A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series

Soil freeze–thaw (F/T) processes are a typical physical phenomenon on the Qinghai-Tibetan Plateau (QTP), significantly impacting regional climate change and the hydrological cycle. This study presents a Seasonal-Trend Decomposition using Loess and Long Short-Term Memory (STL-LSTM) method to detect s...

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Main Authors: Qi Liu, Shuangcheng Zhang, Zhongmin Ma, Xin Zhou, Tao Wang
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:International Journal of Applied Earth Observations and Geoinformation
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Online Access:http://www.sciencedirect.com/science/article/pii/S1569843225000755
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author Qi Liu
Shuangcheng Zhang
Zhongmin Ma
Xin Zhou
Tao Wang
author_facet Qi Liu
Shuangcheng Zhang
Zhongmin Ma
Xin Zhou
Tao Wang
author_sort Qi Liu
collection DOAJ
description Soil freeze–thaw (F/T) processes are a typical physical phenomenon on the Qinghai-Tibetan Plateau (QTP), significantly impacting regional climate change and the hydrological cycle. This study presents a Seasonal-Trend Decomposition using Loess and Long Short-Term Memory (STL-LSTM) method to detect spatiotemporal variations in soil F/T on the QTP using time series data from the Cyclone Global Navigation Satellite System (CYGNSS). The model was validated against ERA5 soil temperature data (0–7 cm) and independent in-situ observations, demonstrating good consistency. The SHapley Additive exPlanations (SHAP) model was integrated into the STL-LSTM framework to quantitatively evaluate the contributions of input features to F/T retrieval, revealing that time features contributes the most to retrieval results, followed by surface reflectivity. Moreover, spatiotemporal analysis of QTP F/T dynamics shows prominent seasonal patterns, with topography-induced shielding delaying thawing in central QTP regions and freezing trends extending from low (28°N) to high latitudes (36°N). The proposed method offers a new pathway for monitoring freeze–thaw transitions in high-latitude regions and holds potential for expansion into future high-frequency and multi-polarization GNSS-R missions.
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issn 1569-8432
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publishDate 2025-03-01
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series International Journal of Applied Earth Observations and Geoinformation
spelling doaj-art-0c6df6d0bee849af83c6fa36694130792025-08-20T03:05:39ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-03-0113710442810.1016/j.jag.2025.104428A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time seriesQi Liu0Shuangcheng Zhang1Zhongmin Ma2Xin Zhou3Tao Wang4College of Geomatics, Xi’an University of Science and Technology, Xi’an, 710054, China; Corresponding author.College of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710054, China; CommSensLab, Universitat Politècnica de Catalunya (UPC), Barcelona, 08860, SpainCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an, 710054, ChinaSoil freeze–thaw (F/T) processes are a typical physical phenomenon on the Qinghai-Tibetan Plateau (QTP), significantly impacting regional climate change and the hydrological cycle. This study presents a Seasonal-Trend Decomposition using Loess and Long Short-Term Memory (STL-LSTM) method to detect spatiotemporal variations in soil F/T on the QTP using time series data from the Cyclone Global Navigation Satellite System (CYGNSS). The model was validated against ERA5 soil temperature data (0–7 cm) and independent in-situ observations, demonstrating good consistency. The SHapley Additive exPlanations (SHAP) model was integrated into the STL-LSTM framework to quantitatively evaluate the contributions of input features to F/T retrieval, revealing that time features contributes the most to retrieval results, followed by surface reflectivity. Moreover, spatiotemporal analysis of QTP F/T dynamics shows prominent seasonal patterns, with topography-induced shielding delaying thawing in central QTP regions and freezing trends extending from low (28°N) to high latitudes (36°N). The proposed method offers a new pathway for monitoring freeze–thaw transitions in high-latitude regions and holds potential for expansion into future high-frequency and multi-polarization GNSS-R missions.http://www.sciencedirect.com/science/article/pii/S1569843225000755Soil freeze/thaw stateCyclone global navigation satellite systemSeasonalityTime seriesQinghai-Tibetan Plateau
spellingShingle Qi Liu
Shuangcheng Zhang
Zhongmin Ma
Xin Zhou
Tao Wang
A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
International Journal of Applied Earth Observations and Geoinformation
Soil freeze/thaw state
Cyclone global navigation satellite system
Seasonality
Time series
Qinghai-Tibetan Plateau
title A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
title_full A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
title_fullStr A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
title_full_unstemmed A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
title_short A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
title_sort novel approach to retrieving the surface soil freeze thaw state in the qinghai tibetan plateau using the seasonality of cygnss time series
topic Soil freeze/thaw state
Cyclone global navigation satellite system
Seasonality
Time series
Qinghai-Tibetan Plateau
url http://www.sciencedirect.com/science/article/pii/S1569843225000755
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