2013
DOI: 10.1117/1.jrs.7.073540
|View full text |Cite
|
Sign up to set email alerts
|

Assessment of ice mapping system and moderate resolution imaging spectroradiometer snow cover maps over Colorado Plateau

Abstract: Satellite snow cover area (SCA) mapping using optical sensors has been known to suffer severe obstruction due to vegetation canopy and cloud cover. Several algorithms have been developed to reduce cloud cover contamination and enhance the SCA mapping. In this study we introduce the use of a daily SCA product from the Multisensor Snow and Ice Mapping System (IMS) at a nominal resolution of 4 km, assess its accuracy and error levels against in situ observations, and compare the IMS SCA product with the SCA produ… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

1
15
0
1

Year Published

2013
2013
2019
2019

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 18 publications
(17 citation statements)
references
References 21 publications
(29 reference statements)
1
15
0
1
Order By: Relevance
“…Although the combination of MODIS and AMSR-E/IMS can take advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the combination might reduce the spatial resolution of MODIS products because cloud pixels in MODIS are replaced by AMSR-E/IMS images, which have coarse spatial resolution; therefore, the accuracy of the cloud pixel reclassification fully depends on the accuracy of AMSR-E and IMS. In previous studies, AMSR-E and IMS data were widely used for monitoring snow, and both of them have relatively high accuracy [28][29][30][31]. On the other hand, because of the poor performance in the forest area, the Kappa test of integrated snow products shows moderate consistency with Landsat, and snow monitoring in this regions also remains a major problem for virtually all types of global remote sensing snow products, for which overall accuracy were seriously affected due to the forest canopy [37].…”
Section: Discussionmentioning
confidence: 99%
“…Although the combination of MODIS and AMSR-E/IMS can take advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the combination might reduce the spatial resolution of MODIS products because cloud pixels in MODIS are replaced by AMSR-E/IMS images, which have coarse spatial resolution; therefore, the accuracy of the cloud pixel reclassification fully depends on the accuracy of AMSR-E and IMS. In previous studies, AMSR-E and IMS data were widely used for monitoring snow, and both of them have relatively high accuracy [28][29][30][31]. On the other hand, because of the poor performance in the forest area, the Kappa test of integrated snow products shows moderate consistency with Landsat, and snow monitoring in this regions also remains a major problem for virtually all types of global remote sensing snow products, for which overall accuracy were seriously affected due to the forest canopy [37].…”
Section: Discussionmentioning
confidence: 99%
“…The overall accuracy (Oc, Oa), snow accuracy (Sc, Sa), and land accuracy (Lc, La) indices (Mazari et al, 2013) are calculated for clear-and all-sky conditions based on the confusion matrix between satellite data versus station measurement. The two error types evaluated are image underestimate error (IU) and image overestimate error (IO; Gao et al, 2010;Parajka & BlöSchl, 2008).…”
Section: Accuracy Assessment 321 Validation Indicesmentioning
confidence: 99%
“…For example, Yu et al (2016) used this method to realize cloud-free snow products on the Tibetan Plateau and the overall accuracy of the new product is 94% as compared with ground stations in allsky conditions. But it is worth mentioning that Mazari et al (2013) indicated that the overestimate error of the IMS is 5% more than that of the original MODIS during the ablation period and 1% to 2% less than original MODIS during the accumulation period. Therefore, to further reduce the possible errors from IMS, we first use temporal and spatial filtering to remove some clouds before combining MODIS and IMS.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Specifically, during the learning phase, connection weights are changed continuously in an adaptive manner, so that the mean square error between the observed and predicted output values is minimum. Extensive descriptions about working principles of ANN can be accessed fromLohani et al (2012).Runoff values observed in UER were simulated byTekeli et al (2005a) using daily average temperature, daily cumulated precipitation, and SCA obtained from MODIS sensor as input variables into snowmelt runoff model (SRM) for water year 2004. Also,…”
mentioning
confidence: 99%