Abstract
Synthetic Aperture Radar (SAR) remote sensing techniques play a significant role in modern agricultural crop monitoring by relating the plant structure (height, biomass, yield and growth-stage) to the backscattering behavior of the vegetative canopy. The current trend in crop monitoring is towards precision agriculture, which needs detailed morphology information. By predicting the physical structure, one can just determine the under and overgrowth conditions. In this study, we propose a probabilistic inversion algorithm for a Radiative Transfer Theory (RTT) model which relates the backscattering response of a canopy to its physical structure. The outcomes of the inversion provided promising results by estimating the dimensions of the primary structures with a small bias.
Original language | English |
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Title of host publication | 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 7121-7124 |
Number of pages | 4 |
ISBN (Electronic) | 9781509033324 |
DOIs | |
Publication status | Published - 1 Nov 2016 |
Event | 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2016-November |
Conference
Conference | 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 |
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Country/Territory | China |
City | Beijing |
Period | 10/07/16 → 15/07/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- crop morphology
- metamodels
- monitoring
- optimization
- Polarimetry
- precision agriculture
- SAR