A Physically Based Soil Moisture Index From Passive Microwave Brightness Temperatures for Soil Moisture Variation Monitoring
A Physically Based Soil Moisture Index From Passive Microwave Brightness Temperatures for Soil Moisture Variation Monitoring
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基于被动微波亮度温度的基于物理的土壤湿度指数，用于土壤湿度变化监测
DOI：
10.1109/tgrs.2019.2955542
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发表时间：
2020-04
影响因子：
8.2
通讯作者：
Bai Xiaojing
中科院分区：
文献类型：
--
作者：
Zeng Jiangyuan;Chen Kun-Shan;Cui Chenyang;Bai Xiaojing
土壤湿度是连接陆地水、能量和碳循环的关键水文变量。本文利用l波段土壤水分主动被动辐射计（SMAP）观测数据建立了一个新的土壤水分指数（SMI），该指数旨在捕捉土壤水分在不考虑云量和太阳光照的情况下的时间变化。SMI的提出基于两个关键基础：1)植被和粗糙度对微波发射“去极化”的影响相似，而SM增强了极化差异；2)植被和粗糙度对地表发射率的影响总体为正，而SM与发射率呈负相关。基于这两个物理原理，可以在独立于植被类型和粗糙度条件的二维空间中解耦SM与植被和表面粗糙度的影响。然后，通过覆盖不同植被和气候条件的5个密集SM网络的原位测量验证了所提出的SMI，并与SMAP被动SM产品和欧洲航天局气候变化倡议（ESA CCI）的粗分辨率SM产品（36 km）以及SMAP增强被动SM产品和日本宇宙航空研究开发机构（JAXA）先进微波扫描辐射计（AMSR2）的中分辨率SM产品（9 km）进行了比较。结果表明，SMI能较好地再现SM的时间动态，在36 km和9 km处的平均相关系数分别为0.87和0.84，高于SMAP被动（0.80）、SMAP增强被动（0.77）、ESA CCI（0.69）和JAXA AMSR2（0.53）。在利用累积分布函数（CDF）匹配技术消除卫星与特定站点SM数据之间的系统差异后，SMI在验证期内36 km和9 km处的平均均方根误差（RMSE）分别为0.031和0.036 m3m−3，低于卫星SM产品。除了地表温度外，SMI不需要其他传感器的进一步信息[例如，光学归一化植被指数（NDVI）或叶面积指数（LAI）数据]来保证全天候监测。因此，在全球尺度上估计SM变率具有很大的潜力。
Soil moisture is a pivotal hydrological variable that links the terrestrial water, energy, and carbon cycles. In this article, a new soil moisture (SM) index (SMI), which aims to capture the temporal variability of SM, irrespective of cloud cover and solar illumination, was developed by using the L-band SM active passive (SMAP) radiometer observations. The SMI was proposed on the basis of two key foundations: 1) vegetation and roughness have similar effects on “depolarization” of microwave emission, while SM enhances polarization differences and 2) vegetation and roughness generally impose positive effects on surface emissivity, while SM and emissivity are negatively correlated. Based on the two physical principles, it is possible to decouple the effects of SM and those of vegetation and surface roughness in a 2-D space independent of vegetation type and roughness condition. The proposed SMI was then validated by in situ measurements from five dense SM networks covering different vegetation and climatic conditions and also compared with SMAP passive and European space agency climate change initiative (ESA CCI) SM products at a coarse resolution of 36 km, and SMAP-enhanced passive and Japan Aerospace Exploration Agency (JAXA) advanced microwave scanning radiometer (AMSR2) SM products at a medium resolution of 9 km. The results show that the new SMI is able to well reproduce the temporal dynamic of SM with a favorable averaged correlation coefficient value of 0.87 and 0.84 at 36 and 9 km, respectively, higher than that of SMAP passive (0.80), SMAP-enhanced passive (0.77), ESA CCI (0.69), and JAXA AMSR2 (0.53). After removing the systematic differences between satellite and site-specific SM data by using the cumulative distribution function (CDF) matching technique, the SMI can achieve an average root mean squared error (RMSE) of 0.031 and 0.036 m3m−3 at 36 and 9 km during the validation period, respectively, lower than that of the satellite SM products. In addition to surface temperature, the SMI does not need any further information from other sensors [e.g., the optical normalized difference vegetation index (NDVI) or leaf area index (LAI) data] to guarantee an all-weather monitoring. Therefore, it has great potential to estimate SM variability on a global scale.
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DOI：
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发表时间：
2014
期刊：
--
影响因子：
--
作者：
D. Entekhabi;S. Yueh;P. O’neill;K. Kellogg;A. Allen;R. Bindlish;Molly E. Brown;S. Chan;A. Colli
通讯作者：
D. Entekhabi;S. Yueh;P. O’neill;K. Kellogg;A. Allen;R. Bindlish;Molly E. Brown;S. Chan;A. Colli
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影响因子：
13.5
作者：
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Zhongbo Su
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影响因子：
8.2
作者：
Zeng Jiangyuan;Chen Kun-Shan;Bi Haiyun;Zhao Tianjie;Yang Xiaofeng
通讯作者：
Yang Xiaofeng
影响因子：
5
作者：
Schwank, Mike;Naderpour, Reza;Matzler, Christian
通讯作者：
Matzler, Christian
影响因子：
2.5
作者：
Andrea Saltelli;S. Tarantola;K. Chan
通讯作者：
Andrea Saltelli;S. Tarantola;K. Chan