{"id":515,"date":"2015-05-04T16:54:43","date_gmt":"2015-05-04T16:54:43","guid":{"rendered":"http:\/\/www.agron.iastate.edu\/glsi\/?p=515"},"modified":"2025-09-27T08:24:12","modified_gmt":"2025-09-27T13:24:12","slug":"comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks","status":"publish","type":"post","link":"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\/","title":{"rendered":"Comparison of Spatial Association Approaches for Landscape Mapping of Soil Organic Carbon Stocks"},"content":{"rendered":"
\n

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m\u22122<\/sup>), covering an area of 122 km2<\/sup>, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R<\/i>2<\/sup> values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks’ spatial distribution.<\/p>\n<\/div><\/div>\n\n

\n

Miller, B.A., S. Koszinski, M. Wehrhan, and M. Sommer. 2015. Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks. SOIL 1(1):217-233. doi:10.5194\/soil-1-217-2015.<\/a><\/p>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"

A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m\u203e\u00b2), covering an area of 122 km\u00b2, with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.<\/p>\n","protected":false},"author":3216,"featured_media":4268,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"ngg_post_thumbnail":0,"footnotes":""},"categories":[5,7],"tags":[19,34,42,48,76,116,128,129],"class_list":["post-515","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-manuscripts","category-miller","tag-carbon","tag-digital-soil-mapping","tag-error-propagation","tag-germany","tag-modelling","tag-soc","tag-spatial-analysis","tag-spatial-association"],"acf":[],"featured_image_urls_v2":{"full":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false],"thumbnail":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",150,132,false],"medium":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false],"medium_large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false],"large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false],"1536x1536":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false],"2048x2048":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare.jpg",170,150,false]},"post_excerpt_stackable_v2":"

A comparison of direct and indirect approaches for mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m\u203e\u00b2), covering an area of 122 km\u00b2, with accompanying maps of estimated error. Although the indirect approach fit the spatial variation better and had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. The optimal approach would depend upon the intended use of the map.<\/p>\n","category_list_v2":"Manuscripts<\/a>, Miller<\/a>","author_info_v2":{"name":"Bradley Miller","url":"https:\/\/www.agron.iastate.edu\/glsi\/author\/millerba\/"},"comments_num_v2":"0 comments","yoast_head":"\nComparison of Spatial Association Approaches for Landscape Mapping of Soil Organic Carbon Stocks - Geospatial Laboratory for Soil Informatics<\/title>\n<meta name=\"description\" content=\"The direct approach to spatially modelling soil organic carbon (SOC) stocks produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparison of Spatial Association Approaches for Landscape Mapping of Soil Organic Carbon Stocks - Geospatial Laboratory for Soil Informatics\" \/>\n<meta property=\"og:description\" content=\"The direct approach to spatially modelling soil organic carbon (SOC) stocks produced a map with less spatial variation than the map produced by the indirect approach. 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For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\/\" \/>\n<meta property=\"og:site_name\" content=\"Geospatial Laboratory for Soil Informatics\" \/>\n<meta property=\"article:published_time\" content=\"2015-05-04T16:54:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-27T13:24:12+00:00\" \/>\n<meta name=\"author\" content=\"Bradley Miller\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:image\" content=\"http:\/\/www.agron.iastate.edu\/glsi\/files\/2015\/05\/SOC-map-compare-thumb.jpg\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Bradley Miller\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/\"},\"author\":{\"name\":\"Bradley Miller\",\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#\\\/schema\\\/person\\\/a96fa0c818314fce5f3928c232490277\"},\"headline\":\"Comparison of Spatial Association Approaches for Landscape Mapping of Soil Organic Carbon Stocks\",\"datePublished\":\"2015-05-04T16:54:43+00:00\",\"dateModified\":\"2025-09-27T13:24:12+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/\"},\"wordCount\":323,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/files\\\/2015\\\/05\\\/SOC-map-compare.jpg\",\"keywords\":[\"carbon\",\"Digital Soil Mapping\",\"error propagation\",\"Germany\",\"modelling\",\"SOC\",\"spatial analysis\",\"spatial association\"],\"articleSection\":[\"Manuscripts\",\"Miller\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/\",\"url\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/\",\"name\":\"Comparison of Spatial Association Approaches for Landscape Mapping of Soil Organic Carbon Stocks - Geospatial Laboratory for Soil Informatics\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/comparison-of-spatial-association-approaches-for-landscape-mapping-of-soil-organic-carbon-stocks\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/files\\\/2015\\\/05\\\/SOC-map-compare.jpg\",\"datePublished\":\"2015-05-04T16:54:43+00:00\",\"dateModified\":\"2025-09-27T13:24:12+00:00\",\"description\":\"The direct approach to spatially modelling soil organic carbon (SOC) stocks produced a map with less spatial variation than the map produced by the indirect approach. 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