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杨贵军,岳继博,李长春,冯海宽,杨浩,兰玉彬.基于改进水云模型和Radarsat-2数据的农田土壤含水量估算[J].农业工程学报,2016,32(22):146-153.DOI:10.11975/j.issn.1002-6819.2016.22.020
基于改进水云模型和Radarsat-2数据的农田土壤含水量估算
投稿时间:2016-03-29  修订日期:2016-10-20
中文关键词:  土壤  水分  植被  水云模型  Radarsat-2  冬小麦  叶面积指数
基金项目:国家自然科学基金项目(61661136003,41471351,41401477);北京市自然科学基金项目(4141001);北京市农林科学院科技创新能力建设项目(KJCX20140417);河南省基础与前沿研究(152300410098)。
作者单位
杨贵军 1. 北京农业信息技术研究中心北京 100097
2. 国家农业信息化工程技术研究中心北京 100097
3. 农业部农业信息技术重点实验室北京 100097
 
岳继博 1. 北京农业信息技术研究中心北京 100097
2. 国家农业信息化工程技术研究中心北京 100097
5. 河南理工大学测绘与国土信息工程学院焦作 454000
 
李长春 5. 河南理工大学测绘与国土信息工程学院焦作 454000
 
冯海宽 1. 北京农业信息技术研究中心北京 100097
2. 国家农业信息化工程技术研究中心北京 100097
 
杨浩 2. 国家农业信息化工程技术研究中心北京 100097
3. 农业部农业信息技术重点实验室北京 100097
 
兰玉彬 3. 农业部农业信息技术重点实验室北京 100097
4. 北京市农业物联网工程技术研究中心北京 100097
 
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中文摘要:为了直接将雷达遥感中"水云模型"进行反演应用,该研究将"水云模型"中植被参数改为雷达植被指数,利用全极化数据直接支持遥感反演土壤含水量,无需遥感反演植被参数输入。改进模型为利用雷达遥感结合"水云模型"进行土壤含水量监测提供了一种高效便捷方法。基于Radarsat-2全极化数据对冬小麦覆盖的农田土壤含水量进行估算,利用2014年在陕西杨凌区获取的4个生育期内Radarsat-2卫星数据及同步田间测量108组冬小麦农田土壤含水量地面测量数据进行模型参数校正和精度验证。验证结果精度为:改进的雷达植被指数模型>原叶面积指数模型(实测叶面积指数验证)>原叶面积指数模型(光学遥感反演叶面积指数验证),且改进的雷达植被指数模型可以在多个生育期内对农田土壤含水量进行监测。
Yang Guijun,Yue Jibo,Li Changchun,Feng Haikuan,Yang Hao,Lan Yubin.Estimation of soil moisture in farmland using improved water cloud model and Radarsat-2 data[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2016,32(22):146-153.DOI:10.11975/j.issn.1002-6819.2016.22.020
Estimation of soil moisture in farmland using improved water cloud model and Radarsat-2 data
Author NameAffiliation
Yang Guijun 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
 
Yue Jibo 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
5. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
 
Li Changchun 5. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
 
Feng Haikuan 1. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
 
Yang Hao 2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
 
Lan Yubin 3. Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
4. Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China
 
Key words:soils  moisture  vegetation  water cloud model  Radarsat-2  winter wheat  leaf area index
Abstract: Soil moisture is one key factor to restrict the growth of crops in rainless regions, and it is crucial to farmland production and can significantly affect the irrigation decision-making for agricultural management. Polarimetric SAR is very sensitive to soil moisture and can penetrate smoke, fog, rain and snow, etc. Therefore, polarimetric SAR system can operate in all-weather conditions. Moreover, soil moisture of farmland can be detected the by penetrating the vegetation and soil to reach the subsurface. In this study, in-situ measurements were carried out in Yangling district, Shannxi province, an important winter wheat producing area in China. Soil moisture and crop parameters (such as leaf area index (LAI), biomass and vegetation water content) were obtained by different teams at Radarsat-2 satellite transit time. Radar vegetation index (RVI), which was less sensitive to change in environmental conditions, had strong correlations with LAI, biomass and vegetation water content, etc. Aiming at monitoring soil moisture by satellite polarimetric SAR data conveniently and accurately, an improved RVI model for soil moisture estimation was proposed via bare soil model and RVI combined with water cloud model (WCM). 108 soil moisture samples (including jointing, heading and filling stageof winter wheat) were used to estimate and verify the accuracy of the parameters in the WCM, among which 62 samples were used to estimate the empirical parameters of the model, and the remaining 46 samples to verify the accuracy of estimated parameters. Usually, the crop parameters were used to describe the water cloud in the WCM, however, it causes lots of difficulties to apply this model. The crop parameters in improved RVI model were replaced by the RVI to reduce the uncertainty of the WCM. The RVI, instead of the crop parameters, was used to describe the water cloud in WCM, then the soil moisture values estimated by LAI model and improved RVI model were compared with in-situ measured soil moisture values, respectively. The LAI model was verified by LAI obtained in ground measurement and estimated by optical remote sensing data, respectively. To prove the potential applications, the improved RVI model was compared with two LAI models using ground measurement data. Conclusions were drawn based on the comparisons of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) as follows: 1) The improved RVI model (combined with the WCM, bare soil model and RVI) achieved the best accuracy (R2 was 0.41, RMSE was 5.7%, MAE was4.7%), the second was the original LAI model (using on ground measured LAI for validation, R2 was 0.28, RMSE was 7.1%, MAE was 5.1%), while the original LAI model (using optical remote sensing estimated LAI for validation) was the worst (R2 was 0.14, RMSE was 7.7%, MAE was 6.1%). 2) The improved RVI model, which used polarimetric SAR data and RVI as inputs, provided a convenient and efficient way to estimate the soil moisture. 3) The improved RVI model provided an accurate and rapid method for monitoring soil moisture in various growth stages of winter wheat.
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