{"id":13723,"date":"2025-03-26T07:20:00","date_gmt":"2025-03-26T12:20:00","guid":{"rendered":"https:\/\/www.agron.iastate.edu\/glsi\/?p=13723"},"modified":"2025-09-26T07:39:56","modified_gmt":"2025-09-26T12:39:56","slug":"an-optimal-sample-size-index-for-updating-spatial-soil-models","status":"publish","type":"post","link":"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/an-optimal-sample-size-index-for-updating-spatial-soil-models\/","title":{"rendered":"An Optimal Sample Size Index for Updating Spatial Soil Models"},"content":{"rendered":"<div class=\"paragraph-widget paragraph-widget--text-html\"><div class=\"text-content\">\n<p>Soil map updates can be expensive due to soil sampling and analysis costs. This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. OSSI determines optimal sample size from subsets of initially available training sets, accounting for different physiographies and model validation metrics with adjustable weights, including root-mean-square-error (RMSE) for cross-validation (CV) and RMSE for independent validation, the standard deviation of RMSE values from 10-fold CV, and relative sampling cost. Relative sampling cost represents the proportion of the number of samples used in modeling to the initially available sample size. We applied two OSSI scenarios to address limitations of the original approach, which prioritized cost reduction but occasionally resulted in unreliable models due to very small training sizes. By adjusting metrics and weights, the second scenario accounted for model uncertainty, producing more reliable models with sample sizes considerably lower than full training sets. Four soil properties (pH, clay, silt, and sand %) were spatially modeled for surface soils in three study areas in Iowa, USA, using random forest regressors. Both scenarios reduced relative sampling costs by up to 92 % compared to using all samples while maintaining similar or improved model performance. The second scenario further ensured model reliability, as shown by lower standard deviations of CV-RMSE values. Our results demonstrate OSSI\u2019s flexibility to balance cost, accuracy, and reliability, offering a practical solution for optimizing soil sample sizes and updating soil survey maps.<\/p>\n<\/div><\/div>\n\n<div class=\"paragraph-widget paragraph-widget--text-html\"><div class=\"text-content\">\n<p>Ferhatoglu, C., M.D. McDaniel, B.A. Miller. 2025. An optimal sample size index for updating spatial soil models. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0016706125000461#f0005\">Geoderma 455:117208. doi: 10.1016\/j.geoderma.2025.117208<\/a>.<\/p>\n<\/div><\/div>\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>Soil map updates can be expensive due to soil sampling and analysis costs. This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. OSSI determines optimal sample size from subsets of initially available training sets, accounting for [&hellip;]<\/p>\n","protected":false},"author":3216,"featured_media":13724,"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":[330,5,7],"tags":[34],"class_list":["post-13723","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ferhatoglu","category-manuscripts","category-miller","tag-digital-soil-mapping"],"acf":[],"featured_image_urls_v2":{"full":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false],"thumbnail":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps-150x150.jpg",150,150,true],"medium":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false],"medium_large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false],"large":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false],"1536x1536":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false],"2048x2048":["https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg",204,193,false]},"post_excerpt_stackable_v2":"<p>Soil map updates can be expensive due to soil sampling and analysis costs. This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. OSSI determines optimal sample size from subsets of initially available training sets, accounting for different physiographies and model validation metrics with adjustable weights, including root-mean-square-error (RMSE) for cross-validation (CV) and RMSE for independent validation, the standard deviation of RMSE values from 10-fold CV, and relative sampling cost. Relative sampling cost represents the proportion of the number of samples used&hellip;<\/p>\n","category_list_v2":"<a href=\"https:\/\/www.agron.iastate.edu\/glsi\/category\/ferhatoglu\/\" rel=\"category tag\">Ferhatoglu<\/a>, <a href=\"https:\/\/www.agron.iastate.edu\/glsi\/category\/manuscripts\/\" rel=\"category tag\">Manuscripts<\/a>, <a href=\"https:\/\/www.agron.iastate.edu\/glsi\/category\/miller\/\" rel=\"category tag\">Miller<\/a>","author_info_v2":{"name":"Bradley Miller","url":"https:\/\/www.agron.iastate.edu\/glsi\/author\/millerba\/"},"comments_num_v2":"0 comments","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An Optimal Sample Size Index for Updating Spatial Soil Models - Geospatial Laboratory for Soil Informatics<\/title>\n<meta name=\"description\" content=\"This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. 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OSSI determines optimal sample size from subsets of initially available training sets, accounting for different physiographies and model validation metrics with adjustable weights, including root-mean-square-error (RMSE) for cross-validation (CV) and RMSE for independent validation, the standard deviation of RMSE values from 10-fold CV, and relative sampling cost.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.agron.iastate.edu\/glsi\/manuscripts\/an-optimal-sample-size-index-for-updating-spatial-soil-models\/\" \/>\n<meta property=\"og:site_name\" content=\"Geospatial Laboratory for Soil Informatics\" \/>\n<meta property=\"article:published_time\" content=\"2025-03-26T12:20:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-09-26T12:39:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.agron.iastate.edu\/glsi\/files\/2025\/09\/Optimal-sample-size-for-updating-soil-maps.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"204\" \/>\n\t<meta property=\"og:image:height\" content=\"193\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Bradley Miller\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\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\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/\"},\"author\":{\"name\":\"Bradley Miller\",\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#\\\/schema\\\/person\\\/a96fa0c818314fce5f3928c232490277\"},\"headline\":\"An Optimal Sample Size Index for Updating Spatial Soil Models\",\"datePublished\":\"2025-03-26T12:20:00+00:00\",\"dateModified\":\"2025-09-26T12:39:56+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/\"},\"wordCount\":281,\"publisher\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/files\\\/2025\\\/09\\\/Optimal-sample-size-for-updating-soil-maps.jpg\",\"keywords\":[\"Digital Soil Mapping\"],\"articleSection\":[\"Ferhatoglu\",\"Manuscripts\",\"Miller\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/\",\"url\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/\",\"name\":\"An Optimal Sample Size Index for Updating Spatial Soil Models - Geospatial Laboratory for Soil Informatics\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/manuscripts\\\/an-optimal-sample-size-index-for-updating-spatial-soil-models\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.agron.iastate.edu\\\/glsi\\\/files\\\/2025\\\/09\\\/Optimal-sample-size-for-updating-soil-maps.jpg\",\"datePublished\":\"2025-03-26T12:20:00+00:00\",\"dateModified\":\"2025-09-26T12:39:56+00:00\",\"description\":\"This study introduces the Optimal Sample Size Index (OSSI), a flexible framework for digital soil mapping that balances model accuracy and sampling costs to update soil spatial models. 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