当前位置: X-MOL 学术J. Hydrol. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Disaggregating satellite soil moisture products based on soil thermal inertia: a comparison of a downscaling model built at two spatial scales
Journal of Hydrology ( IF 6.4 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.jhydrol.2020.125894
I.P. Senanayake , I.-Y. Yeo , G.R. Willgoose , G.R. Hancock

Abstract Lack of high spatial resolution soil moisture data is a major limitation in many regional scale hydrologic, climatic and agricultural applications. The available satellite soil moisture data products are too coarse and unable to cater for this resolution requirement. Downscaling coarse spatial resolution satellite soil moisture retrievals is a feasible option to meet the required level of spatial resolution for those applications. The main focus of this study is to compare two soil thermal inertia-based downscaling models, built by using long-term time records of (i) point scale in-situ data and, (ii) 25 km resolution Global Land Data Assimilation System (GLDAS) land surface model outputs. The developed models were tested over Goulburn River catchment in the Upper Hunter Region of NSW, Australia to downscale Soil Moisture Active Passive (SMAP) 36 km radiometric products into 1 km resolution. The downscaled SMAP products from both models produced encouraging results with unbiased root mean square errors (ubRMSEs) of 0.07 - 0.10 cm3/cm3, against in-situ field data, and an average ubRMSEs of 0.07 cm3/cm3 when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.

中文翻译:

基于土壤热惯性分解卫星土壤水分产品:在两个空间尺度上建立的缩小模型的比较

摘要 缺乏高空间分辨率土壤水分数据是许多区域尺度水文、气候和农业应用的主要限制。可用的卫星土壤水分数据产品过于粗糙,无法满足这种分辨率要求。缩小粗略空间分辨率卫星土壤水分反演是满足这些应用所需空间分辨率水平的可行选择。本研究的主要重点是比较两种基于土壤热惯性的降尺度模型,这些模型是通过使用 (i) 点尺度原位数据和 (ii) 25 公里分辨率全球陆地数据同化系统( GLDAS) 地表模型输出。开发的模型在新南威尔士州上亨特地区的古尔本河集水区进行了测试,澳大利亚将土壤水分主动被动 (SMAP) 36 公里辐射测量产品缩小到 1 公里分辨率。两种模型的缩小 SMAP 产品都产生了令人鼓舞的结果,与原位数据相比,均方根误差 (ubRMSE) 为 0.07 - 0.10 cm3/cm3,与国家机载场相比,平均 ubRMSE 为 0.07 cm3/cm3实验 2005 (NAFE'05) 土壤水分反演。两种模型都在半干旱地区以高空间分辨率估算土壤水分方面显示出有希望的结果,但各有优缺点。此处的发现为跨尺度土壤热惯性关系的稳健性以及模型分辨率对缩小土壤水分估计值的影响提供了有用的见解。
更新日期:2021-03-01
down
wechat
bug