Developing Daily Cloud-Free Snow Composite Products From MODIS Terra–Aqua and IMS for the Tibetan Plateau

Yu, Jinyuan; Zhang, Guoqing; Yao, Tandong; Xie, Hongjie; Zhang, Hongbo; Ke, Chang‐Qing; Yao, Ruohe

doi:10.1109/tgrs.2015.2496950

Cited by 84 publications

(64 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Huang et al [39] combined a digital elevation model to apply the snow line (SNOWL) algorithm [40] to different elevation regions of the Tibetan Plateau and developed a set of cloud removal algorithms based on optical and passive microwave remote sensing data to obtain a cloud-free daily snow cover product. Yu et al [41] combined MODIS with the IMS snow cover product, and the overall classification accuracy for the generated cloud-free daily snow cover product reached 94%.…”

Section: Introductionmentioning

confidence: 99%

Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Because the daytime cloud algorithm is expected to present more confidence than that for nighttime (Ackerman et al, 1998), using the nighttime LST for air temperature estimation may be influenced more by undetected clouds. For the TP, cloud contamination also constitutes a major problem, generating a mean daily cloud cover fraction of > 45 % (Yu et al, 2016). Thus, the effects of clouds are particularly essential for T air estimation in the TP.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Zhang

et al. 2016

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (T max ) and minimum (T min ) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (T air ) estimation accuracy. This study comprehensively analyzes the effects of clouds on T air estimation based on MODIS LST using detailed halfhourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the T min estimation accuracy. However, for T max estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between T max and daytime LST. The errors of T max estimation are obviously larger than those of T min and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate T min estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on T max and T min estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the T air estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

show abstract

“…The spatial comparison with Landsat-TM data (NDSI > 0.4) showed an overall accuracy of 77% to 91% for the MODIS product but they noted that thin snow cover was with both sensors not always properly detected. Zhang et al [18] and more recently Yu et al [19] considered MODIS data for their studies on the Tibetan Plateau. Zhang et al [18] combined Terra/Aqua snow products and aggregated it to multi-day snow maps to further reduce cloud cover for improved retrieval of SCD and other metrics for different drainage basins on the Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [18] combined Terra/Aqua snow products and aggregated it to multi-day snow maps to further reduce cloud cover for improved retrieval of SCD and other metrics for different drainage basins on the Tibetan Plateau. Yu et al [19] combined not only Terra/Aqua snow products but also included the daily snow map of the Interactive Multisensor Snow and Ice Mapping System (IMS) [20] to improve snow mapping significantly due to reduced cloud coverage. A study of Gafurov et al [21] applied an improved method to reduce cloud cover in the MODIS snow product.…”

Section: Introductionmentioning

confidence: 99%

“…Snowfall was also occasionally detected in October/November, but only lasted for one day [25]. Grab and Linde [24] presented a study on snow cover distribution in Lesotho, which was based on an 8-year long MODIS data series but without any gap-filling techniques to reduce cloud cover as shown by [13,16,18,19]. The high snowfall variability and its spatial distribution require a time series that should be as long as possible to support more reliable analyses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

2016

View full text Add to dashboard Cite

Abstract:In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000-2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

show abstract

Developing Daily Cloud-Free Snow Composite Products From MODIS Terra–Aqua and IMS for the Tibetan Plateau

Cited by 84 publications

References 47 publications

Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

Contact Info

Product

Resources

About