Estimating the effect of crop classification error on evapotranspiration derived from remote sensing in the lower Colorado River basin, USA

摘要

In the U.S. Bureau of Reclamation's Lower Colorado River Accounting System (LCRAS), crop classifications derived from remote sensing are used to calculate regional estimates of crop evapotranspiration for water monitoring and management activities on the lower Colorado River basin. The LCRAS accuracy assessment was designed to quantify the impact of crop classification error on annual total crop evapotranspiration (ETc), as calculated from the Penman-Monteith method using the map crop classification as input. The accuracy assessment data were also used to generate a sample-based estimate of total ETc using the crop type identified by direct ground observation of each sample field. A stratified random sampling design was implemented using field size as the stratification variable. The stratified design did not markedly improve precision for the accuracy assessment objective, but it was highly effective for the objective of estimating ETc derived from the ground-ob served crop types. The sampling design and analysis methodology developed for LCRAS demonstrates the utility of a multi-purpose approach that satisfies the accuracy assessment objectives, but also allows for rigorous, sample-based estimates of other collective properties of a region (e.g., total ETc in this study). We discuss key elements of this multi-purpose sampling strategy and the planning process used to implement such a strategy. (c) 2006 Elsevier Inc. All rights reserved.