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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Rice Biomass Estimation Using Radar Backscattering Data at S-band
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Rice Biomass Estimation Using Radar Backscattering Data at S-band

机译：利用S波段雷达反向散射数据估算水稻生物量。

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This paper presents an inversion method based on neural networks (NN) to estimate rice biomass in a paddy rice field with fully polarimetric (HH, HV, VH, VV) measurements at S-band. The backscattering coefficients are measured by a ground-based scatterometer system during the rice growth period from May to September 2010. The rice growth parameters including biomass, leaf-area index (LAI) and canopy structure are collected by random sampling at the same time. Data analyses show that the multi-temporal backscattering coefficients are very sensitive to the changes of biomass, LAI, canopy height and stem density. We also find that multi-temporal observations are suitable for paddy detection in the early growth period, and co-polarimetric observations perform well for monitoring rice status in the late growth period. According to the field measurements, a rice growth model was established as the function of rice age. The model made the parameters more representative and universal than limited random measurements over a given rice field. The scatter model of rice fields was simulated based on Monte Carlo simulations. The input parameters in the scatter model were generated by the rice growth model. The simulation results of the scatter model were composed as the NN training dataset, which was used for training and accessing the NN inversion algorithm. Two NN models, a simple training model (STM) and a related training model (RTM), were applied to estimate rice biomass. The obtained results show that the root mean square error (RMSE = 0.816 Kg/m²) of the RTM is better than that of the STM (RMSE = 1.226 kg/m²). The results suggest that the inversion model is able to estimate rice biomass with radar backscattering coefficients at S-band.
机译：本文提出了一种基于神经网络的反演方法，通过在S波段进行全极化（HH，HV，VH，VV）测量，估算稻田中的水稻生物量。在2010年5月至2010年9月水稻生长期间，通过地面散射仪系统测量了背向散射系数。同时通过随机抽样收集了包括生物量，叶面积指数（LAI）和冠层结构在内的水稻生长参数。数据分析表明，多时相后向散射系数对生物量，LAI，冠层高度和茎密度的变化非常敏感。我们还发现，多时相观测适用于生育早期的稻谷检测，同极化观测在监测生育后期的水稻状况方面表现良好。根据田间测量，建立了水稻生长模型作为水稻年龄的函数。该模型使参数比给定稻田的有限随机测量更具代表性和通用性。基于蒙特卡洛模拟对稻田的散布模型进行了模拟。分散模型中的输入参数由水稻生长模型生成。分散模型的仿真结果被构成为NN训练数据集，用于训练和访问NN反演算法。两种神经网络模型分别是简单训练模型（STM）和相关训练模型（RTM），用于估算稻米生物量。所得结果表明，RTM的均方根误差（RMSE = 0.816 Kg /m²）优于STM（RMSE = 1.226 kg /m²）。结果表明，该反演模型能够在S波段用雷达后向散射系数估算水稻生物量。

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