The Variability of Snow Cover and Its Contribution to Water Resources in the Chinese Altai Mountains from 2000 to 2022 DOI Creative Commons

Fengchen Yu,

Puyu Wang, Lin Liu

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(24), P. 5765 - 5765

Published: Dec. 17, 2023

As one of the major water supply systems for inland rivers, especially in arid and semi-arid regions, snow cover strongly affects hydrological cycles. In this study, remote sensing datasets combined with in-situ observation data from a route survey were used to investigate changes parameters on Chinese Altai Mountains 2000 2022, responses climate hydrology also discussed. The annual frequency (SCF), area, depth (SD), density 45.03%, 2.27 × 104 km2, 23.4 cm, ~0.21 g·cm−3, respectively. equivalent ranged 0.58 km3 1.49 km3, an average 1.12 km3. Higher lower SCF mainly distributed at high elevations both sides Irtysh river. maximum minimum occurred Burqin River Basin Lhaster Basin. years SCF, abnormal westerly airflow was favorable vapor transport Mountains, resulting strong snowfall, vice versa low SCF. There significant seasonal differences impact temperature precipitation regional changes. snowmelt runoff ratios 11.2%, 25.30%, 8.04%, 30.22%, 11.56% Irtysh, Kayit, Haba, Kelan, Basins. Snow meltwater has made contribution Mountains.

Language: Английский

Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques DOI
Ying Ma, Xiaodong Huang,

Xia-Li Yang

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 205, P. 246 - 262

Published: Oct. 18, 2023

Language: Английский

Citations

18
Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications DOI Creative Commons
Samuel Schilling, A.J. Dietz,

Claudia Kuenzer

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(6), P. 1085 - 1085

Published: March 20, 2024

Snow plays a crucial role in the global water cycle, providing to over 20% of world’s population and serving as vital component for flora, fauna, climate regulation. Changes snow patterns due warming have far-reaching impacts on management, agriculture, other economic sectors such winter tourism. Additionally, they implications environmental stability, prompting migration cultural shifts snow-dependent communities. Accurate information its variables is, thus, essential both scientific understanding societal planning. This review explores potential remote sensing monitoring equivalent (SWE) large scale, analyzing 164 selected publications from 2000 2023. Categorized by methodology content, analysis reveals growing interest topic, with concentration research North America China. Methodologically, there is shift passive microwave (PMW) inversion algorithms artificial intelligence (AI), particularly Random Forest (RF) neural network (NN) approaches. A majority studies integrate PMW data auxiliary information, focusing thematically research, limited incorporation into broader contexts. Long-term (>30 years) suggest general decrease SWE Northern Hemisphere, though regional seasonal variations exist. Finally, suggests future directions addressing issues, downsampling detailed analyses, conducting interdisciplinary studies, incorporating forecasting enable more widespread applications.

Language: Английский

Citations

5
Significant decreasing trends in snow cover and duration in Northeast China during the past 40 years from 1980 to 2020 DOI
Yanlin Wei, Xiaofeng Li, Lingjia Gu

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 626, P. 130318 - 130318

Published: Oct. 17, 2023

Language: Английский

Citations

10
Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable machine learning and SHAP analysis DOI Creative Commons
Mahboubeh Boueshagh, J. M. Ramage, M. J. Brodzik

et al.

Frontiers in Remote Sensing, Journal Year: 2025, Volume and Issue: 6

Published: March 19, 2025

Seasonal snowpack is a crucial water resource, making accurate Snow Water Equivalent (SWE) estimation essential for management and environmental assessment. This study introduces novel approach to Passive Microwave (PMW) SWE estimation, leveraging the strong, unexpected correlation between Spatial Standard Deviation (SSD) of PMW Calibrated Enhanced-Resolution Brightness Temperatures (CETB). By integrating spatial statistics, linear correlation, machine learning (Linear Regression, Random Forest, GBoost, XGBoost), SHapley Additive exPlanations (SHAP) analysis, this research evaluates CETB SSD as key feature improve estimations or other retrievals by investigating drivers SSD. Analysis at three sites—Monument Creek, AK; Mud Flat, ID; Jones Pass, CO—reveals site-specific variability, showing correlations 0.64, 0.82, 0.72 with SNOTEL SWE, 0.67, 0.89, 0.67 PMW-derived respectively. Among sites, Monument Creek exhibits highest ML model accuracy, Forest XGBoost achieving test R 2 values 0.89 RMSEs ranging from 0.37 0.39 [K] when predicting SHAP analysis highlights driver while soil moisture plays larger role Pass. In snow-dominated regions less surface heterogeneity, such SSDs can capturing snow variability. complex environments like aid accounting factors that impact dynamics. enhance remote sensing capabilities across diverse environments, benefiting hydrological modeling resource management.

