Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model-Reference-Cited by-同舟云学术

Improving Snow Estimates Through Assimilation of MODIS Fractional Snow Cover Data Using Machine Learning Algorithms and the Common Land Model

Published:2021-07 Issue:7 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Res

Author:

Hou Jinliang¹^ORCID,Huang Chunlin¹^ORCID,Chen Weijing²^ORCID,Zhang Ying¹

Affiliation:

1. Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research Station Northwest Institute of Eco‐Environment and Resources Chinese Academy of Sciences Lanzhou China

2. Department of Geological Sciences Jackson School of Geosciences The University of Texas at Austin Austin TX USA

Funder

Chinese Academy of Sciences

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029010

Reference74 articles.

1. Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

2. Assimilating remotely sensed snow observations into a macroscale hydrology model

3. Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation

4. Impacts of snow cover fraction data assimilation on modeled energy and moisture budgets

5. Potential impacts of a warming climate on water availability in snow-dominated regions

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