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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| Formato: | Online |
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| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Acesso em linha: | ONIX_20221206_9783036554839_73 |
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| _version_ | 1869530527970623488 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-94550 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-945502024-04-11T15:11:07Z Remote Sensing in Agriculture: State-of-the-Art Borgogno-Mondino, Enrico Tarantino, Eufemia Capolupo, Alessandra feature selection spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology 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. 2022-12-06T16:11:18Z 2022-12-06T16:11:18Z 2022 book ONIX_20221206_9783036554839_73 9783036554839 9783036554846 https://directory.doabooks.org/handle/20.500.12854/94550 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6385 https://mdpi.com/books/pdfview/book/6385 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5484-6 10.3390/books978-3-0365-5484-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036554839 9783036554846 220 Basel open access |
| spellingShingle | feature selection spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Remote Sensing in Agriculture: State-of-the-Art |
| title | Remote Sensing in Agriculture: State-of-the-Art |
| title_full | Remote Sensing in Agriculture: State-of-the-Art |
| title_fullStr | Remote Sensing in Agriculture: State-of-the-Art |
| title_full_unstemmed | Remote Sensing in Agriculture: State-of-the-Art |
| title_short | Remote Sensing in Agriculture: State-of-the-Art |
| title_sort | remote sensing in agriculture state of the art |
| topic | feature selection spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | feature selection spectral angle mapper support vector machine support vector regression hyperspectral imaging UAV cross-scale yellow rust spatial resolution winter wheat MODIS northern Mongolia remote sensing indices spring wheat yield estimation UAV-based LiDAR biomass crop height field phenotyping oasis crop type mapping Sentinel-1 and 2 integration statistically homogeneous pixels (SHPs) red-edge spectral bands and indices recursive feature increment (RFI) random forest (RF) unmanned aerial vehicles (UAVs) remote sensing (RS) thermal UAV RS thermal infrared (TIR) precision agriculture (PA) crop water stress monitoring plant disease detection vegetation status monitoring Landsat data blending crop yield prediction gap-filling volumetric soil moisture synthetic aperture radar (SAR) Sentinel-1 soil moisture semi-empirical model soil moisture Karnataka India reflectance digital number (DN) vegetation index (VI) Parrot Sequoia (Sequoia) DJI Phantom 4 Multispectral (P4M) Synthetic Aperture Radar SAR lodging Hidden Markov Random Field HMRF CDL corn soybean crop Monitoring crop management apple orchard damage polarimetric decomposition entropy anisotropy alpha angle storm damage mapping economic loss insurance support thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20221206_9783036554839_73 |