{"title":"深度学习与多任务卷积神经网络生成国家尺度三维土壤数据产品:德国农业土壤景观的粒度分布","authors":"Mareike Ließ, Ali Sakhaee","doi":"10.5194/egusphere-2023-2386","DOIUrl":null,"url":null,"abstract":"<strong>Abstract.</strong> Many soil functions and processes are controlled by the soil particle size distribution. The generated three-dimensional continuous data product, which covers the particle size fractions of sand, silt, and clay in the agricultural soil-landscape of Germany, has a spatial resolution of 100 m and a depth resolution of 1 cm. This product is an important component for predicting the effects of agricultural management practices and their adaptability to climate change, as well as for analyzing soil functions and numerous risks. The effectiveness of the convolutional neural network (CNN) algorithm in producing multidimensional, multivariate data products is demonstrated. Even though the potential of this deep learning approach to understand and model the complex soil-landscape relationship is virtually limitless, limitations are data-driven. Further research is needed to assess the required complexity and depth of the CNN and the inclusion of the landscape surrounding each soil profile.","PeriodicalId":48610,"journal":{"name":"Soil","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning with a multi-task convolutional neural network to generate a national-scale 3D soil data product: Particle size distribution of the German agricultural soil-landscape\",\"authors\":\"Mareike Ließ, Ali Sakhaee\",\"doi\":\"10.5194/egusphere-2023-2386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<strong>Abstract.</strong> Many soil functions and processes are controlled by the soil particle size distribution. The generated three-dimensional continuous data product, which covers the particle size fractions of sand, silt, and clay in the agricultural soil-landscape of Germany, has a spatial resolution of 100 m and a depth resolution of 1 cm. This product is an important component for predicting the effects of agricultural management practices and their adaptability to climate change, as well as for analyzing soil functions and numerous risks. The effectiveness of the convolutional neural network (CNN) algorithm in producing multidimensional, multivariate data products is demonstrated. Even though the potential of this deep learning approach to understand and model the complex soil-landscape relationship is virtually limitless, limitations are data-driven. Further research is needed to assess the required complexity and depth of the CNN and the inclusion of the landscape surrounding each soil profile.\",\"PeriodicalId\":48610,\"journal\":{\"name\":\"Soil\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2023-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Soil\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.5194/egusphere-2023-2386\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOIL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.5194/egusphere-2023-2386","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
Deep learning with a multi-task convolutional neural network to generate a national-scale 3D soil data product: Particle size distribution of the German agricultural soil-landscape
Abstract. Many soil functions and processes are controlled by the soil particle size distribution. The generated three-dimensional continuous data product, which covers the particle size fractions of sand, silt, and clay in the agricultural soil-landscape of Germany, has a spatial resolution of 100 m and a depth resolution of 1 cm. This product is an important component for predicting the effects of agricultural management practices and their adaptability to climate change, as well as for analyzing soil functions and numerous risks. The effectiveness of the convolutional neural network (CNN) algorithm in producing multidimensional, multivariate data products is demonstrated. Even though the potential of this deep learning approach to understand and model the complex soil-landscape relationship is virtually limitless, limitations are data-driven. Further research is needed to assess the required complexity and depth of the CNN and the inclusion of the landscape surrounding each soil profile.
SoilAgricultural and Biological Sciences-Soil Science
CiteScore
10.80
自引率
2.90%
发文量
44
审稿时长
30 weeks
期刊介绍:
SOIL is an international scientific journal dedicated to the publication and discussion of high-quality research in the field of soil system sciences.
SOIL is at the interface between the atmosphere, lithosphere, hydrosphere, and biosphere. SOIL publishes scientific research that contributes to understanding the soil system and its interaction with humans and the entire Earth system. The scope of the journal includes all topics that fall within the study of soil science as a discipline, with an emphasis on studies that integrate soil science with other sciences (hydrology, agronomy, socio-economics, health sciences, atmospheric sciences, etc.).