Remote Sensing in Agriculture: State-of-the-Art
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...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (220 p.) |
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| 245 | 1 | 0 | |a Remote Sensing in Agriculture: State-of-the-Art |
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| 520 | |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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 650 | 7 | |a Environmental science, engineering & technology |2 bicssc | |
| 653 | |a feature selection | ||
| 653 | |a spectral angle mapper | ||
| 653 | |a support vector machine | ||
| 653 | |a support vector regression | ||
| 653 | |a hyperspectral imaging | ||
| 653 | |a UAV | ||
| 653 | |a cross-scale | ||
| 653 | |a yellow rust | ||
| 653 | |a spatial resolution | ||
| 653 | |a winter wheat | ||
| 653 | |a MODIS | ||
| 653 | |a northern Mongolia | ||
| 653 | |a remote sensing indices | ||
| 653 | |a spring wheat | ||
| 653 | |a yield estimation | ||
| 653 | |a UAV-based LiDAR | ||
| 653 | |a biomass | ||
| 653 | |a crop height | ||
| 653 | |a field phenotyping | ||
| 653 | |a oasis crop type mapping | ||
| 653 | |a Sentinel-1 and 2 integration | ||
| 653 | |a statistically homogeneous pixels (SHPs) | ||
| 653 | |a red-edge spectral bands and indices | ||
| 653 | |a recursive feature increment (RFI) | ||
| 653 | |a random forest (RF) | ||
| 653 | |a unmanned aerial vehicles (UAVs) | ||
| 653 | |a remote sensing (RS) | ||
| 653 | |a thermal UAV RS | ||
| 653 | |a thermal infrared (TIR) | ||
| 653 | |a precision agriculture (PA) | ||
| 653 | |a crop water stress monitoring | ||
| 653 | |a plant disease detection | ||
| 653 | |a vegetation status monitoring | ||
| 653 | |a Landsat | ||
| 653 | |a data blending | ||
| 653 | |a crop yield prediction | ||
| 653 | |a gap-filling | ||
| 653 | |a volumetric soil moisture | ||
| 653 | |a synthetic aperture radar (SAR) | ||
| 653 | |a Sentinel-1 | ||
| 653 | |a soil moisture semi-empirical model | ||
| 653 | |a soil moisture Karnataka India | ||
| 653 | |a reflectance | ||
| 653 | |a digital number (DN) | ||
| 653 | |a vegetation index (VI) | ||
| 653 | |a Parrot Sequoia (Sequoia) | ||
| 653 | |a DJI Phantom 4 Multispectral (P4M) | ||
| 653 | |a Synthetic Aperture Radar | ||
| 653 | |a SAR | ||
| 653 | |a lodging | ||
| 653 | |a Hidden Markov Random Field | ||
| 653 | |a HMRF | ||
| 653 | |a CDL | ||
| 653 | |a corn | ||
| 653 | |a soybean | ||
| 653 | |a crop Monitoring | ||
| 653 | |a crop management | ||
| 653 | |a apple orchard damage | ||
| 653 | |a polarimetric decomposition | ||
| 653 | |a entropy | ||
| 653 | |a anisotropy | ||
| 653 | |a alpha angle | ||
| 653 | |a storm damage mapping | ||
| 653 | |a economic loss | ||
| 653 | |a insurance support | ||
| 776 | |z 3-0365-5483-1 | ||
| 700 | 1 | |a Tarantino, Eufemia |4 edt | |
| 700 | 1 | |a Capolupo, Alessandra |4 edt | |
| 700 | 1 | |a Borgogno-Mondino, Enrico |4 oth | |
| 700 | 1 | |a Tarantino, Eufemia |4 oth | |
| 700 | 1 | |a Capolupo, Alessandra |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:41:58 Europe/Vienna |f system |c marc21 |a 2023-01-09 04:44:33 Europe/Vienna |g false | ||
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