Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover
We developed a methodology to evaluate quantitative uncertainty characteristics of satellite data retrievals including the contribution of systematic error and statistical error. This is introduced by assessing the total product error of optical, satellite data-based, Fractional Snow Cover (FSC) est...
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| Veröffentlicht in: | Remote sensing of environment 2018-06, Vol.212, p.103-113 |
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| creator | Salminen, Miia Pulliainen, Jouni Metsämäki, Sari Ikonen, Jaakko Heinilä, Kirsikka Luojus, Kari |
| description | We developed a methodology to evaluate quantitative uncertainty characteristics of satellite data retrievals including the contribution of systematic error and statistical error. This is introduced by assessing the total product error of optical, satellite data-based, Fractional Snow Cover (FSC) estimates. Here the FSC estimation is based on an algorithm allowing the consideration of the effect of different error sources; a semi-empirical reflectance model describing the relationship of the observed reflectance and FSC through several variables and parameters. We assume that after the statistical error analysis, the remaining portion of total product error arises due to systematic factors. Hence, first we define a statistical error component through the theory of error propagation, and then estimate the total product error (PE) by using in situ observations on FSC, and finally derive the systematic error from these two error components. The experimental approach for estimating PE is conducted through an analysis of the observed estimation errors (i.e. residuals) in the GlobSnow Snow Extent (SE) v2.1 products on FSC. In practice, independent in situ snow course observations from Finland on FSC are compared to corresponding satellite FSC estimates to quantify the residuals. The approach is then illustrated for an extended region of corresponding European boreal forest. Our results show that the total PE in the GlobSnow FSC product is significantly higher than the originally provided statistical error. This is due to deficiencies in the parameterization of the applied forward modelling approach, in particular in the consideration of the forest canopy effects.
•Statistical error is not sufficient to describe full error of GlobSnow SE product.•GlobSnow FSC-product error is dominated by the systematic error in boreal forests.•A novel experimental analysis approach using a unique regional data set is proposed.•Using the approach all GlobSnow SE error components are defined for the first time. |
| doi_str_mv | 10.1016/j.rse.2018.04.038 |
| format | Article |
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•Statistical error is not sufficient to describe full error of GlobSnow SE product.•GlobSnow FSC-product error is dominated by the systematic error in boreal forests.•A novel experimental analysis approach using a unique regional data set is proposed.•Using the approach all GlobSnow SE error components are defined for the first time.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2018.04.038</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Accuracy ; Boreal forests ; Empirical analysis ; Error analysis ; Estimation errors ; Forest canopy ; Forests ; Fractional interests ; Fractional snow cover ; Mapping ; Optical ; Optical industry ; Parameterization ; Reflectance ; Remote sensing ; Satellite data ; Satellites ; Seasonal snow ; Seasonal variations ; Snow ; Snow cover ; Snow mapping ; Statistical analysis ; Statistics ; Taiga ; Uncertainty</subject><ispartof>Remote sensing of environment, 2018-06, Vol.212, p.103-113</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright Elsevier BV Jun 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c240t-994f47d56b24f56963d3b7505082be2e6a30ce0de61d63a388598fb3bd8f01613</citedby><cites>FETCH-LOGICAL-c240t-994f47d56b24f56963d3b7505082be2e6a30ce0de61d63a388598fb3bd8f01613</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2018.04.038$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids></links><search><creatorcontrib>Salminen, Miia</creatorcontrib><creatorcontrib>Pulliainen, Jouni</creatorcontrib><creatorcontrib>Metsämäki, Sari</creatorcontrib><creatorcontrib>Ikonen, Jaakko</creatorcontrib><creatorcontrib>Heinilä, Kirsikka</creatorcontrib><creatorcontrib>Luojus, Kari</creatorcontrib><title>Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover</title><title>Remote sensing of environment</title><description>We developed a methodology to evaluate quantitative uncertainty characteristics of satellite data retrievals including the contribution of systematic error and statistical error. This is introduced by assessing the total product error of optical, satellite data-based, Fractional Snow Cover (FSC) estimates. Here the FSC estimation is based on an algorithm allowing the consideration of the effect of different error sources; a semi-empirical reflectance model describing the relationship of the observed reflectance and FSC through several variables and parameters. We assume that after the statistical error analysis, the remaining portion of total product error arises due to systematic factors. Hence, first we define a statistical error component through the theory of error propagation, and then estimate the total product error (PE) by using in situ observations on FSC, and finally derive the systematic error from these two error components. The experimental approach for estimating PE is conducted through an analysis of the observed estimation errors (i.e. residuals) in the GlobSnow Snow Extent (SE) v2.1 products on FSC. In practice, independent in situ snow course observations from Finland on FSC are compared to corresponding satellite FSC estimates to quantify the residuals. The approach is then illustrated for an extended region of corresponding European boreal forest. Our results show that the total PE in the GlobSnow FSC product is significantly higher than the originally provided statistical error. This is due to deficiencies in the parameterization of the applied forward modelling approach, in particular in the consideration of the forest canopy effects.
