日期:2022-06-15
down
wechat
bug