Reprint
Remote Sensing Applications for Agriculture and Crop Modelling
Edited by
February 2020
308 pages
- ISBN978-3-03928-226-5 (Paperback)
- ISBN978-3-03928-227-2 (PDF)
This book is a reprint of the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling that was published in
Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Summary
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, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
crop residue management; remote sensing; satellite images; hyperspectral sensor; vegetation index; yield monitoring; remote sensing; proximal sensing; crop modeling; soil; plant; management zone; spatial variability; temporal variability; precision agriculture; Á Trous algorithm; conservation agriculture; crop inventory; remote sensing; spectral-weight variations in fused images; soil stoichiometry; land use change; soil organic carbon; nitrogen; Tarim Basin; SPAD; leaf nitrogen concentration; nitrogen nutrition index; grain yield; dynamic model; wheat; disease; yield; septoria tritici blotch; leaf area index; crop modelling; decision support system for agrotechnology transfer (DSSAT); Cropsim-CERES Wheat; sorghum biomass; prediction modeling; machine learning; fAPAR; Sentinel-2 satellite imagery; big data technology; remote sensing; UAV; vegetation indices; relative frequencies; yield; precision agriculture; cultivars; crop growth model; data assimilation; Leaf Area Index; Sentinel-2; EPIC model; yield estimation; NDVI; remote sensing; GIS; precision farming; variable rate technology; yield mapping; protein content; wheat; canopy temperature depression; NDVI; RGB images; grain yield; δ13C; UAV chemical application; droplet drift; flat-fan atomizer; simulation analysis; control variables; agricultural land-cover; multi-spectral; generalized model; machine learning; crop type mapping; Integrated Administration and Control System; remote sensing; hydroponic; vegetable monitoring; crop production; spectral simulation; hyperspectral data; n/a; fractional cover; irrigation; satellite; crop simulation model; AquaCrop; yield mapping; remote sensing; durum wheat; precision agriculture; large cardamom; remote sensing; species modelling; habitat assessment; climate change