Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping
This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse d...
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| Veröffentlicht in: | Environmental monitoring and assessment 2019-11, Vol.191 (11), p.684-12, Article 684 |
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| creator | Fazeli Sangani, Mahmood Namdar Khojasteh, Davood Owens, Gary |
| description | This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land. |
| doi_str_mv | 10.1007/s10661-019-7844-y |
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Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-019-7844-y</identifier><identifier>PMID: 31659465</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Agricultural land ; Agricultural practices ; Agriculture ; Atmospheric Protection/Air Quality Control/Air Pollution ; Datasets ; Dependence ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Environment ; Environmental Management ; Environmental Monitoring ; Environmental science ; Errors ; Fields ; Interpolation ; Interpolation methods ; Kriging interpolation ; Land use ; Mapping ; Monitoring/Environmental Analysis ; Performance assessment ; Performance testing ; Polynomials ; Radial basis function ; Salinity ; Salinity effects ; Soil ; Soil - chemistry ; Soil mapping ; Soil salinity ; Soils ; Spatial Analysis ; Statistical methods</subject><ispartof>Environmental monitoring and assessment, 2019-11, Vol.191 (11), p.684-12, Article 684</ispartof><rights>Springer Nature Switzerland AG 2019</rights><rights>Environmental Monitoring and Assessment is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-ec582c0df483749707d8b808b36c5f895a0cc16fd023cddb270e0e6face3fe733</citedby><cites>FETCH-LOGICAL-c372t-ec582c0df483749707d8b808b36c5f895a0cc16fd023cddb270e0e6face3fe733</cites><orcidid>0000-0001-7259-6593</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-019-7844-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-019-7844-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31659465$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fazeli Sangani, Mahmood</creatorcontrib><creatorcontrib>Namdar Khojasteh, Davood</creatorcontrib><creatorcontrib>Owens, Gary</creatorcontrib><title>Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.</description><subject>Agricultural land</subject><subject>Agricultural practices</subject><subject>Agriculture</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Datasets</subject><subject>Dependence</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental Monitoring</subject><subject>Environmental science</subject><subject>Errors</subject><subject>Fields</subject><subject>Interpolation</subject><subject>Interpolation methods</subject><subject>Kriging interpolation</subject><subject>Land use</subject><subject>Mapping</subject><subject>Monitoring/Environmental Analysis</subject><subject>Performance assessment</subject><subject>Performance testing</subject><subject>Polynomials</subject><subject>Radial basis function</subject><subject>Salinity</subject><subject>Salinity effects</subject><subject>Soil</subject><subject>Soil - chemistry</subject><subject>Soil mapping</subject><subject>Soil salinity</subject><subject>Soils</subject><subject>Spatial Analysis</subject><subject>Statistical methods</subject><issn>0167-6369</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kcGKFDEQhoMo7uzqA3iRgBcvrZWkO-kcZVdXYcGLnkMmXdnJ0p20SVqYtzfDrAqCp1Dk-_8q-Ah5xeAdA1DvCwMpWQdMd2rs--74hOzYoETH9aCfkh0wqToppL4gl6U8AIBWvX5OLgSTg-7lsCM_b2y1BSt1B5utq5hDqcEVGqKfN4wOaT0gXTH7lBd7mpOnU_AeM8basBZZ02xrSJEuWA9pKrSxtKQw02LnEEM90rI2ws50sesa4v0L8szbueDLx_eKfP_08dv15-7u6-2X6w93nROK1w7dMHIHk-9H0S5XoKZxP8K4F9INftSDBeeY9BNw4aZpzxUgoPTWofCohLgib8-9a04_NizVLKE4nGcbMW3FcMGAa9lz1dA3_6APacuxXdco0JK1uhPFzpTLqZSM3qw5LDYfDQNzkmLOUkyTYk5SzLFlXj82b_sFpz-J3xYawM9AaV_xHvPf1f9v_QWo2JrS</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Fazeli Sangani, Mahmood</creator><creator>Namdar Khojasteh, Davood</creator><creator>Owens, Gary</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7QL</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TG</scope><scope>7TN</scope><scope>7U7</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>KL.</scope><scope>L.-</scope><scope>L.G</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7259-6593</orcidid></search><sort><creationdate>20191101</creationdate><title>Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping</title><author>Fazeli Sangani, Mahmood ; Namdar Khojasteh, Davood ; Owens, Gary</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-ec582c0df483749707d8b808b36c5f895a0cc16fd023cddb270e0e6face3fe733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Agricultural land</topic><topic>Agricultural practices</topic><topic>Agriculture</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Datasets</topic><topic>Dependence</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental Monitoring</topic><topic>Environmental science</topic><topic>Errors</topic><topic>Fields</topic><topic>Interpolation</topic><topic>Interpolation methods</topic><topic>Kriging interpolation</topic><topic>Land use</topic><topic>Mapping</topic><topic>Monitoring/Environmental Analysis</topic><topic>Performance assessment</topic><topic>Performance testing</topic><topic>Polynomials</topic><topic>Radial basis function</topic><topic>Salinity</topic><topic>Salinity effects</topic><topic>Soil</topic><topic>Soil - 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Academic</collection><jtitle>Environmental monitoring and assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fazeli Sangani, Mahmood</au><au>Namdar Khojasteh, Davood</au><au>Owens, Gary</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2019-11-01</date><risdate>2019</risdate><volume>191</volume><issue>11</issue><spage>684</spage><epage>12</epage><pages>684-12</pages><artnum>684</artnum><issn>0167-6369</issn><eissn>1573-2959</eissn><abstract>This study compared the performance of different interpolation methods for mapping soil salinity of three different agricultural fields having the same land use but different dataset characteristics. Four common spatial interpolation methods including global polynomial interpolation (GPI), inverse distance weighted (IDW), ordinary kriging (OK), and radial basis functions (RBF) were employed for mapping soil EC. The performance of interpolation methods in predicting soil EC was evaluated based on mean bias error, root mean square error, mean absolute percentage error, and coefficient of determinations criteria. Results showed that dataset characteristics, including central tendency and distribution, were significantly different among the studied fields. Experimental semivariogram and fitted model parameters indicated that three studied fields were also different in their spatial dependence strength. Considering all of the performance assessment measures used, the best interpolation method for fields A and C was OK and IDW for field B. The performance of interpolation methods was found to be affected by data characteristics of the studied fields, which were mostly ascribed to management practices. This study suggests in order to obtain accurate mapping of soil salinity in agricultural fields, it is essential to first find the best spatial interpolation method compatible with the characteristics of the collected data from the selected agricultural land.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31659465</pmid><doi>10.1007/s10661-019-7844-y</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-7259-6593</orcidid></addata></record> |
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| subjects | Agricultural land Agricultural practices Agriculture Atmospheric Protection/Air Quality Control/Air Pollution Datasets Dependence Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring Environmental science Errors Fields Interpolation Interpolation methods Kriging interpolation Land use Mapping Monitoring/Environmental Analysis Performance assessment Performance testing Polynomials Radial basis function Salinity Salinity effects Soil Soil - chemistry Soil mapping Soil salinity Soils Spatial Analysis Statistical methods |
| title | Dataset characteristics influence the performance of different interpolation methods for soil salinity spatial mapping |
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