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Open AccessArticle

Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

1
Department of European and Mediterranean Cultures, Architecture, Environment, Cultural Heritage, University of Basilicata, 75100 Matera, Italy
2
Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
3
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy
4
Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education, School of Water and Environment, Chang’an University, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 509; https://doi.org/10.3390/rs12030509
Received: 10 January 2020 / Revised: 1 February 2020 / Accepted: 3 February 2020 / Published: 5 February 2020
(This article belongs to the Special Issue Microwave Remote Sensing for Hydrology)
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. View Full-Text
Keywords: Tibetan Plateau; soil moisture; root zone; triple collocation; CDF matching; SMAR Tibetan Plateau; soil moisture; root zone; triple collocation; CDF matching; SMAR
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MDPI and ACS Style

Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sens. 2020, 12, 509. https://doi.org/10.3390/rs12030509

AMA Style

Zhuang R, Zeng Y, Manfreda S, Su Z. Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sensing. 2020; 12(3):509. https://doi.org/10.3390/rs12030509

Chicago/Turabian Style

Zhuang, Ruodan; Zeng, Yijian; Manfreda, Salvatore; Su, Zhongbo. 2020. "Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau" Remote Sens. 12, no. 3: 509. https://doi.org/10.3390/rs12030509

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