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提出了利用雷达数据进行水稻估产的技术方法,并以ASAR数据为例,探讨了雷达数据在水稻估产中的可行性。首先利用ASAR数据进行水稻制图,从各时相ASAR数据中提取水稻后向散射系数。随后,基于像元尺度,采用同化方法,以LAI为结合点,将水稻作物模型ORYZA2000与半经验水稻后向散射模型结合,建立嵌套模型模拟水稻后向散射系数。选择水稻出苗期和播种密度为参数优化对象,利用全局优化算法SCE-UA对ORYZA2000模型重新初始化,使模拟的水稻后向散射系数值与实测值误差最小,并由优化后的ORYZA2000模型计算每个像元的水稻产量,生成水稻产量分布图。结果表明,水稻产量分布图能够描绘研究区水稻实际产量的分布趋势,但由于采用潜在生长条件模拟,模拟的水稻平均产量比实测平均值高约13%,验证点的水稻产量模拟值与实测值相对误差为11.2%。由于半经验水稻后向散射模型存在对LAI变化不够敏感和对水层的简化处理,增加了水稻估产的误差。但从总体上看,利用该方法进行区域水稻估产是可行的,并为多云多雨地区的水稻遥感监测提供了重要参考。
A method of using radar data to estimate rice yield was put forward. Taking ASAR data as an example, the feasibility of radar data estimation in rice estimation was discussed. First, ASAR data were used for rice mapping, and rice backscattering coefficients were extracted from each phase ASAR data. Subsequently, based on the pixel size, the assimilation method was used to combine the rice crop model ORYZA2000 with semi-empirical rice backscatter model using LAI as the binding point, and a nested model was established to simulate the rice backscattering coefficient. Select the seedling and sowing density of rice as the parameter optimization object, using the global optimization algorithm SCE-UA to reinitialize the ORYZA2000 model, so that the simulated rice backscattering coefficient and the measured value error minimum, and by the optimized ORYZA2000 model to calculate each Pixel yield of rice yields a map of rice yield. The results showed that the distribution of rice yield could describe the distribution trend of actual rice yield in the study area. However, due to the simulation of potential growth conditions, the average yield of paddy rice was about 13% higher than the average measured value. The simulated and measured values The relative error is 11.2%. Due to the semi-empirical rice backscatter model is not sensitive enough to change the LAI and simplifies the aquatic layer, it increases the error of rice yield estimation. However, on the whole, it is feasible to use this method to estimate the area rice yield, and it provides an important reference for the remote sensing monitoring of rice in cloudy and rainy areas.