XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: EVALUATING THE CONSISTENCY OF REMOTE SENSING BASED SNOW DEPTH PRODUCTS IN ARID ZONE OF WESTERN CHINA Qiming Zhou & Bo Sun

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia EVALUATING THE CONSISTENCY OF REMOTE SENSING BASED SNOW DEPTH PRODUCTS IN ARID ZONE OF WESTERN CHINA Qiming Zhou & Bo Sun* *corresponding author, E-mail: sun.bo(gmsn.com Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong, S.A.R., China KEY WORDS: Snow ice, depth, global-environmental-databases, evaluation, data reliability, western China ABSTRACT: Snow cover is a sensitive indicator of global climate change. Among various snow cover parameters, snow depth which can indicate snow accumulation is essential for retrieving snow water equivalent. In arid zone of western China, based on different inversion models, snow depth products retrieved from passive microwave remote sensing sensors have been issued. However, none of them can promise a high accuracy due to the spatial heterogeneity of snow cover especially in mountain areas with complex terrain. This study aims to analyse the reliability of existing long-term snow depth products in arid zone of western China. Two datasets are compared including GlobSnow snow water equivalent (SWE) product and snow depth dataset provided by Environmental and Ecological Science Data Center for West China. Statistical techniques like regression and intra-class correlation coefficient (ICC) models are employed to examine the consistency of these two remote sensing based snow depth products in a selected sampling site. More than 260 samples during three years are tested covering from snow falling to snow melting periods. Result shows that there is a discrepancy between the two datasets. Accordingly, remote sensing based snow depth measurement is not reliable in mountain areas in arid zone of western China. This study gives an awareness of the stabilities of current snow depth detection models. A further study is expected to calibrate snow depth products based on in-situ observation and measurements from ground monitoring stations. 1. INTRODUCTION Snow cover in mountain areas is increasingly considered as one of the most sensitive indicators of global climate change due to the less direct influence of human activities. Under the background of global warming, the long-term trend of snow cover change has been focused. Among snow cover change studies, snow depth which indicates snow accumulation is an essential parameter for retrieving the amount of water contained within the snowpack (snow water equivalent). Passive microwave remote sensing data has shown the capability of providing a large-extent and successive dataset for long-term snow cover change studies in terms of snow depth information. The retrieval of snow depth from passive microwave data is on the basis of scattering theory. Retrieval algorithms are based on the difference in emissivity between two microwave frequencies, e.g., 18/19 and 36/37 GHz (Chang et al., 1987). Historical passive microwave remote sensing data can date back to 1978. Commonly used sensors and platforms include SMMR carried by Nimbus-7, SSM/I carried by DMSP satellite series and AMSR-E carried by EOS satellite. Based on the same data source, various snow depth retrieval algorithms have been developed and several snow products have been released. At present, although long-term snow depth and snow water equivalent products at global scale have been issued, none of them can promise a high accuracy for everywhere in the world. One of the major reasons is that physical parameters of snow cover can be different at different places. Besides, as a known issue, the error caused by spatial heterogeneity is hard to be solved. Data accuracy might become lower in mountain areas and in the place where underlying surface of snow cover is complex. In this study, two existing snow products are collected. Depth information is retrieved from the same long-term passive microwave remote sensing dataset. We attempt to evaluate the consistency of snow depth measurements from these two products and assess the reliability of the products for mountain areas in arid zone of western China. 2. METHODOLOGY Pairs of snow depth measurements sampled from two snow datasets are compared for examining their differences by using statistical techniques. Given that the data is at ratio scale in terms of the level of measurement, regression and intra-class correlation coefficient (ICC) models are employed to test the consistency of the two datasets. 2.1 Existing snow depth products Two existing snow depth products are tested in this study including Long-term Snow Depth Dataset of China and GlobSnow SWE product. Long-term Snow Depth Dataset of China is issued by Environmental and Ecological Science Data Center for West China (EESDCWestChina). Snow depth retrieval algorithm is based on a modified Chang's algorithm so that the algorithm can be suitable for snow depth retrieval in China (Che, et al., 2008). For the accuracy of snow depth, it is reported that the standard deviations can reach to 60 mm regarding different passive microwave sensors (Che, et al., 2008). GlobSnow SWE product is issued by Finish Meteorological Institute, which is supported by a European Space Agency (ESA) project. Snow depth retrieval algorithm is based on a semi-empirical snow emission model (called HUT model) (Pulliainen, 2006).

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