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马红章,刘素美,彭爱华,孙林,孙根云.L波段主被动微波协同反演裸土土壤水分[J].农业工程学报,2016,32(19):133-138.DOI:10.11975/j.issn.1002-6819.2016.19.019
L波段主被动微波协同反演裸土土壤水分
投稿时间:2015-11-17  修订日期:2016-06-21
中文关键词:  土壤水分  遥感  算法  模型  主被动微波  裸露地表  地表粗糙度
基金项目:国家自然基金项目(41201336/41471353);中央高校基础科研专项(14CX02143A /15CX05056A)
作者单位
马红章 1. 中国石油大学<华东>理学院青岛 266580; 
刘素美 1. 中国石油大学<华东>理学院青岛 266580; 
彭爱华 1. 中国石油大学<华东>理学院青岛 266580; 
孙林 2. 山东科技大学测绘科学与工程学院青岛 266590; 
孙根云 3. 中国石油大学<华东>地球科学与技术学院青岛 266580; 
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中文摘要:土壤水分是进行干旱监测、土壤侵蚀、农作物产量预测以及地表温度研究的重要参量,利用主被动微波协同的方法提高土壤水分的反演精度是定量遥感发展所面临的重要任务。本文基于土壤L波段微波散射辐射模拟数据集,通过对比分析主被动微波数据对土壤水分含量和粗糙度2个参数的敏感性,提出了基于L波段主被动协同的裸土土壤水分反演算法,算法充分利用了被动微波地表发射率对土壤水分较为敏感,而主动微波后向散射系数对地表粗糙度较为敏感的特点。首先由地表垂直极化发射率和VV极化后向散射系数协同提取地表粗糙度信息,再由被动微波双极化数据结合地表粗糙度信息来估算土壤水分信息。利用SMAPVEX12实验数据集中部分稀疏植被采样点的观测数据对算法进行验证,验证结果显示,土壤水分反演结果与地面实测数据相关性为0.6637,RMSE为0.0607 cm3/cm3。该文反演算法模型系数直接由模拟数据集计算得到,克服了常规经验算法的发展对地表实测数据的依赖性,减小了算法在实际应用中的局限性。
Ma Hongzhang,Liu Sumei,Peng Aihua,Sun Lin,Sun Genyun.Active and passive cooperative algorithm at L-Band for bare soil moisture inversion[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2016,32(19):133-138.DOI:10.11975/j.issn.1002-6819.2016.19.019
Active and passive cooperative algorithm at L-Band for bare soil moisture inversion
Author NameAffiliation
Ma Hongzhang 1. College of Science, China University of Petroleum, Qingdao 266580, China 
Liu Sumei 1. College of Science, China University of Petroleum, Qingdao 266580, China 
Peng Aihua 1. College of Science, China University of Petroleum, Qingdao 266580, China 
Sun Lin 2. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China 
Sun Genyun 3. School of Geosciences, China University of Petroleum, Qingdao 266580, China 
Key words:soil moisture  remote sensing  algorithms  models  active and passive microwave  bare soil  surface roughness
Abstract: Soil moisture (SM) is an important parameter for drought monitoring as well as soil erosion, crop production and surface temperature. Microwave remote sensing especially at L-band is one of the most promising approaches to monitor the variable of SM at the global scale with frequent revisiting times. We carried out a large number of experimental and theoretical studies to assess the potential of monitoring soil moisture by means of both active and passive microwave satellite observations. Improving the inversion accuracy of soil moisture by combining active and passive microwave remote sensing data is an important task for the development of quantitative remote sensing. In this paper, the main objective was to estimate soil volumetric water content (SVWC) of bare soil collaborative using the active and passive observations. A novel approach, which was based on the simulation database of the advanced integral equation model (AIEM), was presented. In the method, we took the advantages of the active and passive microwave remote sensing data to obtain SVWC. Based on the analysis of the simulation database, some significant patterns had been found that the characteristics of the vertical polarization microwave emissivity (EV) had stronger sensitivity to the soil moisture than the roughness while the active microwave backscatter coefficient was more sensitive to the soil roughness than passive microwave. Among the vertical polarization microwave emissivity (EV), horizontal polarization microwave emissivity (EH) and VV backscatter coefficient (σvv), the effect of roughness on the σvv was greater than on passive microwave surface emissivity (EV and EH) and the effect of roughness on EV was minimized. The EV and the σvv were used to estimate the soil surface roughness, and on the premise of knowing soil surface roughness, soil moisture can be accurately calculated by passive microwave data. This algorithm had following two steps, the first step was to estimate the soil surface roughness using the vertical polarization emissivity and VV polarization backscatter coefficient; and the second step was to estimate SVWC using a combination of the EV and EH at L band under the condition of soil roughness was known. At last, based on the dataset of the SMAP experimental campaigns carried out in 2012 (SMAPVEX12), we evaluated the superiority of the algorithm. The validation data were obtained through the online Data Pool at the National Snow and Ice Data Center of the SMAPVEX12, the good inversion results were achieved with R2 equal to 0.6637 and the RMSE equal to 0.0607 cm3/cm3. One of the biggest advantages of the algorithm developed in this paper was that the coefficient of algorithm was calculated by the simulated data set, which different from the conventional experience algorithm that depended on the field data, which reduced greatly the limitations of the algorithm in practical application. The threshold value 0.25 of NDVI was used to select the appropriate sampling points for validation and the error of the result partly came from the effect of the little vegetation covering the surface, so the algorithm proposed in this paper should be revalidated using the observed data of the bare soil in the future.
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