{"id":15046,"date":"2026-02-13T14:29:11","date_gmt":"2026-02-13T20:29:11","guid":{"rendered":"https:\/\/www.agron.iastate.edu\/glsi\/?p=15046"},"modified":"2026-02-13T14:31:19","modified_gmt":"2026-02-13T20:31:19","slug":"digital-soil-mapping-accuracy","status":"publish","type":"post","link":"https:\/\/www.agron.iastate.edu\/glsi\/bohn\/digital-soil-mapping-accuracy\/","title":{"rendered":"Bohn PhD Defense 2022"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Bohn PhD Defense 2022 - Precision land surface analysis and machine learning for enhanced soil maps\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/zEhveLsLA7Y?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<details class=\"wp-block-stackable-accordion stk-block-accordion stk-inner-blocks stk-block-content stk-block stk-6717a15 is-style-default\" data-block-id=\"6717a15\">\n<summary class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-b0a660c stk--container-small stk-block-accordion__heading\" data-v=\"4\" data-block-id=\"b0a660c\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-b0a660c-container stk-hover-parent\"><div class=\"stk-block-content stk-inner-blocks stk-b0a660c-inner-blocks\">\n<div class=\"wp-block-stackable-icon-label stk-block-icon-label stk-block stk-f890275\" data-block-id=\"f890275\"><div class=\"stk-row stk-inner-blocks stk-block-content\">\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-f29ba9a\" id=\"abstract\" data-block-id=\"f29ba9a\"><h4 class=\"stk-block-heading__text\">Abstract<\/h4><\/div>\n\n\n\n<div class=\"wp-block-stackable-icon stk-block-icon stk-block stk-daab86e\" data-block-id=\"daab86e\"><span class=\"stk--svg-wrapper\"><div class=\"stk--inner-svg\"><svg style=\"height:0;width:0\"><defs><linearGradient id=\"linear-gradient-daab86e\" x1=\"0\" x2=\"100%\" y1=\"0\" y2=\"0\"><stop offset=\"0%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-daab-86-e-color-1)\"><\/stop><stop offset=\"100%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-daab-86-e-color-2)\"><\/stop><\/linearGradient><\/defs><\/svg><svg data-prefix=\"fas\" data-icon=\"chevron-down\" class=\"svg-inline--fa fa-chevron-down fa-w-14\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\" aria-hidden=\"true\" width=\"32\" height=\"32\"><path fill=\"currentColor\" d=\"M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z\"><\/path><\/svg><\/div><\/span><\/div>\n<\/div><\/div>\n<\/div><\/div><\/summary>\n\n\n\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-ed59041 stk-block-accordion__content\" data-v=\"4\" data-block-id=\"ed59041\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-ed59041-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-ed59041-inner-blocks\">\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-fe16eae\" data-block-id=\"fe16eae\"><p class=\"stk-block-text__text\">Digital soil mapping (DSM) is widely promoted as a next-generation approach for producing soil maps with greater accuracy and spatial precision than traditional soil survey products. Advances in fine-resolution digital terrain analysis, remote sensing, geospatial workflows, and machine learning (ML) have greatly improved spatial prediction capabilities over the past two decades. However, the degree to which DSM provides clear improvements over conventional soil maps is not always evident, making evaluation essential. As global demands increase for agronomic efficiency while reducing nutrient and sediment losses, greenhouse gas emissions, erosion, and enhancing carbon sequestration, there is growing need for updated, subfield-scale soil property information to support agroecosystem research and management. This dissertation evaluated the effectiveness of DSM using ML through multiple approaches: regional-scale comparisons of map accuracy and resolution (Chapter 2), assessment of validation methods for unbiased accuracy estimation (Chapter 3), evaluation of the predictability of diverse soil properties and depths at the plot scale (Chapter 4), and testing the impact of DSM-derived soil inputs on agroecosystem model simulations (Chapter 5). In Chapter 2, DSM products were compared to SSURGO in a high-quality soil survey region. Target variables included key morphologic indicators of topsoil thickness and hydrologic regime\u2014depth of mollic colors (mollic depth) and depth to reduced redoximorphic depletions (RRD depth)\u2014as well as sand, silt, clay, and soil organic matter (OM) across six depth intervals to 2 m. DSM generally produced more accurate 10-m maps than SSURGO, improving mollic depth and RRD depth predictions by ~20 cm. DSM also outperformed SSURGO for most texture and OM predictions, with a few depth-specific exceptions. Chapter 3 evaluated how different validation strategies influence estimated map accuracy. Hypotheses tested whether (1) cross-validation underestimates error relative to