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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
by
Manfreda, Salvatore
, Zhuang, Ruodan
, Zeng, Yijian
, Su, Zhongbo
in
cdf matching
/ China
/ climate
/ climatology
/ cumulative distribution
/ data collection
/ least squares
/ monsoon season
/ remote sensing
/ rhizosphere
/ root zone
/ satellites
/ smar
/ soil moisture
/ soil water
/ tibetan plateau
/ triple collocation
2020
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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
by
Manfreda, Salvatore
, Zhuang, Ruodan
, Zeng, Yijian
, Su, Zhongbo
in
cdf matching
/ China
/ climate
/ climatology
/ cumulative distribution
/ data collection
/ least squares
/ monsoon season
/ remote sensing
/ rhizosphere
/ root zone
/ satellites
/ smar
/ soil moisture
/ soil water
/ tibetan plateau
/ triple collocation
2020
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Do you wish to request the book?
Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
by
Manfreda, Salvatore
, Zhuang, Ruodan
, Zeng, Yijian
, Su, Zhongbo
in
cdf matching
/ China
/ climate
/ climatology
/ cumulative distribution
/ data collection
/ least squares
/ monsoon season
/ remote sensing
/ rhizosphere
/ root zone
/ satellites
/ smar
/ soil moisture
/ soil water
/ tibetan plateau
/ triple collocation
2020
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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
Journal Article
Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau
2020
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Overview
It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.
Publisher
MDPI AG
Subject
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