000			03646nam a2200457 i 4500
710	2		_aSpringerLink (Online service) _9106996
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008			200220s2020 si a s |||| 0|eng d
020			_a9789811532382
024	7		_a10.1007/978-981-15-3238-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aS592.147 _b2020 EB
100	1		_aGarg, Pradeep Kumar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673838
245	0	0	_aDigital Mapping of Soil Landscape Parameters : _bGeospatial Analyses using Machine Learning and Geomatics _cby Pradeep Kumar Garg, Rahul Dev Garg, Gaurav Shukla, Hari Shanker Srivastava.
250			_aFirst edition 2020.
264		1	_aSingapore _bSpringer Singapore _c2020
300			_a1 recurso en línea (XIX, 142 páginas) _b39 ilustraciones, 31 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aArchivo de texto _bPDF
490	0		_aStudies in Big Data _x2197-6503 _v72
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aChapter 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	3		_aThis 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. .
988			_aSpringer_Robotics_31032020
650		7	_2embne _aEdafología _9138176
700	1		_aGarg, Rahul Dev _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673839
700	1		_aShukla, Gaurav _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673840
700	1		_aSrivastava, Hari Shanker _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9673841
776	0	8	_iPrinted edition: _z9789811532375
776	0	8	_iPrinted edition: _z9789811532399
776	0	8	_iPrinted edition: _z9789811532405
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-3238-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2020 _dz _ek _zSI