independent validation, (2) independent validation underestimates error relative to geographically disjoint validation, and (3) spatial autocorrelation between training and validation points reduces estimated error. Only hypothesis (2) was supported. Chapter 4 assessed DSM-ML prediction performance for 18 agronomically relevant soil properties across seven depth intervals to 2 m at the plot scale. SOM fraction properties were easiest to predict, followed by carbonate properties and soil-water solution properties, while texture was most difficult. The 15\u201330 cm interval was most predictable, and accuracy generally declined with depth. Chapter 5 integrated the DSM products into agroecosystem process modeling to evaluate sensitivity to soil input data. Simulations using DSM-derived inputs produced substantially lower average maximum maize biomass and yield than simulations driven by SSURGO, demonstrating that soil input choice can strongly influence model outcomes.<\/p><\/div>\n\n\n<div class=\"paragraph-widget paragraph-widget--text-html\"><div class=\"text-content\">\n<p><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n<\/details>\n\n\n<div class=\"paragraph-widget paragraph-widget--text-html\"><div class=\"text-content\">\n<p><\/p>\n<\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Abstract Digital soil mapping (DSM) is widely promoted as a next-generation approach for producing soil maps with greater accuracy and spatial precision than traditional soil survey products. Advances in fine-resolution digital terrain analysis, remote sensing, geospatial workflows, and machine learning (ML) have greatly improved spatial prediction capabilities over the past two decades. However, the degree [&hellip;]<\/p>\n","protected":false},"author":3348,"featured_media":15047,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"ngg_post_thumbnail":0,"footnotes":""},"categories":[11,355],"tags":[27,34,35,47,170,60,356,76,87,91,116,121,201,124,128,129],"class_list":["post-15046","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bohn","category-presentations","tag-crop-yield","tag-digital-soil-mapping","tag-digital-terrain-analysis","tag-geomorphology","tag-gssurgo","tag-iowa","tag-map-validation","tag-modelling","tag-particle-size-analysis","tag-philosophy","tag-soc","tag-soil-maps","tag-soil-properties","tag-soil-survey","tag-spatial-analysis","tag-spatial-association"],"acf":[],"featured_image_urls_v2":{"full":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825.jpeg",1536,2048,false],"thumbnail":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825-150x150.jpeg",150,150,true],"medium":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825-225x300.jpeg",225,300,true],"medium_large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825-768x1024.jpeg",768,1024,true],"large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825-768x1024.jpeg",768,1024,true],"1536x1536":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825-1152x1536.jpeg",1152,1536,true],"2048x2048":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2026\/02\/IMG_0825.jpeg",1536,2048,false]},"post_excerpt_stackable_v2":"<p>Abstract Digital soil mapping (DSM) is widely promoted as a next-generation approach for producing soil maps with greater accuracy and spatial precision than traditional soil survey products. Advances in fine-resolution digital terrain analysis, remote sensing, geospatial workflows, and machine learning (ML) have greatly improved spatial prediction capabilities over the past two decades. However, the degree to which DSM provides clear improvements over conventional soil maps is not always evident, making evaluation essential. As global demands increase for agronomic efficiency while reducing nutrient and sediment losses, greenhouse gas emissions, erosion, and enhancing carbon sequestration, there is growing need for updated, subfield-scale&hellip;<\/p>\n","category_list_v2":"<a href=\"https:\/\/www.agron.iastate.edu\/glsi\/category\/bohn\/\" rel=\"category tag\">Bohn<\/a>, <a href=\"https:\/\/www.agron.iastate.edu\/glsi\/category\/presentations\/\" rel=\"category tag\">Presentations<\/a>","author_info_v2":{"name":"Meyer Bohn","url":"https:\/\/www.agron.iastate.edu\/glsi\/author\/mpbohn\/"},"comments_num_v2":"0 comments","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Bohn PhD Defense 2022 - Geospatial Laboratory for Soil Informatics<\/title>\n<meta name=\"description\" content=\"This dissertation evaluates digital soil mapping accuracy vs conventional soil maps (SSURGO), including validation methods, depth predictions, and soil map impacts on maize modeling.\" \/>\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\/bohn\/digital-soil-mapping-accuracy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bohn PhD Defense 2022 - 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