Snow-Cover Mapping, Digital Image Information Extraction Techniques for from Remote Sensing Data

2012 ◽  
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Snow Cover ◽  
Digital Image ◽  
Remote Sensing Data ◽  
Extraction Techniques ◽  
Image Information ◽  
Sensing Data

Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

2021 ◽  
Vol 973 (7) ◽  
pp. 21-31
Author(s):  
Е.А. Rasputina ◽  
A.S. Korepova
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Spatial Resolution ◽  
Baikal Region ◽  
Remote Sensing Data ◽  
Meteorological Station ◽  
Sensing Data ◽  
Modis Data ◽  
Weather Stations ◽  
The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

scholarly journals PREFACE: TECHNICAL COMMISSION III

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 7-7
Author(s):  
J. Jiang ◽  
A. Shaker ◽  
H. Zhang
Keyword(s):  
Remote Sensing ◽  
Information Extraction ◽  
Working Group ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Deformation Monitoring ◽  
Atmospheric Environment ◽  
Surface Generation ◽  
Sensing Data ◽  
Working Groups

Abstract. The work of ISPRS Technical Commission III is devoted to remote sensing. For the XXIVth ISPRS CONGRESS – 2021 Edition (Digital), Technical Commission III received total 201 submissions, including 72 full papers and 138 abstracts. Among these submissions 38 are accepted as peer-reviewed contributions for publication in the ISPRS Annals, 118 were accepted for publication in the ISPRS Archives.These papers are dedicated mostly to topics of the 10 TC III working groups and 4 inter-commission working groups as follows – WG III/1: Thematic Information Extraction; WG III/2: Microwave Remote Sensing; WG III/3: SAR-based Surface Generation and Deformation Monitoring; WG III/4: Hyperspectral Image Processing; WG III/5: Information Extraction from LiDAR Intensity Data; WG III/6: Remote Sensing Data Fusion; WG III/7: Landuse and Landcover Change Detection; WG III/8: Remote Sensing of Atmospheric Environment; WG III/9: Cryosphere and Hydrosphere; WG III/10: Agriculture and Natural Ecosystems Modelling and Monitoring; ICWG III/II: Planetary Remote Sensing and Mapping; ICWG III/Iva: Disaster Assessment, Monitoring and Management; ICWG III/IVb: Remote Sensing Data Quality; ICWG III/IVc: Environment and Health. The papers and abstracts were evaluated by the experts in the field and Working Group Chairs according to content, significance, originality, relevance, and clearness of presentation.We would like to thank the authors for their contributions, the reviewers for their reviewing, the working group officers for their efforts on calling for papers, and the organizers of the Congress for publishing this volume.

scholarly journals Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

2018 ◽  
Vol 19 (11) ◽  
pp. 1777-1791 ◽  
Author(s):  
Nicholas Dawson ◽  
Patrick Broxton ◽  
Xubin Zeng
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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