Name
Title | ||
|---|---|---|
Improvement Of Long-Term Snow Depth Product Accuracy From Passive Microwave Satellite Observations : A Case Study With Snodas Data |
Abstract | ||
|---|---|---|
This paper presented a pixel-based statistical regression method based on a high-resolution snow depth product to improve the accuracy of passive microwave snow depth retrievals. The statistical regression relation was established based on a linear relationship between the snow depth and the brightness temperature (TB) difference. The coefficients of these regression equations were derived using the snow depth product of Snow Data Assimilation System (SNODAS) as a reference. The regression relation was established over the winter period of 2013 and 2014. Passive microwave SD maps can be produced using the regression relation. Then retrieved SD was evaluated by the SNODAS SD product from November to December in 2010. The root mean square error (RMSE) and correlations (R) were computed between the SD retrievals and the SNODAS SD product. The R (mostly greater than 0.55) and RMSE (mostly lower than 16cm) maps showed a good agreement between the retrieved SD and the SNODAS SD product. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7730272 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
snow depth, regression analysis, long-term record, SNODAS | Meteorology,Satellite,Brightness temperature,Regression,Computer science,Regression analysis,Remote sensing,Mean squared error,Microwave imaging,Data assimilation,Snow | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojing Liu | 1 | 0 | 0.68 |
| Lingmei Jiang | 2 | 108 | 34.89 |
| Gongxue Wang | 3 | 0 | 2.37 |
| Shirui Hao | 4 | 1 | 1.02 |