Remote Sensing Applications for Agriculture and Crop Modelling
Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable meth...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04783naaaa2201285uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_58167 | ||
| 005 | 20210212 | ||
| 020 | |a books978-3-03928-227-2 | ||
| 020 | |a 9783039282265 | ||
| 020 | |a 9783039282272 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-227-2 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Toscano, Piero |4 auth | |
| 245 | 1 | 0 | |a Remote Sensing Applications for Agriculture and Crop Modelling |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (308 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a nitrogen nutrition index | ||
| 653 | |a n/a | ||
| 653 | |a soil organic carbon | ||
| 653 | |a yield estimation | ||
| 653 | |a hyperspectral sensor | ||
| 653 | |a crop modeling | ||
| 653 | |a crop residue management | ||
| 653 | |a land use change | ||
| 653 | |a flat-fan atomizer | ||
| 653 | |a vegetation index | ||
| 653 | |a septoria tritici blotch | ||
| 653 | |a crop simulation model | ||
| 653 | |a temporal variability | ||
| 653 | |a spectral-weight variations in fused images | ||
| 653 | |a plant | ||
| 653 | |a EPIC model | ||
| 653 | |a large cardamom | ||
| 653 | |a crop inventory | ||
| 653 | |a proximal sensing | ||
| 653 | |a sorghum biomass | ||
| 653 | |a soil | ||
| 653 | |a UAV | ||
| 653 | |a Integrated Administration and Control System | ||
| 653 | |a canopy temperature depression | ||
| 653 | |a fractional cover | ||
| 653 | |a Cropsim-CERES Wheat | ||
| 653 | |a hyperspectral data | ||
| 653 | |a yield | ||
| 653 | |a wheat | ||
| 653 | |a precision farming | ||
| 653 | |a SPAD | ||
| 653 | |a AquaCrop | ||
| 653 | |a prediction modeling | ||
| 653 | |a spectral simulation | ||
| 653 | |a leaf nitrogen concentration | ||
| 653 | |a machine learning | ||
| 653 | |a crop production | ||
| 653 | |a protein content | ||
| 653 | |a Á Trous algorithm | ||
| 653 | |a spatial variability | ||
| 653 | |a variable rate technology | ||
| 653 | |a crop type mapping | ||
| 653 | |a Tarim Basin | ||
| 653 | |a leaf area index | ||
| 653 | |a management zone | ||
| 653 | |a irrigation | ||
| 653 | |a multi-spectral | ||
| 653 | |a agricultural land-cover | ||
| 653 | |a crop modelling | ||
| 653 | |a dynamic model | ||
| 653 | |a satellite images | ||
| 653 | |a climate change | ||
| 653 | |a control variables | ||
| 653 | |a generalized model | ||
| 653 | |a Sentinel-2 satellite imagery | ||
| 653 | |a vegetation indices | ||
| 653 | |a vegetable monitoring | ||
| 653 | |a Sentinel-2 | ||
| 653 | |a remote sensing | ||
| 653 | |a cultivars | ||
| 653 | |a crop growth model | ||
| 653 | |a yield monitoring | ||
| 653 | |a big data technology | ||
| 653 | |a conservation agriculture | ||
| 653 | |a GIS | ||
| 653 | |a fAPAR | ||
| 653 | |a droplet drift | ||
| 653 | |a simulation analysis | ||
| 653 | |a durum wheat | ||
| 653 | |a hydroponic | ||
| 653 | |a grain yield | ||
| 653 | |a Leaf Area Index | ||
| 653 | |a NDVI | ||
| 653 | |a precision agriculture | ||
| 653 | |a relative frequencies | ||
| 653 | |a soil stoichiometry | ||
| 653 | |a habitat assessment | ||
| 653 | |a data assimilation | ||
| 653 | |a satellite | ||
| 653 | |a species modelling | ||
| 653 | |a ?13C | ||
| 653 | |a disease | ||
| 653 | |a nitrogen | ||
| 653 | |a yield mapping | ||
| 653 | |a UAV chemical application | ||
| 653 | |a RGB images | ||
| 653 | |a decision support system for agrotechnology transfer (DSSAT) | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2023 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/58167 |7 0 |z DOAB: description of the publication |