Research Papers

Rice biomass retrieval from multitemporal ground-based scatterometer data and RADARSAT-2 images using neural networks

[+] Author Affiliations
Mingquan Jia

University of Electronic Science and Technology of China (UESTC), School of Automation Engineering, Chengdu, Sichuan 611731, China

Ling Tong

University of Electronic Science and Technology of China (UESTC), School of Automation Engineering, Chengdu, Sichuan 611731, China

Yan Chen

University of Electronic Science and Technology of China (UESTC), School of Automation Engineering, Chengdu, Sichuan 611731, China

Yong Wang

University of Electronic Science and Technology of China (UESTC), School of Resources and Environment, Chengdu, Sichuan 611731, China

East Carolina University, Department of Geography, Greenville, North Carolina 27858

Yuanzhi Zhang

Chinese University of Hong Kong, Yuen Yuen Research Centre for Satellite Remote Sensing, Institute of Space and Earth Information Science, Shatin, Hong Kong, China

J. Appl. Remote Sens. 7(1), 073509 (Sep 06, 2013). doi:10.1117/1.JRS.7.073509
History: Received May 19, 2013; Revised July 26, 2013; Accepted August 12, 2013
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Abstract.  A neural network (NN) algorithm to invert biomass of rice plants using quad-polarization radar datasets of ground-based scatterometer and spaceborne RADARSAT-2 has been studied. The NN is trained with pairs of multipolarization radar backscattering and biomass data. The backscattering data are simulated from a Monte Carlo backscatter model that uses the outputs from a growth model of the rice plant. The growth model is developed from the plant data collected in growing cycles of several years. In addition to producing parameters needed by the backscatter model, the growth model outputs the biomass value of the plant. Multipolarization data collected by a ground-based scatterometer at eight stages during the 2012 growing cycle are input to the NN to invert biomass. Satisfactory results are obtained due to a small root mean squared error (RMSE) of 0.477kg/m2 and a high correlation coefficient of 0.989 when the inverted and measured biomass values are compared. Finally, RADARSAT-2 synthetic aperture radar images acquired on four different dates during the 2012 growth period are analyzed to delineate rice paddies within the study area and to invert biomass using the NN. Inversion results from the delineated rice paddies are encouraging because the RMSE is 0.582kg/m2 and correlation coefficient is 0.983.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Mingquan Jia ; Ling Tong ; Yan Chen ; Yong Wang and Yuanzhi Zhang
"Rice biomass retrieval from multitemporal ground-based scatterometer data and RADARSAT-2 images using neural networks", J. Appl. Remote Sens. 7(1), 073509 (Sep 06, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.073509


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