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230202 ||| eng |
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|a 9783036554839
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|a books978-3-0365-5484-6
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|a 9783036554846
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| 100 |
1 |
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|a Borgogno-Mondino, Enrico
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| 245 |
0 |
0 |
|a Remote Sensing in Agriculture: State-of-the-Art
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (220 p.)
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| 653 |
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|a Sentinel-1 and 2 integration
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| 653 |
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|a alpha angle
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| 653 |
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|a red-edge spectral bands and indices
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| 653 |
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|a unmanned aerial vehicles (UAVs)
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| 653 |
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|a Landsat
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| 653 |
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|a Synthetic Aperture Radar
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| 653 |
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|a HMRF
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| 653 |
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|a Parrot Sequoia (Sequoia)
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| 653 |
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|a plant disease detection
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| 653 |
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|a cross-scale
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| 653 |
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|a reflectance
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| 653 |
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|a spectral angle mapper
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| 653 |
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|a feature selection
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| 653 |
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|a crop Monitoring
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| 653 |
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|a insurance support
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| 653 |
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|a History of engineering and technology / bicssc
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| 653 |
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|a yield estimation
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| 653 |
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|a statistically homogeneous pixels (SHPs)
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| 653 |
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|a soil moisture Karnataka India
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| 653 |
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|a oasis crop type mapping
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| 653 |
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|a vegetation index (VI)
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| 653 |
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|a CDL
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a yellow rust
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| 653 |
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|a thermal UAV RS
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| 653 |
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|a UAV-based LiDAR
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| 653 |
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|a random forest (RF)
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| 653 |
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|a DJI Phantom 4 Multispectral (P4M)
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| 653 |
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|a corn
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| 653 |
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|a hyperspectral imaging
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| 653 |
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|a biomass
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| 653 |
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|a digital number (DN)
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| 653 |
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|a crop management
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| 653 |
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|a Environmental science, engineering and technology / bicssc
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| 653 |
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|a lodging
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| 653 |
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|a support vector regression
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| 653 |
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|a Hidden Markov Random Field
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| 653 |
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|a thermal infrared (TIR)
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| 653 |
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|a UAV
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| 653 |
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|a polarimetric decomposition
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| 653 |
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|a remote sensing (RS)
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| 653 |
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|a support vector machine
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| 653 |
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|a gap-filling
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| 653 |
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|a recursive feature increment (RFI)
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| 653 |
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|a northern Mongolia
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| 653 |
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|a synthetic aperture radar (SAR)
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| 653 |
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|a winter wheat
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| 653 |
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|a precision agriculture (PA)
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| 653 |
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|a soil moisture semi-empirical model
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| 653 |
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|a spring wheat
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| 653 |
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|a apple orchard damage
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| 653 |
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|a entropy
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| 653 |
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|a soybean
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| 653 |
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|a Sentinel-1
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| 653 |
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|a storm damage mapping
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| 653 |
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|a remote sensing indices
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| 653 |
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|a vegetation status monitoring
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| 653 |
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|a data blending
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| 653 |
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|a economic loss
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| 653 |
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|a crop height
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| 653 |
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|a crop water stress monitoring
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| 653 |
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|a volumetric soil moisture
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| 653 |
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|a SAR
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| 653 |
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|a crop yield prediction
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| 653 |
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|a spatial resolution
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| 653 |
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|a anisotropy
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| 653 |
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|a field phenotyping
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| 653 |
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|a MODIS
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| 700 |
1 |
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|a Tarantino, Eufemia
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| 700 |
1 |
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|a Capolupo, Alessandra
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| 700 |
1 |
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|a Borgogno-Mondino, Enrico
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
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|a 10.3390/books978-3-0365-5484-6
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/94550
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/6385
|7 0
|x Verlag
|3 Volltext
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|a 900
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|a 363
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|a 630
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|a 620
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|a 330
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|a The Special Issue on "Remote Sensing in Agriculture: State-of-the-Art" gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue.
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