Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA
Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA
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DOI：
10.1016/j.ejrh.2021.100946
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
D. Warner;M. Guevara;J. Callahan;R. Vargas
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文献类型：
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作者：
D. Warner;M. Guevara;J. Callahan;R. Vargas
Study regionDelaware，USA and its surrounding watersheds.Study focusAn ensemble using multiple Kernel K-nearest neighbors（KKNN）models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency's Climate Change Initiative Soil Moisture Product，terrain data，soil maps，and local meteorological network data.估计的SSM根据独立的原位SSM观测进行了评估，并调查了与土地覆盖等级和植被物候的关系（即，NDVI）。该地区的新水文见解与原始、粗分辨率遥感SSM数据集相比，当校准到实地观测值时，缩小的每日平均SSM估计值在空间上的误差较小（27%），并且随着时间的推移具有更好的预测性能。缩小尺度的SSM确定更强和更广泛的时间关系与NDVI比其他估计方法。然而，粗和细分辨率数据集大大低估了SSM在湿地地区。调查结果强调，需要加强现场SSM监测在不同的设置，以提高企业级规模缩小SSM。在这项研究中开发的降尺度方法能够产生每日SSM估计，提供了一个框架，可以支持未来的SSM建模工作，水文生态调查和农业研究在这个和世界其他地区的地面监测网络结合使用时。
Study regionDelaware, USA and its surrounding watersheds.Study focusAn ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI).New hydrological insights for the regionDownscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.