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Remote sensing-based soil water balance for irrigation water accounting at plot and water user association management scale

机译:遥感基于灌溉用水算法的土壤水平,包括水电用户协会管理规模

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Irrigation water accounting (IWA) plays a key role in irrigation management in arid or semi-arid environments. Currently, water managers perform IWA through indirect or direct measurements such as statistical methods or flow meters. However, they have a high maintenance cost and great efforts must be done when large irrigated areas must be covered. The presented framework based on the dual crop coefficient FAO56 methodology introduces an operative application of a Remote Sensing-based Soil Water Balance (RS-SWB) to obtain a Remote Sensing-based Irrigation Water Accounting (RS-IWA). A basic input of the model is the time series of basal crop coefficient and fractional vegetation cover. It has been implemented in a large water user association (100,000 ha) along three years (2010-2012). The results are analysed from the perspective of two water management scales: the plot and the water user association. At plot scale, the RS-IWA of maize and wheat, as primary crops irrigated on demand, show a root square mean error (RMSE) of about 12 % compared with the records from local farmers. At water user association management scale, the results from RS-IWA show an RMSE of about 15 % for a comprehensive range of irrigated crops group such as spring crops, summer crops, double harvest, alfalfa, and vineyards. Hence, RS-IWA based on RS-SWB offers reproducible and reliable mapped estimations that can be used for different water managers, as they are being required from actual agro-environmental laws that are pushing these actors to better knowledge in time and space of those water resources applied.
机译:灌溉水会计(IWA)在干旱或半干旱环境中发挥着灌溉管理的关键作用。目前,水管理人员通过间接或直接测量执行IWA,例如统计方法或流量计。但是,当必须覆盖大型灌溉区域时,它们具有很高的维护成本,并且必须努力完成。基于双重作物系数FaO56方法的框架介绍了遥感的土壤水平(RS-SWB)的手术应用,以获得遥感灌溉水核算(RS-IWA)。该模型的基本输入是基底作物系数和分数植被覆盖的时间序列。它已经在大型水用户协会(100,000公顷)中实施了三年(2010-2012)。从两个水管理尺度的角度分析了结果:绘图和水用户协会。在绘制规模,玉米和小麦的RS-IWA,因为当地农作物灌溉原农作物,与当地农民的记录相比,大规模平均误差(RMSE)约为12%。在水用户协会管理规模中,RS-IWA的结果表明,春季作物,夏季作物,双重收获,苜蓿和葡萄园等综合灌溉作物群体的RMSE大约为15%。因此,基于RS-SWB的RS-IWA提供可重复且可靠的映射估计估计,可以用于不同的水管理人员,因为他们正在从推动这些演员推动这些演员的实际农业环境法,以更好地了解那些应用水资源。

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