03912nam 22006015 450 991048423370332120200705041509.0981-15-3238-910.1007/978-981-15-3238-2(CKB)4100000010348454(MiAaPQ)EBC6118518(DE-He213)978-981-15-3238-2(PPN)243767714(EXLCZ)99410000001034845420200220d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDigital Mapping of Soil Landscape Parameters Geospatial Analyses using Machine Learning and Geomatics /by Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (159 pages)Studies in Big Data,2197-6503 ;72981-15-3237-0 Chapter 1. Concept of Digital Mapping -- Chapter 2. Different Approaches on Digital Mapping of Soil -- Chapter 3. Selection of Suitable Variables and Their Development -- Chapter 4. Digital Soil Mapping: Implementation and Assessment -- Chapter 5. Prediction Modelsfor Crop Mapping -- Chapter 6. Spatial Soil Moisture Prediction Model over an Agricultural Land.This book addresses the mapping of soil-landscape parameters in the geospatial domain. It begins by discussing the fundamental concepts, and then explains how machine learning and geomatics can be applied for more efficient mapping and to improve our understanding and management of ‘soil’. The judicious utilization of a piece of land is one of the biggest and most important current challenges, especially in light of the rapid global urbanization, which requires continuous monitoring of resource consumption. The book provides a clear overview of how machine learning can be used to analyze remote sensing data to monitor the key parameters, below, at, and above the surface. It not only offers insights into the approaches, but also allows readers to learn about the challenges and issues associated with the digital mapping of these parameters and to gain a better understanding of the selection of data to represent soil-landscape relationships as well as the complex and interconnected links between soil-landscape parameters under a range of soil and climatic conditions. Lastly, the book sheds light on using the network of satellite-based Earth observations to provide solutions toward smart farming and smart land management. .Studies in Big Data,2197-6503 ;72Computational intelligenceBig dataRemote sensingComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Big Datahttps://scigraph.springernature.com/ontologies/product-market-codes/I29120Remote Sensing/Photogrammetryhttps://scigraph.springernature.com/ontologies/product-market-codes/J13010Computational intelligence.Big data.Remote sensing.Computational Intelligence.Big Data.Remote Sensing/Photogrammetry.631.470223Garg Pradeep Kumarauthttp://id.loc.gov/vocabulary/relators/aut860953Garg Rahul Devauthttp://id.loc.gov/vocabulary/relators/autShukla Gauravauthttp://id.loc.gov/vocabulary/relators/autSrivastava Hari Shankerauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910484233703321Digital Mapping of Soil Landscape Parameters1921333UNINA