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首页> 外文期刊>Surveys in Geophysics: An International Review Journal of Geophysics and Planetary Sciences >Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing
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Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing

机译：遥感估算蒸散量的植被指数方法

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Evapotranspiration (ET) is the largest term after precipitation in terrestrial water budgets. Accurate estimates of ET are needed for numerous agricultural and natural resource management tasks and to project changes in hydrological cycles due to potential climate change. We explore recent methods that combine vegetation indices (VI) from satellites with ground measurements of actual ET (ET_a) and meteorological data to project ET_a over a wide range of biome types and scales of measurement, from local to global estimates. The majority of these use time-series imagery from the Moderate Resolution Imaging Spectrometer on the Terra satellite to project ET over seasons and years. The review explores the theoretical basis for the methods, the types of ancillary data needed, and their accuracy and limitations. Coefficients of determination between modeled ET_a and measured ET_a are in the range of 0.45-0.95, and root mean square errors are in the range of 10-30% of mean ET_a values across biomes, similar to methods that use thermal infrared bands to estimate ET_a and within the range of accuracy of the ground measurements by which they are calibrated or validated. The advent of frequent-return satellites such as Terra and planed replacement platforms, and the increasing number of moisture and carbon flux tower sites over the globe, have made these methods feasible. Examples of operational algorithms for ET in agricultural and natural ecosystems are presented. The goal of the review is to enable potential end-users from different disciplines to adapt these methods to new applications that require spatially-distributed ET estimates.
机译：蒸发蒸腾（ET）是陆地水预算中仅次于降水的最大术语。对于许多农业和自然资源管理任务以及预测由于潜在的气候变化而引起的水文循环变化，需要准确估算ET。我们探索了将卫星的植被指数（VI）与实际ET（ET_a）的地面测量值和气象数据相结合的最新方法，以便在从本地到全球估计的各种生物群落类型和测量规模上预测ET_a。其中大多数使用时间序列图像（来自Terra卫星上的中分辨率成像光谱仪）来预测季节和年份的ET。这篇综述探讨了这些方法的理论基础，所需的辅助数据的类型及其准确性和局限性。建模的ET_a和测得的ET_a之间的确定系数在0.45-0.95范围内，并且均方根误差在整个生物群落中平均ET_a值的10％至30％范围内，类似于使用热红外波段估算ET_a的方法并在地面测量的精度范围内进行校准或验证。频繁返回的卫星（例如Terra和计划中的替换平台）的出现，以及全球不断增加的水分和碳通量塔站点的数量，使这些方法变得可行。给出了农业和自然生态系统中ET的运算算法示例。审查的目的是使来自不同学科的潜在最终用户能够使这些方法适应需要空间分布ET估计的新应用程序。

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