•Statistical error is not sufficient to describe full error of GlobSnow SE product.•GlobSnow FSC-product error is dominated by the systematic error in boreal forests.•A novel experimental analysis approach using a unique regional data set is proposed.•Using the approach all GlobSnow SE error components are defined for the first time.</description><subject>Accuracy</subject><subject>Boreal forests</subject><subject>Empirical analysis</subject><subject>Error analysis</subject><subject>Estimation errors</subject><subject>Forest canopy</subject><subject>Forests</subject><subject>Fractional interests</subject><subject>Fractional snow cover</subject><subject>Mapping</subject><subject>Optical</subject><subject>Optical industry</subject><subject>Parameterization</subject><subject>Reflectance</subject><subject>Remote sensing</subject><subject>Satellite data</subject><subject>Satellites</subject><subject>Seasonal snow</subject><subject>Seasonal variations</subject><subject>Snow</subject><subject>Snow cover</subject><subject>Snow mapping</subject><subject>Statistical analysis</subject><subject>Statistics</subject><subject>Taiga</subject><subject>Uncertainty</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kEtPAyEUhYnRxPr4Ae5IXM94GZgZJq5MfSZN3OiaMHBJadqhAtX476WpcemKuzjfyeEj5IpBzYB1N6s6JqwbYLIGUQOXR2TGZD9U0IM4JjMALirRtP0pOUtpBcBa2bMZ2dxjxrjxk84-TDQ4upsMxqz9lL-pWeqoTQn4lL1J1IVI8xJp0hnXa5-RWp11NeqElmLJbP5q3B4st17TNIUvasInxgty4vQ64eXve07eHx_e5s_V4vXpZX63qEwjIFfDIJzobduNjXBtN3Tc8rFvoQXZjNhgpzkYBIsdsx3XXMp2kG7ko5WuyGD8nFwfercxfOzKMLUKu1i2JNWUjo6zoW1Kih1SJoaUIjq1jeUH8VsxUHuraqWKVbW3qkCoYrUwtwcGy_xPj1El47Eosz6iycoG_w_9A5ERgXA</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>Salminen, Miia</creator><creator>Pulliainen, Jouni</creator><creator>Metsämäki, Sari</creator><creator>Ikonen, Jaakko</creator><creator>Heinilä, Kirsikka</creator><creator>Luojus, Kari</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>201806</creationdate><title>Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover</title><author>Salminen, Miia ; Pulliainen, Jouni ; Metsämäki, Sari ; Ikonen, Jaakko ; Heinilä, Kirsikka ; Luojus, Kari</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c240t-994f47d56b24f56963d3b7505082be2e6a30ce0de61d63a388598fb3bd8f01613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Boreal forests</topic><topic>Empirical analysis</topic><topic>Error analysis</topic><topic>Estimation errors</topic><topic>Forest canopy</topic><topic>Forests</topic><topic>Fractional interests</topic><topic>Fractional snow cover</topic><topic>Mapping</topic><topic>Optical</topic><topic>Optical industry</topic><topic>Parameterization</topic><topic>Reflectance</topic><topic>Remote sensing</topic><topic>Satellite data</topic><topic>Satellites</topic><topic>Seasonal snow</topic><topic>Seasonal variations</topic><topic>Snow</topic><topic>Snow cover</topic><topic>Snow mapping</topic><topic>Statistical analysis</topic><topic>Statistics</topic><topic>Taiga</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Salminen, Miia</creatorcontrib><creatorcontrib>Pulliainen, Jouni</creatorcontrib><creatorcontrib>Metsämäki, Sari</creatorcontrib><creatorcontrib>Ikonen, Jaakko</creatorcontrib><creatorcontrib>Heinilä, Kirsikka</creatorcontrib><creatorcontrib>Luojus, Kari</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Salminen, Miia</au><au>Pulliainen, Jouni</au><au>Metsämäki, Sari</au><au>Ikonen, Jaakko</au><au>Heinilä, Kirsikka</au><au>Luojus, Kari</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover</atitle><jtitle>Remote sensing of environment</jtitle><date>2018-06</date><risdate>2018</risdate><volume>212</volume><spage>103</spage><epage>113</epage><pages>103-113</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>We developed a methodology to evaluate quantitative uncertainty characteristics of satellite data retrievals including the contribution of systematic error and statistical error. This is introduced by assessing the total product error of optical, satellite data-based, Fractional Snow Cover (FSC) estimates. Here the FSC estimation is based on an algorithm allowing the consideration of the effect of different error sources; a semi-empirical reflectance model describing the relationship of the observed reflectance and FSC through several variables and parameters. We assume that after the statistical error analysis, the remaining portion of total product error arises due to systematic factors. Hence, first we define a statistical error component through the theory of error propagation, and then estimate the total product error (PE) by using in situ observations on FSC, and finally derive the systematic error from these two error components. The experimental approach for estimating PE is conducted through an analysis of the observed estimation errors (i.e. residuals) in the GlobSnow Snow Extent (SE) v2.1 products on FSC. In practice, independent in situ snow course observations from Finland on FSC are compared to corresponding satellite FSC estimates to quantify the residuals. The approach is then illustrated for an extended region of corresponding European boreal forest. Our results show that the total PE in the GlobSnow FSC product is significantly higher than the originally provided statistical error. This is due to deficiencies in the parameterization of the applied forward modelling approach, in particular in the consideration of the forest canopy effects.
•Statistical error is not sufficient to describe full error of GlobSnow SE product.•GlobSnow FSC-product error is dominated by the systematic error in boreal forests.•A novel experimental analysis approach using a unique regional data set is proposed.•Using the approach all GlobSnow SE error components are defined for the first time.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2018.04.038</doi><tpages>11</tpages></addata></record> |
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| ispartof | Remote sensing of environment, 2018-06, Vol.212, p.103-113 |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Accuracy Boreal forests Empirical analysis Error analysis Estimation errors Forest canopy Forests Fractional interests Fractional snow cover Mapping Optical Optical industry Parameterization Reflectance Remote sensing Satellite data Satellites Seasonal snow Seasonal variations Snow Snow cover Snow mapping Statistical analysis Statistics Taiga Uncertainty |
| title | Determination of uncertainty characteristics for the satellite data-based estimation of fractional snow cover |
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