| 基于试验反射光谱数据的土壤含水率遥感反演 |
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| 引用本文： | 杨曦光,于颖.基于试验反射光谱数据的土壤含水率遥感反演[J].农业工程学报,2017,33(22):195-199. |
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| 作者姓名： | 杨曦光 于颖 |
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| 作者单位： | 1. 东北盐碱植被恢复与重建教育部重点实验室,东北林业大学盐碱地生物资源环境研究中心,哈尔滨 150040;2. 东北林业大学林学院,哈尔滨,150040 |
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| 基金项目： | 国家自然科学基金（31500519,31500518）；中央高校基本科研业务费（2572017BA06）。 |
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| 摘 要： | 土壤含水率是土壤水循环研究中不可或缺的参数,已广泛应用于土壤水分的监测。土壤光谱特性的研究是土壤含水率光学遥感定量反演的基础。该研究首先通过野外调查收集土样;然后,在实验室条件下制备不同水分梯度的土壤样品,并利用便携式地物光谱仪采集不同水分梯度土壤样品的反射光谱;最后,通过试验光谱数据分析建立一个基于指数函数的土壤含水率遥感反演模型,并对结果进行精度评价。结果表明,基于指数函数的土壤含水率反演模型可以较好的反演土壤水分特征,在640 nm处土壤含水率的估计值与真实值之间的决定系数为0.7062,RMSE为3.49%。相关研究为表层土壤含水量的遥感监测提供新方法和新思路。
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| 关 键 词： | 土壤含水率 遥感 模型 指数函数 反演模型 高光谱遥感 |
| 收稿时间： | 2017/6/5 0:00:00 |
| 修稿时间： | 2017/11/7 0:00:00 |
Remote sensing inversion of soil moisture based on laboratory spectral reflectance data |
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| Yang Xiguang and Yu Ying.Remote sensing inversion of soil moisture based on laboratory spectral reflectance data[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(22):195-199. |
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| Authors: | Yang Xiguang and Yu Ying |
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| Institution: | 1. Key Laboratory of Saline-Alkali Vegetation Ecology Restoration , Ministry of Education, Alkali Soil Natural Environmental Science Center , Northeast Forestry University, Harbin 150040, China; and 2. College of Forestry, Northeast Forestry University, Harbin 150040, China; |
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| Abstract: | Abstract: Soil moisture is one of the important components of soil and plays an important role in the material and vegetative nutrient transport process in the soil system. Soil moisture is also an essential soil physical parameter in the study on water cycle in ecological system, and a key variable of drought monitoring, soil erosion and surface evaporation studying. Therefore, soil moisture monitoring is very important. Remote sensing technology has been applied to soil moisture monitoring with its advantage of high efficiency and rapidness. The soil hyper-spectral ground experiment and the soil hyper-spectral characteristics are the basis for the inversion of soil moisture. In this paper, soil samples collected in field were mixed to achieve the purpose of keeping approximately constant soil properties. Then mixed soil sample was divided into 16 independent samples in order to ensure that the effects of soil properties on reflectance of each soil sample were at the same level, such as soil organic matter, soil texture, and soil salinity. After that, the samples were slowly irrigated with distilled water to get different levels of moisture. And the spectral data of each sample were measured at the same time under laboratory conditions. Based on this dataset, a remote sensing inversion model of soil moisture content based on exponential function was established and the parameters of model were fitted by using the experimental spectrum data. Fitted parameters illustrated the effects of soil moisture on soil reflected energy at each single band from 350 to 2 500 nm. A larger value of the fitted parameter indicated that more energy was absorbed by water and less energy was reflected. Result showed that there were 2 peaks near 1 400 and 1 900 nm after a steady trend less than 1 300 nm. And this fitted result was consistent with the absorption coefficients of pure water. It indicates that the exponential model with physically definable parameters can be used to describe the characteristics of soil reflectance changing with soil moisture conditions. Then this inversion model was used to estimate the soil moisture based on laboratory spectral data. The accuracy varied with soil moisture level, and it was lower for samples with soil moisture larger than 32.75% and lower than 5.52%. When soil moisture was 32.75%, the maximum absolute error and the minimum absolute error were 134.89% and 25.44%, respectively. When soil moisture was lower than 5.52%, the maximum absolute error was larger than 200%. The estimation accuracy was better when the soil moisture was between 5.52% and 32.75%. The mean absolute error was less than 30% and the maximum absolute error was less than 70%. The determination coefficient and RMSE (root mean square error) between estimated and measured soil moisture content at 640 nm were 0.706 2 and 3.49%, respectively. The results indicate that the inversion model based on the exponential function can be used for soil moisture content estimation with good accuracy. This work provides new methods and ideas for monitoring topsoil moisture content by using remote sensing technology. |
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| Keywords: | soil moisture remote sensing models exponential function inversion model hyperspectral remote sensing |
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