Retrieval of Vegetation Water Content Using Brightness Temperatures from the Soil Moisture Active Passive (SMAP) Mission

S. Chan, R. Bindlish
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引用次数: 1

Abstract

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) of SMAP brightness temperature observations.
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利用土壤水分主被动(SMAP)任务的亮度温度反演植被含水量
在本文中,我们探索了一种时间序列方法,使用tau-omega (τ-ω)模型在最少使用辅助数据的情况下检索植被含水量(kg/m2)。解析上,该方法需要分两步进行非线性优化。首先,利用多天同地亮度温度观测数据反演有效植被不透明度,该数据综合了地表粗糙度和植被不透明度的辐射和极化效应;然后使用所得的有效植被不透明度来检索植被含水量,使其在增益因子α和偏移因子β之间。利用气候植被含水量辅助数据库作为SMAP标准和增强土壤水分产品开发的辅助数据库,可以利用植被含水量年最小值和年最大值在全球范围内确定α和β。得到的α和β值可以用来重建反演的植被含水量。本文介绍了该方法的公式、假设和局限性,并利用一年(2016年)的SMAP亮度温度观测数据初步检索了全球植被含水量。
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