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Global scale error assessments of soil moisture estimates from microwave-based active and passive satellites and land surface models over forest and mixed irrigated/dryland agriculture regions
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.rse.2020.112052
Hyunglok Kim , Jean-Pierre Wigneron , Sujay Kumar , Jianzhi Dong , Wolfgang Wagner , Michael H. Cosh , David D. Bosch , Chandra Holifield Collins , Patrick J. Starks , Mark Seyfried , Venkataraman Lakshmi

Abstract Over the past four decades, satellite systems and land surface models have been used to estimate global-scale surface soil moisture (SSM). However, in areas such as densely vegetated and irrigated regions, obtaining accurate SSM remains challenging. Before using satellite and model-based SSM estimates over these areas, we should understand the accuracy and error characteristics of various SSM products. Thus, this study aimed to compare the error characteristics of global-scale SSM over vegetated and irrigated areas as obtained from active and passive satellites and model-based data: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). We employed triple collocation analysis (TCA) and caluclated conventional error metrics from in-situ SSM measurements. We also considered all possible triplets from 6 different products and showed the viability of considering the standard deviation of TCA-based numbers in producing robust results. Over forested areas, it was expected that model-based SSM data might provide more accurate SSM estimates than satellites due to the intrinsic limitations of microwave-based systems. Alternately, over irrigated regions, observation-based SSM data were expected to be more accurate than model-based products because land surface models (LSMs) cannot capture irrigation signals caused by human activities. Contrary to these expectations, satellite-based SSM estimates from ASCAT, SMAP, and SMOS showed fewer errors than ERA5 and GLDAS SSM products over vegetated conditions. Furthermore, over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products; however, model-based data from ERA5 and GLDAS outperformed AMSR2. Our results emphasize that, over irrgated areas, considering satellite-based SSM data as alternatives to model-based SSM data sometimes produces misleading results; and considering model-based data as alternatives to satellite-based SSM data in forested areas can also sometimes be misleading. In addition, we discovered that no products showed much degradation in TCA-based errors under different vegetated conditions, while different irrigation conditions impacted both satellite and model-based SSM data sets. The present research demonstrates that limitations in satellite and modeled SSM data can be overcome in many areas through the synergistic use of satellite and model-based SSM products, excluding areas where satellite-based data are masked out. In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.

中文翻译:

基于微波的有源和无源卫星以及森林和混合灌溉/旱地农业区的地表模型对土壤水分估计的全球尺度误差评估

摘要 在过去的四年中,卫星系统和地表模型已被用于估计全球尺度的表层土壤水分 (SSM)。然而,在植被茂密和灌溉地区等地区,获得准确的 SSM 仍然具有挑战性。在对这些区域使用卫星和基于模型的 SSM 估计之前,我们应该了解各种 SSM 产品的精度和误差特性。因此,本研究旨在比较从有源和无源卫星获得的全球尺度 SSM 在植被和灌溉区域的误差特征以及基于模型的数据:高级散射计 (ASCAT)、土壤湿度和海洋盐度 (SMOS)、高级微波扫描辐射计 2 (AMSR2)、土壤水分主动被动 (SMAP)、欧洲中期天气预报中心再分析 5 (ERA5)、和全球土地数据同化系统 (GLDAS)。我们采用了三重搭配分析 (TCA) 并根据原位 SSM 测量计算了常规误差指标。我们还考虑了来自 6 种不同产品的所有可能的三元组,并展示了在产生稳健结果时考虑基于 TCA 的数字的标准偏差的可行性。在森林地区,由于基于微波的系统的固有局限性,预计基于模型的 SSM 数据可能比卫星提供更准确的 SSM 估计。或者,在灌溉地区,基于观测的 SSM 数据预计比基于模型的产品更准确,因为地表模型 (LSM) 无法捕获人类活动引起的灌溉信号。与这些预期相反,ASCAT、SMAP、SMOS 在植被条件下显示出比 ERA5 和 GLDAS SSM 产品更少的错误。此外,在灌溉区域,ASCAT、SMOS 和 SMAP 的表现优于其他 SSM 产品;然而,来自 ERA5 和 GLDAS 的基于模型的数据优于 AMSR2。我们的结果强调,在灌溉地区,将基于卫星的 SSM 数据作为基于模型的 SSM 数据的替代方案有时会产生误导性结果;将基于模型的数据作为森林地区基于卫星的 SSM 数据的替代方案有时也会产生误导。此外,我们发现在不同的植被条件下,没有任何产品在基于 TCA 的误差方面表现出很大的退化,而不同的灌溉条件会影响卫星和基于模型的 SSM 数据集。目前的研究表明,通过卫星和基于模型的 SSM 产品的协同使用,可以在许多领域克服卫星和模型 SSM 数据的局限性,不包括卫星数据被屏蔽的区域。In fact, when four satellite and model data sets are used selectively, the probability of obtaining SSM with stronger signal than noise can be close to 100%.
更新日期:2020-12-01
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