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

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 (~10yr) 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.

How to cite: Zhuang, R.; Zeng, Y.; Manfreda, S.; Su, Z. Quantifying Long-term Land Surface and Root Zone Soil Moisture over Tibetan Plateau. Remote Sens. 202012, 509. [pdf]

Salvatore Manfreda
Author: Salvatore Manfreda

Salvatore Manfreda received his Ph.D. in Environmental Monitoring and Modeling in 2004. He started his career as researcher associate at Princeton University (2004-2005), became Assistant Professor in 2008, and Associate Professor in Hydraulic Constructions and Hydrology in 2014 at the University of Basilicata. He has a broad interest in hydrology and ecohydrology, with particular emphasis on distributed modeling, flood prediction, stochastic processes in hydrology, soil moisture process, delineation of flood prone areas, mathematical filters and vegetation patterns. His research activity has produced more than 120 publications.

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# File Description Date added Added by File size Downloads
1 pdf 2020_Zhuang_et_al_remotesensing 5 February 2020 18:13 Salvatore Manfreda 7 MB 35