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A Snow Water Equivalent Retrieval Framework Coupling 1D Hydrology and Passive Microwave Radiative Transfer Models.
- Published in:
- Remote Sensing, 2024, v. 16, n. 10, p. 1732, doi. 10.3390/rs16101732
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- Article
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022).
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- Earth System Science Data, 2024, v. 16, n. 5, p. 2501, doi. 10.5194/essd-16-2501-2024
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- Article
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method.
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- Cryosphere, 2024, v. 18, n. 4, p. 1561, doi. 10.5194/tc-18-1561-2024
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- Article
Prediction of The Binding Mode of NP‐G2‐044 Targeting Fascin and Its Drug Resistance Mutations.
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- Advanced Theory & Simulations, 2024, v. 7, n. 1, p. 1, doi. 10.1002/adts.202300427
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- Article
Can the Accuracy of Fine-Resolution Precipitation Products Be Assessed from the Surrounding Water Balance and Drought Chain (WBDC) in the Qinghai–Tibetan Plateau?
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- Remote Sensing, 2024, v. 16, n. 1, p. 79, doi. 10.3390/rs16010079
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- Publication type:
- Article
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022).
- Published in:
- Earth System Science Data Discussions, 2023, p. 1, doi. 10.5194/essd-2023-250
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- Article
Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme.
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- Journal of Advances in Modeling Earth Systems, 2023, v. 15, n. 9, p. 1, doi. 10.1029/2023MS003626
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- Article
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method.
- Published in:
- Cryosphere Discussions, 2023, p. 1, doi. 10.5194/tc-2023-85
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- Publication type:
- Article
Snow Density Retrieval in Quebec Using Space-Borne SMOS Observations.
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- Remote Sensing, 2023, v. 15, n. 8, p. 2065, doi. 10.3390/rs15082065
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- Article
Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale.
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- Climate Dynamics, 2023, v. 60, n. 5/6, p. 1523, doi. 10.1007/s00382-022-06376-0
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- Article
Comparison of Machine Learning-Based Snow Depth Estimates and Development of a New Operational Retrieval Algorithm over China.
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- Remote Sensing, 2022, v. 14, n. 12, p. N.PAG, doi. 10.3390/rs14122800
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- Article
A reduction in metabolism explains the tradeoffs associated with the long‐term adaptation of phytoplankton to high CO<sub>2</sub> concentrations.
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- New Phytologist, 2022, v. 233, n. 5, p. 2155, doi. 10.1111/nph.17917
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- Article
Lipid Remodeling Reveals the Adaptations of a Marine Diatom to Ocean Acidification.
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- Frontiers in Microbiology, 2021, v. 12, p. 1, doi. 10.3389/fmicb.2021.748445
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- Article
Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow.
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- Remote Sensing, 2021, v. 13, n. 10, p. 2012, doi. 10.3390/rs13102012
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- Article
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach.
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- Cryosphere, 2020, v. 14, n. 6, p. 1763, doi. 10.5194/tc-14-1763-2020
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- Article
High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area.
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- Remote Sensing, 2020, v. 12, n. 3, p. 460, doi. 10.3390/rs12030460
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- Article
Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China.
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- Remote Sensing, 2020, v. 12, n. 3, p. 507, doi. 10.3390/rs12030507
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- Article
Real-Time Snow Depth Estimation and Historical Data Reconstruction Over China Based on a Random Forest Machine Learning Approach.
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- Cryosphere Discussions, 2019, p. 1, doi. 10.5194/tc-2019-161
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- Article
The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China.
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- Remote Sensing, 2019, v. 11, n. 16, p. 1879, doi. 10.3390/rs11161879
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- Article
Comparison of the multi-layer HUT snow emission model with observations of wet snowpacks.
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- Hydrological Processes, 2014, v. 28, n. 3, p. 1071, doi. 10.1002/hyp.9617
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- Article