%0 Journal Article %T 基于改进作物散射模型的陕西杨凌区麦田土壤水分反演研究 %T Soil Moisture Inversion Based on Modified Crop Scattering Model Over Wheat Fields in Yangling District, Shaanxi Province %A 蔡庆空,李二俊,陶亮亮,潘洁晨,陈 超,王 果 %A CAI,Qingkong %A LI,Erjun %A TAO,Liangliang %A PAN,Jiechen %A CHEN,Chao %A WANG,Guo %J 土壤 %J Soils %@ 0253-9829 %V 52 %N 4 %D 2020 %P 846-852 %K 改进作物散射模型;RADARSAT-2;土壤水分;后向散射系数;冬小麦 %K Modified crop scattering model; RADARSAT-2; Soil moisture; Backscatter coefficient; Winter wheat %X 本文提出一种改进作物散射模型反演麦田土壤水分,该模型根据冬小麦等低矮植被的散射特性,在原模型的基础上保留植被层直接散射部分以及植被与地表相互耦合作用的信息,同时加入裸土地表的直接散射部分,并根据经验权重将两部分信息分离开,构建出适用于冬小麦等低矮植被的后向散射模型,并结合RADARSAT-2雷达数据以及陕西杨凌农田试验区的地面实测数据,计算得到改进模型的经验参数,进而对模型进行验证分析。研究结果表明:改进作物散射模型的模拟精度相对于未改进的作物散射模型有显著的提高,R2在HH和VV极化下都达到80% 以上。为了验证改进的作物散射模型算法及土壤水分反演的有效性,本研究将改进作物散射模型与TVDI光学指数模型、简化的MIMICS模型的土壤水分反演结果进行对比分析,改进的作物散射模型反演精度比TVDI和简化的MIMICS模型要好,R2达到84.3%,均方根误差为0.028 cm3/cm3,简化的MIMICS模型反演结果比TVDI要好,但是精度不高,R2为66.9%,均方根误差为0.043 cm3/cm3。改进的作物散射模型对地表植被比较敏感,可以有效地将冬小麦对雷达信号散射影响和裸土层散射贡献区分开,为植被覆盖下地表土壤水分的反演创造条件,给大面积大范围的地表土壤水分反演提供强有力的技术支撑。 %X In this paper, we proposed a modified crop scattering model to inverse soil moisture for low vegetation. According to the scattering characteristics of winter wheat, the model retained the direct scattering contribution of vegetation layer and the coupling component between vegetation and surface ground on the basis of the original model, but considered the direct scattering contribution of the underlying ground and separated the scattering effects between vegetation and surface bare soil by introducing an empirical weight. Combined with RADARSAT-2 radar data and ground measurements of farmland experimental area in Yangling District, Shaanxi Province, the values of structural parameters of the modified model were calculated and then the model was verified and analyzed. The results showed that the simulation accuracy of the modified model was significantly improved than the original model, withR2 of more than 80% in both HH and VV polarizations. In order to verify the effectiveness of the modified model algorithm and soil moisture inversion, soil moistures inversed by the modified model were compared with those by the TVDI optical model and the simplified MIMICS model, and the results showed that the modified model was better, with R2 and RMSE of 84.3% and 0.028 cm3/cm3, respectively. The simplified MIMICS model was better than TVDI, but the inversion accuracy was not high, withR2 of 66.9% and the RMSE of 0.043 cm3/cm3, respectively. In conclusion, the modified model is sensitive to surface vegetation, can effectively distinguish the influences of winter wheat and bare soil on radar signal, thus can create conditions for inversing surface soil moisture in vegetation-covered area and provide strong technical support for large-area and large-scale surface soil moisture inversion. %R 10.13758/j.cnki.tr.2020.04.027 %U http://soils.issas.ac.cn/tr/home %1 JIS Version 3.0.0