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Proceedings Paper

Retrieving rice yield and biomass from Radarsat-2 SAR data with Artificial Neural Network (ANN)
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Paper Abstract

The main objective of this study was to retrieve rice yield and biomass fromRadarsat-2 SAR data with artificial neuralnetwork (ANN).For this purpose, a practical scheme for estimating rice yield from Radarsat-2 data is established, which demonstrates that Radarsat-2 data can serve asan important data source for monitoring rice system and estimating rice yield.The ANN was composed of the rice backscattering coefficients extracted from multi-temporal Radarsat-2 images and rice canopy parameters (i.e. height, moisture content and biomass) observed from the fields, and then it was applied to simulate the correlation betweenthese two parts. The rice yield and biomass onAugust 21 and September14 were retrieved based on the trained network, respectively. Compared with the measured data, the retrieved rice yield and biomassonAug.21 and Sept.14 were quite accurate.Our results suggested thatRadarsat-2SGX images can be usedto estimate rice yield regionally, and neural network method is feasible with respects to the estimation of rice yield and biomass.

Paper Details

Date Published: 24 September 2013
PDF: 6 pages
Proc. SPIE 8869, Remote Sensing and Modeling of Ecosystems for Sustainability X, 88690X (24 September 2013); doi: 10.1117/12.2022576
Show Author Affiliations
Zhuoxin Jing, East China Normal Univ. (China)
Ctr. for Earth Observation and Digital Earth (China)
Yuan Zhang, East China Normal Univ. (China)
Ctr. for Earth Observation and Digital Earth (China)
Kejing Wang, East China Normal Univ. (China)
Ctr. for Earth Observation and Digital Earth (China)
Runhe Shi, East China Normal Univ. (China)
Ctr. for Earth Observation and Digital Earth (China)


Published in SPIE Proceedings Vol. 8869:
Remote Sensing and Modeling of Ecosystems for Sustainability X
Wei Gao; Thomas J. Jackson; Jinnian Wang; Ni-Bin Chang, Editor(s)

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