{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/developing-a-machine-learning-framework-for","title":"Developing a machine learning framework for estimating soil moisture with VNIR hyperspectral data","arxiv_id":"1804.09046","date":"2018-04-24","proceeding":null,"authors":["Sina Keller","Felix M. Riese","Johanna Stötzer","Philipp M. Maier","Stefan Hinz"],"abstract":"In this paper, we investigate the potential of estimating the soil-moisture\ncontent based on VNIR hyperspectral data combined with LWIR data. Measurements\nfrom a multi-sensor field campaign represent the benchmark dataset which\ncontains measured hyperspectral, LWIR, and soil-moisture data conducted on\ngrassland site. We introduce a regression framework with three steps consisting\nof feature selection, preprocessing, and well-chosen regression models. The\nlatter are mainly supervised machine learning models. An exception are the\nself-organizing maps which combine unsupervised and supervised learning. We\nanalyze the impact of the distinct preprocessing methods on the regression\nresults. Of all regression models, the extremely randomized trees model without\npreprocessing provides the best estimation performance. Our results reveal the\npotential of the respective regression framework combined with the VNIR\nhyperspectral data to estimate soil moisture measured under real-world\nconditions. In conclusion, the results of this paper provide a basis for\nfurther improvements in different research directions.","url_abs":"http://arxiv.org/abs/1804.09046v4","url_pdf":"http://arxiv.org/pdf/1804.09046v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"developing-a-machine-learning-framework-for","repo_url":"https://github.com/felixriese/hyperspectral-processing","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}