Language: Английский

Citations

0
Snow depth inversion and mapping at 500 m resolution from 1980 to 2020 in Northeast China using radiative transfer model and machine learning DOI
Yanlin Wei, Xiaofeng Li, Lingjia Gu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104533 - 104533

Published: April 14, 2025

Language: Английский

Citations

0
Spatiotemporal Changes of Snow Depth in Western Jilin, China from 1987 to 2018 DOI
Yanlin Wei, Xiaofeng Li, Lingjia Gu

et al.

Chinese Geographical Science, Journal Year: 2024, Volume and Issue: 34(2), P. 357 - 368

Published: March 1, 2024

Language: Английский

Citations

1
Impacts of snow cover seasonality on spring land surface phenology of forests in Changbai mountains of Northeast China DOI
Shuai Chang, Fang Huang, Hong S. He

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 927, P. 171965 - 171965

Published: March 26, 2024

Language: Английский

Citations

1
A Snow Depth Downscaling Algorithm Based on Deep Learning Fusion of Enhanced Passive Microwave and Cloud-Free Optical Remote Sensing Data in China DOI Creative Commons

Zhao Zi-sheng,

Xiaohua Hao, Donghang Shao

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4756 - 4756

Published: Dec. 20, 2024

High spatial resolution snow depth (SD) is crucial for hydrological, ecological, and disaster research. However, passive microwave SD product (10/25 km) increasingly insufficient to meet contemporary requirements due its coarse resolution, particularly in heterogeneous alpine areas. In this study, we develop a superior downscaling algorithm based on the FT-Transformer (Feature Tokenizer + Transformer) model, termed FTSD. This fuses latest calibrated enhanced brightness temperature (CETB) (3.125/6.25 with daily cloud-free optical data (500 m), including cover fraction (SCF) days (SCD). Developed evaluated using 42,692 ground measurements across China from 2000 2020, FTSD demonstrated notable improvements accuracy of retrieval. Specifically, RMSE temporal spatiotemporal independent validation 7.64 cm 9.74 cm, respectively, indicating reliable generalizability stability. Compared long-term series (25 km, = 10.77 cm), m, 7.67 cm) provides accuracy, especially improved by 48% deep (> 40 cm). Moreover, higher effectively captures SD’s heterogeneity mountainous regions China. When compared algorithms utilizing raw TB traditional random forest CETB model optimize 10.08% 4.84%, which demonstrates superiority regarding sources regression methods. Collectively, these results demonstrate that innovative exhibits performance has potential provide robust foundation meteorological environmental

Language: Английский

Citations

1
Snow Depth Retrieval With Multiazimuth and Multisatellite Data Fusion of GNSS-IR Considering the Influence of Surface Fluctuation DOI
Pengfei Ma, Chunlin Huang, Jinliang Hou

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2023, Volume and Issue: 61, P. 1 - 14

Published: Jan. 1, 2023

Utilizing global navigation satellite system interferometric reflectometry (GNSS-IR) technology to obtain snow depth (SD) has the advantages of all-day, low cost and large amount available data. At present, there is still a lack in-depth research on influence weak surface fluctuation SD inversion. In this paper, we investigate GNSS-IR retrieval by analyzing variation reflection height in different azimuths through clustering based satellites during snow-free period, correction value each cluster obtained correct snowy most probable daily multi-azimuth multi-satellite fusion. order prove rationality effectiveness proposed method, data two GNSS observation stations (AB33 P351) with elevations from Plate Boundary Observation (PBO) are used carry out experiments. The results show that accuracy fusion after improved significantly. correlation coefficient (R) increased 5.04%, root mean square error (RMSE) decreased 43.49%, absolute (MAE) 47.62%. Additionally, average R, RMSE, MAE 0.99, 0.02m 0.01m respectively. (ME) methods also significantly reduced. study provides insightful new ideas for inverting using signals.

Language: Английский

Citations

1
Snow Depth Inversion Based on Simulated Pixel-Scale Ground Measurements DOI
Ying Ma, Xiaodong Huang, Yuxin Li

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 12

Published: Jan. 1, 2024

The absence of pixel-scale ground measurements presents a notable challenge in the creation and validation passive microwave snow depth (SD) inversion models, due to huge scale mismatch between remotely sensed pixels. coarse spatial resolution remote sensing products further complicates accurate representation detailed SD information space, particularly mountainous regions. In this study, values were generated using regression kriging (RK) simple averaging (SA) point-to-surface upscaling their impact on two downscaling Chang's best subset models evaluated, respectively. results indicate that RK model exhibits superior accuracy alignment with observed SD, yielding root-mean-square error (RMSE) mean absolute (MAE) 1.75 1.41 cm, performance SA is affected by thickness within quadrat, RMSE MAE 2.35 1.92 However, does not significantly influence algorithm. Nevertheless, model, utilizing ascending brightness temperature data simulated measurements, achieves ideal suitability for complex terrain areas, 2.04 1.53 This study expected provide valuable insights developing strategies addressing effect challenges inversion.

Language: Английский

Citations

0