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Digital mapping of soil landscape pa...

Garg, Pradeep Kumar.

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Digital mapping of soil landscape parameters = geospatial analyses using machine learning and geomatics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Digital mapping of soil landscape parameters/ by Pradeep Kumar Garg ... [et al.].
Reminder of title:	geospatial analyses using machine learning and geomatics /
other author:	Garg, Pradeep Kumar.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xix, 142 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Digital soil mapping. -
Online resource:	https://doi.org/10.1007/978-981-15-3238-2
ISBN:	9789811532382

Digital mapping of soil landscape parameters = geospatial analyses using machine learning and geomatics /
Digital mapping of soil landscape parametersgeospatial analyses using machine learning and geomatics /[electronic resource] :by Pradeep Kumar Garg ... [et al.]. - Singapore :Springer Singapore :2020. - xix, 142 p. :ill., digital ;24 cm. - Studies in big data,v.722197-6503 ;. - Studies in big data ;v.72..

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.

ISBN: 9789811532382

Standard No.: 10.1007/978-981-15-3238-2doiSubjects--Topical Terms:

907742
Digital soil mapping.

LC Class. No.: S592.147

Dewey Class. No.: 631.49

Digital mapping of soil landscape parameters = geospatial analyses using machine learning and geomatics /
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245 0 0 $a Digital mapping of soil landscape parameters $h [electronic resource] : $b geospatial analyses using machine learning and geomatics / $c by Pradeep Kumar Garg ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xix, 142 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6503 ; $v v.72
505 0 $a 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.
520 $a 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.
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700 1 $a Garg, Pradeep Kumar. $3 3448571
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