Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”
The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3665 |
| _version_ | 1827702633523052544 |
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| author | Simone Pascucci Stefano Pignatti Raffaele Casa Roshanak Darvishzadeh Wenjiang Huang |
| author_facet | Simone Pascucci Stefano Pignatti Raffaele Casa Roshanak Darvishzadeh Wenjiang Huang |
| author_sort | Simone Pascucci |
| collection | DOAJ |
| description | The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. |
| first_indexed | 2024-03-10T15:00:00Z |
| format | Article |
| id | doaj.art-95313ad9a8084807b6632891dcb2ed40 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:00:00Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-95313ad9a8084807b6632891dcb2ed402023-11-20T20:15:05ZengMDPI AGRemote Sensing2072-42922020-11-011221366510.3390/rs12213665Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”Simone Pascucci0Stefano Pignatti1Raffaele Casa2Roshanak Darvishzadeh3Wenjiang Huang4Institute of Methodologies for Environmental Analysis (CNR IMAA), National Research Council, 85050 Tito Scalo, PZ, ItalyInstitute of Methodologies for Environmental Analysis (CNR IMAA), National Research Council, 85050 Tito Scalo, PZ, ItalyDepartment of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University Via San Camillo de Lellis, 01100 Viterbo, ItalyITC—Faculty of Geo-Information Science and Earth Observation, Department of Natural Resources, University of Twente, PO Box 217, 7500 AE Enschede, The NetherlandsAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. https://www.mdpi.com/2072-4292/12/21/3665hyperspectral remote sensing for soil and crops in agriculturehyperspectral imaging for vegetationplant traitshigh-resolution spectroscopy for agricultural soils and vegetationhyperspectral databases for agricultural soils and vegetationhyperspectral data as input for modelling soil, crop, and vegetation |
| spellingShingle | Simone Pascucci Stefano Pignatti Raffaele Casa Roshanak Darvishzadeh Wenjiang Huang Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” Remote Sensing hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation |
| title | Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” |
| title_full | Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” |
| title_fullStr | Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” |
| title_full_unstemmed | Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” |
| title_short | Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation” |
| title_sort | special issue hyperspectral remote sensing of agriculture and vegetation |
| topic | hyperspectral remote sensing for soil and crops in agriculture hyperspectral imaging for vegetation plant traits high-resolution spectroscopy for agricultural soils and vegetation hyperspectral databases for agricultural soils and vegetation hyperspectral data as input for modelling soil, crop, and vegetation |
| url | https://www.mdpi.com/2072-4292/12/21/3665 |
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