基于随机森林的黄河源区土壤湿度降尺度研究
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1.扬州大学水利科学与工程学院;2.水利部水文气象灾害机理与预警重点实验室,南京信息工程大学;3.中国气象局兰州干旱气象研究所;4.河海大学水文水资源与水利工程科学国家重点实验室;5.中国科学院西北生态环境资源研究院冰冻圈科学国家重点实验室

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P33;TV11

基金项目:

国家自然科学基金资助项目(42371021, 52109036);河海大学水灾害防御全国重点实验室“一带一路”水与可持续发展科技基金面上项目(2022491111, 2021490611);水利部水文气象灾害机理与预警重点实验室开放基金(HYMED202203, HYMED202210);干旱气象科学研究基金(IAM202119)


The study of downscaling SMAP surface soil moisture in source region of Yellow River
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1.College of Hydraulic Science and Engineering, Yangzhou University;2.Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science &3.Technology;4.Lanzhou Institute of Drought Meteorology, China Meteorological Administration;5.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University;6.State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences

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    摘要:

    土壤湿度作为全球水循环的重要变量,影响大气和地表水分交换。为获取高精度高空间分辨率的土壤湿度数据,本文以黄河源区为研究区域,基于9km空间分辨率的SMAP(soil moisture active passive)遥感地表土壤湿度(SSM)和随机森林模型,结合地表反照率、叶面积指数、归一化植被指数、地表温度、高程、土壤质地数据建立降尺度模型,获取黄河源区1km空间分辨率的地表土壤湿度,分析不同时期(冻期、融期)土壤湿度变化规律及降尺度模型的效果,探讨土壤湿度的空间分布特征。结果表明:降尺度后的土壤湿度数据(1km×1km)在精度上高于SMAP SSM,且相较于站点观测资料,SMAP SSM在冻期高估了地表土壤湿度,在非冻期对土壤湿度高位数表现为低估。此外,在冻期,对划分冻融期的土壤湿度数据应用降尺度模型效果优于不划分冻融期的降尺度效果;而在非冻期,不划分冻融期应用降尺度模型效果更佳。从土壤湿度空间分布上看,SMAP SSM与降尺度结果具有一致性,土壤湿度总体上呈现东高西低的特点。研究结果可为黄河源区土壤湿度时空变化研究提供理论依据,对源区水资源分布规律提供参考。

    Abstract:

    Soil moisture is an important variable in the global water cycle and affects atmospheric and surface moisture exchange. In order to obtain high accuracy and high spatial resolution soil moisture data, this study takes the source region of the Yellow River (SRYR) as the study area, and establishes a downscaling model to obtain the downscaled surface soil moisture with 1km spatial resolution in SRYR based on the 9 km spatial resolution SMAP (Soil Moisture Active and Passive) remote sensing surface soil moisture (SSM) and Random Forests, combined with albedo, leaf area index, normalized difference vegetation index, land surface temperature, elevation data and soil texture, and to analyze change rules of soil moisture in different period (freezing and thawing period) and the performance of downscaling method, and to discuss the soil moisture spatial distribution. The results show that the downscaled soil moisture data (1km×1km) are more accurate than the SMAP SSM. Comparison with the in-situ observation, the SMAP SSM overestimated the soil moisture in freezing period, and underestimated it for the higher values in thawing period. In addition, at the freezing period, the performance of downscaled model only trained using freezing period data is stronger than that using the freeze-thaw period data, but it is the opposite conclusion during the thawing period. SMAP SSM is consistent with the downscaled results in spatial distribution, and SSM changes to be low from east to west. The results can provide the theoretical basis for the study of spatial-temporal variation of soil moisture and also provide a reference for the water resources distribution in SRYR.

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  • 收稿日期:2023-08-16
  • 最后修改日期:2024-01-31
  • 录用日期:2024-02-21
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