多源遥感数据反演土壤水分方法  

Soil moisture retrieval from multi-resource remotely sensed images over a wheat area

作　　者：张友静[1] 王军战[2] 鲍艳松[3] 

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098 [2]中国科学院寒区旱区环境与工程研究所,甘肃兰州730000 [3]南京信息工程大学大气物理学院,江苏南京210044

出　　处：《水科学进展》2010年第2期222-228,共7页Advances in Water Science

基　　金：国家自然科学基金资助项目(40701130;40830639)~~

摘　　要：基于ASAR-APP影像数据和光学影像数据,根据水云模型研究了小麦覆盖下地表土壤含水量的反演方法。利用TM和MODIS影像构建的植被生物、物理参数与实测小麦含水量进行回归分析,发现TM影像提取的归一化水分指数(NDWI)反演精度较好,相关系数达到0.87。根据这一关系,结合水云模型并联立裸露地表土壤湿度反演模型,建立了基于多源遥感数据的土壤含水量反演模型和参数统一求解方案。反演结果表明:该方案可得到理想的土壤水分反演精度,并可控制参数估计的误差。反演土壤含水量和准同步实测数据的相关系数为0.9,均方根误差为3.83%。在此基础上,分析了模型参数的敏感性,并制作了研究区土壤缺水量分布图。The aim of this study is to use the multi-resource remotely sensed images over land surfaces in a semi-arid region in northern China to infer the soil moisture content（SMC） in the presence of wheat.The multi-images include Advanced Synthetic Aperture Radar（ASAR）,Landsat Thematic Mapper（TM） and Moderate-resolution Imaging Spectroradiometer（MODIS）.The influence of vegetation on the satellite signal can be described by a water-cloud model.The vegetation water content（Mv）,an important parameter in the water-cloud model,was synchronously measured with the corresponding satellite passes over the wheat area.The evaluation of the relationship between Mv and four satellite-derived vegetation indices（VIs） shows that the NDWI（normalized difference water index） from TM has the closest relationship with Mv as revealed by the correlation coefficient of 0.87.Based on the established Mv-NDWI relationship,combining the water-cloud model with the soil moisture retrieval over bare soil conditions,a semi-empirical model is developed for estimating SMC over a wheat area.A sensitivity analysis is also performed on the model parameters.In comparison with the in-situ soil moisture observation,a satisfactory result is obtained in the soil moisture retrieval using the semi-empirical model as revealed by the RMSE（root mean squared error） of 3.83% and the correlation coefficient of 0.9.The spatial distribution of soil moisture deficits in the study area is also produced.

关 键 词：土壤含水量 多源遥感数据 水云模型 ASAR 多尺度 

分 类 号：P338.9[天文地球—水文科学]