Estimating Paddy Rice Biomass Using Radarsat-2 Data Based on Artificial Neural Network

Jing, Zhuoxin; Zhang, Yuan; Wang, Kejing

doi:10.2991/rsete.2013.103

Cited by 2 publications

(1 citation statement)

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“…As compared to optical sensors, spaceborne radar synthetic aperture (SAR) instruments can overcome optical inherent limitations of systems owing to its all-weather, day and capabilities night acquisition and sensitivity to surface characteristics (Koppe et al 2013). Most paddy rice is planted in warm and humid regions in the world, frequent cloud cover or rainfall often occurs in rice growing season (Jing et al 2013). Synthetic aperture radar (SAR) has great potential, especially in monsoon Asia, since optical observations are often hampered by cloudy conditions.…”

Section: Introductionmentioning

confidence: 99%

Satellite Imagery for Classification of Rice Growth Phase Using Freeman Decomposition in Indramayu, West Java, Indonesia

et al. 2018

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Monitoring at every growth of rice plants is an important information for determining the grain pro-duction estimation of rice. Monitoring must to be have timely work on the rice plant development. However, timely monitoring and the high accuracy of information is a challenge in remote sensing based on rice agriculture monitoring and observation. With increased quality of synthetic aperture radar (SAR) systems utilizing polarimetric information recently, the development and applications of polarimetric SAR (PolSAR) are one of the current major topics in radar remote sensing. The ad-vantages provided by PolSAR data for agricultural monitoring have been extensively studied for applications such as crop type classification and mapping, crop phenology monitoring, productivity assessment based on the sensitivity of polarimetric parameters to indicators of crop conditions. Freeman and Durden successfully decomposed fully PolSAR data into three components: Single bounce, double bounce, and volume scattering. The three-component scattering provide features for distinguishing between different surface cover types. These sensitivities assist in the identification of growing phase. The observed growing phase development in time series, reflected in the consistent temporal trends in scattering, was generally in agreement with crop phenological development stages. Supervised classification was performed on repeat-pass Radarsat-2 images, with an overall classification accuracy of 77.27% achieved using time series Fine beam data. The study demonstrated that Radarsat-2 Fine mode data provide useful information for crop monitoring and classification of rice plants.

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Section: Introductionmentioning

confidence: 99%