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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations
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The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations

机译:光学梯形模型:一种新的遥感方法对哨片 - 2和Landsat-8观察的土壤水分

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The "trapezoid" or "triangle" model constitutes the most popular approach to remote sensing (RS) of surface soil moisture based on coupled thermal (Le., land surface temperature) and optical RS observations. The model, hereinafter referred to as Thermal-OptiCal TRAapezoid Model (TOTRAM), is based on interpretation of the pixel distribution within the land surface temperature - vegetation index (LST-VI) space. TOTRAM suffers from two inherent limitations. It is not applicable to satellites that do not provide thermal data (e.g., Sentinel-2) and it requires parameterization for each individual observation date. To overcome these restrictions we propose a novel Optical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance (SIR) and is parameterized based on the pixel distribution within the SIR-VI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and TOTRAM are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm(3) cm(-3) with local calibration and about 0.04-0.05 cm(3) cm(-3) without calibration. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture. (C) 2017 Elsevier Inc. All rights reserved.
机译:“梯形”或“三角形”模型基于耦合的热(LE。,陆地表面温度)和光学RS观测,构成了基于耦合的热(陆地温度)和光学RS观测的表面土壤水分遥感(RS)最流行的遥感方法方法。该模型,下文中称为热光学性质结构模型(TOTRAM),基于陆地表面温度 - 植被指数(LST-VI)空间内的像素分布的解释。 Totram遭受了两个固有的局限性。它不适用于不提供热数据(例如,Sentinel-2)的卫星,并且需要为每个单独的观察日期进行参数化。为了克服这些限制,我们提出了一种新的光学梯形模型(OPTRAM),其基于土壤湿度和短波红外变换反射率(SIR)之间的线性物理关系,并基于SIR-VI空间内的像素分布来参数化。与地面真理土壤湿度数据进行比较了莫纳特-2和Land Shikita流域的Sentinel-2和Landsat-8观察的基于OPTRAM的表面土壤水分估算。结果表明,OPTRAM和TOTRAM的预测精度是可比的,OPTRAM仅需要在光学电磁频域中观察。两种型号的体积含量估计误差低于0.04cm(3)厘米(-3),局部校准和约0.04-0.05cm(3)厘米(3)厘米(3)厘米(-3)而无校准。我们还证明了OPTRAM仅需要一个定位位置的一个通用参数化,这是一个重要的进步,即开启了遥感土壤水分的新途径。 (c)2017年Elsevier Inc.保留所有权利。

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