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008			221027s2022 sz | s |||| 0|eng d
020			_a9783031149375
024	7		_a10.1007/978-3-031-14937-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aNiu, Haoyu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aTowards Tree-level Evapotranspiration Estimation with Small UAVs in Precision Agriculture _cby Haoyu Niu, YangQuan Chen
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIV, 156 páginas) _b60 ilustraciones, 56 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_aChapter 1: Introduction -- Chapter 2: ET Estimation Methods with UAVs: A Comprehensive Review -- Chapter 3: Existing ET Estimation Methods with UAVs: Results and Discussions -- Chapter 4: Estimating Actual Crop Evapotranspiration Using Deep Stochastic Configuration Networks Model and UAV-based Crop Coefficients in A Pomegranate Orchard -- Chapter 5: Reliable Tree-level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- Chapter 6: Tree-level Water Status Inference Using UAV Thermal Imagery and Machine Learning -- Chapter 7: Conclusion and Future Research.
520			_aEstimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. On the other hand, while remote sensing is able to provide spatially distributed measurements, the spatial resolution of multispectral satellite images is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. This book examines the different UAV-based approaches of ET estimation. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are discussed. It also covers the challenges and opportunities for UAVs in ET estimation, with the final chapters devoted to new ET estimation methods and their potential applications for future research.
700	1		_aChen, YangQuan _eautor _0(orcid)0000-0002-7422-5988 _1https://orcid.org/0000-0002-7422-5988 _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031149368
776	0	8	_iPrinted edition: _z9783031149382
776	0	8	_iPrinted edition: _z9783031149399
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-14937-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_BiomedLife_2022
999			_c394280 _d394280