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当前位置: 首页 > 文章 > 地面资料稀缺区域的农田土壤水分微波与光学遥感协同反演方法研究 中国农学通报 2018 (36) 117-123
Position: Home > Articles > Farmland Soil Moisture in the Area with Scare Ground Data: Synergetic Inversion Method of Microwave and Optical Remote Sensing Chinese Agricultural Science Bulletin 2018 (36) 117-123

地面资料稀缺区域的农田土壤水分微波与光学遥感协同反演方法研究

作  者：
汪倩倩;汪权方;王新生;张雨;王渊;唐文澜;全璟
单  位：
湖北大学资源环境学院;农业部遥感应用中心武汉分中心
关键词：
土壤水分;地面资料稀缺;高分三号;Landsat8;回归分析;江汉平原
摘  要：
大尺度区域范围的地表粗糙度因子难以获取是微波反演土壤水分的难点所在。对此,从协同微波和光学遥感数据在土壤水分监测中的优势以提高土壤水分反演精度的角度出发,笔者进行了地表粗糙度参数稀缺区域的大范围农田土壤水分遥感反演方法研究,即利用GF-3和Landsat8光学遥感数据,通过水云模型消除植被对雷达后向散射系数的影响,获取土壤直接后散射系数,然后结合入射角、PDI、TVDI和NDWI指数共同作为模型输入参数分别建立了HH和HV 2种极化方式下的土壤水分反演模型。结果表明:采用相关系数(R2)、一致性指数(IA)和均方根误差(RMSE),对2种模型应用于江汉平原农田土壤水分反演的实验结果进行了对比分析,结果显示HH极化方式下的土壤水分反演精度整体上优于HV极化方式(HH极化方式R2=0.6864,IA=0.8895,RMSE=6.979%)。在空间分布上,温度较高和植被覆盖较低区域,土壤水分含量较低,如钟祥市的北部、江陵县的东北部、荆门市的东部、仙桃市的西部和天门市的西北部;温度较低和植被覆盖较高区域,土壤水分较高,如监利县以及天门市的南部。
译  名：
Farmland Soil Moisture in the Area with Scare Ground Data: Synergetic Inversion Method of Microwave and Optical Remote Sensing
作  者：
Wang Qianqian;Wang Quanfang;Wang Xinsheng;Zhang Yu;Wang Yuan;Tang Wenlan;Quan Jing;School of Resources and Environment, Hubei University;Wuhan Branch Center of Remote Sensing Application Center, Ministry of Agriculture;
单  位：
Wang Qianqian%Wang Quanfang%Wang Xinsheng%Zhang Yu%Wang Yuan%Tang Wenlan%Quan Jing%School of Resources and Environment, Hubei University%Wuhan Branch Center of Remote Sensing Application Center, Ministry of Agriculture
关键词：
soil moisture;;scare ground data;;GF-3;;Landsat8;;regression analysis;;Jianghan Plain
摘  要：
The inability to obtain surface roughness factor in large scale area is the difficulty in microwave inversion of soil moisture. In this regard, from the perspective of the advantages of synergetic microwave andoptical remote sensing data in soil moisture monitoring to improve the inversion accuracy of soil moisture, westudied a remote sensing inversion method for soil moisture in large areas of surface roughness parameters. Theimpact of vegetation to the backscattering was removed by applying the water-cloud model based on GF-3 andLandsat8 remote sensing data. Then, the backscattering, the incident angle, PDI, TVDI and NDWI index wereused together as the model input parameters. The soil moisture inversion model was under 2 polarization modes(HH and HV). The results showed that: R~2, IA, RMSE were used to compare the experimental results of the twomodels in farmland of Jianghan Plain, the inversion accuracy of soil moisture under HH polarization was betterthan that of HV polarization mode(HH polarization method: R~2=0.6864, IA=0.8895, RMSE=6.979%). In spacedistribution, the areas with higher temperatures and lower vegetation coverage had lower soil moisture, such as the north of Zhongxiang, the northeast of Jiangling, the east of Jingmen, the west of Xiantao, and the northwest of Tianmen; the areas with lower temperatures and higher vegetation coverage had higher soil moisture, such as Jianli and the southern part of Tianmen.

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