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A Downscaling Approach for SMOS Land Observations: Evaluation of High-Resolution Soil Moisture Maps Over the Iberian Peninsula

机译:SMOS土地观测的缩减方法:伊比利亚半岛高分辨率土壤水分图的评估

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The ESA’s Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earth’s surface soil moisture. It has a spatial resolution of  km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0–5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatio-temporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of soil moisture measurements is reproduced in the high-resolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula.
机译:ESA的土壤湿度和海洋盐度(SMOS)任务是第一颗专门用于测量地球表面土壤湿度的卫星。它具有km的空间分辨率和3天的重访时间。在本文中,提出了一种降尺度算法,这是一种使用可见到红外遥感观测值从SMOS获得多分辨率土壤湿度估算值的新功能。该算法适用于将伊比利亚半岛2年的SMOS和MODIS Terra / Aqua数据合并成精细的(1 km)土壤湿度估算值。将分类的土壤水分图与REMEDHUS网络中基于0-5 cm的地面测量结果进行比较。采用了三种匹配策略:1)在40 km空间分辨率下进行比较,以确保在缩小的地图中保留SMOS灵敏度; 2)通过与点比例尺观测值的比较分析缩小比例尺地图的时空相关性;和3)根据每个土地用途汇总高分辨率地图和地面观测,以识别与植被活动和土壤类型有关的空间格局。结果表明,降尺度方法改善了SMOS粗土壤水分估算的空间表达,同时与基于地面的测量保持时间相关性和均方根差。土壤湿度测量的动态范围在高分辨率地图中进行了复制,包括具有不同平均土壤湿度条件的测站。缩小比例的地图记录了除灌溉作物外的一般土地用途的土壤水分动态。这项评估研究支持使用这种缩小比例的方法来增强SMOS观测在伊比利亚半岛等半干旱地区的空间分辨率。

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