Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US
Abstract
:1. Introduction
2. Interpolated In Situ SWE Data over US
3. Simple Scattering Model for Dry Snow
4. Using Dual Polarization Dual Frequency Backscattered Power to Estimate SWE
4.1. Snow in the US with Satisfying SWE Retrieval Limitations
4.2. Sensitivity of Retrieval to Different Parameters
5. Using Differential Interferometry to Estimate SWE
5.1. Snow in US with Satisfying SWE Retrieval Limitations
5.2. Sensitivity of Retrieval to Different Parameters
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix Generating Interpolated In Situ Data
- The ratio of observed SWE over estimated net snowfall (accumulated snowfall minus accumulated snow ablation), rather than SWE itself, is used for interpolation from point measurements to other points or pixels [21].
- The snowfall versus rainfall is separated using a daily 2-m air temperature threshold based on station data, and the snow ablation is also estimated as a function of temperature based on station data [21].
- A new snow density parameterization [22] is developed to combine the SWE and snow depth measurements from hundreds of snow telemetry (SNOTEL) sites with the snow depth measurements from thousands of continuity of operations (COOP) sites. This snow parameterization is driven by PRISM (the parameter-elevation regressions on independent slopes model) 4 km daily precipitation and temperature data and it includes up to 10 snow layers (depending the total SWE). Each day, the snow mass and snow depth for each layer, and total snowpack liquid water, are updated. This parameterization, which directly includes the impact of total snowpack liquid water on snowpack density, has been validated using the SNOTEL snow density data [22].
- Liquid water increment in the snowpack due to snowmelt is estimated as a function of daily 2-m air temperature. The total snowpack liquid water is part of the total SWE that is interpolated (see the first step) from in situ measurements of SWE from SNOTEL sites and snow depth from COOP sites (which are used along with our snow density model (see the third step) to obtain the in situ SWE). In other words, the snowpack liquid water product is constrained by in situ SWE and snow depth measurements, but it has not been validated itself due to the lack of direct measurements.
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% points over US | 66 | 66 | 66 | 55 | 55 | 55 | 53 | 53 | 53 |
, , | |||||||||
% points over US | 46 | 46 | 46 | 39 | 39 | 39 | 38 | 38 | 37 |
, , , | |||||||||
% of SWE over US | 35 | 35 | 35 | 17 | 17 | 17 | 16 | 16 | 16 |
, , | |||||||||
% of SWE over US | 23 | 23 | 23 | 11 | 11 | 11 | 10 | 10 | 10 |
, , , |
% points over US | 74 | 74 | 74 | 65 | 65 | 65 | 63 | 63 | 63 |
, , | |||||||||
% points over US | 47 | 47 | 47 | 44 | 44 | 44 | 43 | 43 | 43 |
, , , | |||||||||
% of SWE over US | 41 | 41 | 41 | 20 | 20 | 20 | 18 | 18 | 18 |
, , | |||||||||
% of SWE over US | 16 | 16 | 16 | 10 | 10 | 10 | 10 | 10 | 10 |
, , , |
% points over US | 77 | 77 | 77 | 58 | 58 | 58 | 55 | 55 | 55 |
, , daily | |||||||||
% points over US | 55 | 55 | 55 | 41 | 41 | 41 | 39 | 39 | 39 |
, , daily , | |||||||||
% of SWE over US | 72 | 72 | 72 | 19 | 19 | 19 | 17 | 17 | 16 |
, , daily | |||||||||
% of SWE over US | 53 | 53 | 53 | 12 | 12 | 12 | 11 | 11 | 11 |
, , daily , |
% points over US | 82 | 82 | 82 | 68 | 68 | 68 | 66 | 66 | 66 |
, , daily | |||||||||
% points over US | 53 | 53 | 53 | 47 | 47 | 47 | 46 | 46 | 46 |
, , daily , | |||||||||
% of SWE over US | 60 | 60 | 60 | 21 | 21 | 21 | 20 | 19 | 19 |
, , daily | |||||||||
% of SWE over US | 32 | 32 | 32 | 12 | 12 | 12 | 11 | 11 | 11 |
, , daily , |
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Oveisgharan, S.; Esteban-Fernandez, D.; Waliser, D.; Friedl, R.; Nghiem, S.; Zeng, X. Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US. Remote Sens. 2020, 12, 2021. https://doi.org/10.3390/rs12122021
Oveisgharan S, Esteban-Fernandez D, Waliser D, Friedl R, Nghiem S, Zeng X. Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US. Remote Sensing. 2020; 12(12):2021. https://doi.org/10.3390/rs12122021
Chicago/Turabian StyleOveisgharan, Shadi, Daniel Esteban-Fernandez, Duane Waliser, Randall Friedl, Son Nghiem, and Xubin Zeng. 2020. "Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US" Remote Sensing 12, no. 12: 2021. https://doi.org/10.3390/rs12122021