A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

Chen, Yuejun; Chen, Yumin; Wilson, John P.; Yang, Jiaxin; Su, Heng; Xu, Rui

doi:10.3390/rs15153821

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…However, there are exceptions to this trend. For example, European-affiliated researchers have conducted studies in Asia [108,109,[183][184][185][186]; American-affiliated researchers have explored regions in Asia [106,107,[187][188][189]; Chinese researchers have examined study areas in North America [190,191]. Affiliation at the time of publication was considered for this analysis.…”

Section: Spatial and Temporal Distribution Of The Examined Studiesmentioning

confidence: 99%

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Schilling,

Dietz,

Kuenzer

2024

Remote Sensing

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

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Section: Spatial and Temporal Distribution Of The Examined Studiesmentioning

confidence: 99%

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Schilling,

Dietz,

Kuenzer

2024

Remote Sensing

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Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering

Xu,

Chen,

Han

et al. 2024

Forests

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Terrestrial gross primary productivity (GPP) is a critical part of land carbon fluxes. Accurately quantifying GPP in terrestrial ecosystems and understanding its spatiotemporal dynamics are essential for assessing the capability of vegetation to absorb carbon from the atmosphere. Nevertheless, traditional remote sensing estimation models often require complex parameters and data inputs, and they do not account for spatial effects resulting from the distribution of monitoring sites. This can lead to biased parameter estimation and unstable results. To address these challenges, we have raised a spatial autocorrelation light gradient boosting machine model (SA-LGBM) to enhance GPP estimation. SA-LGBM combines reflectance information from remote sensing observations with eigenvector spatial filtering (ESF) methods to create a set of variables that capture continuous spatiotemporal variations in plant functional types and GPP. SA-LGBM demonstrates promising results when compared to existing GPP products. With the inclusion of eigenvectors, we observed an 8.5% increase in R2 and a 20.8% decrease in RMSE. Furthermore, the residuals of the model became more random, reducing the inherent spatial effects within them. In summary, SA-LGBM represents the first attempt to quantify the impact of spatial autocorrelation and addresses the limitations of underestimation present in existing GPP products. Moreover, SA-LGBM exhibits favorable applicability across various vegetation types.

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A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

Cited by 2 publications

References 61 publications

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering

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