| 基于模型平均法的表层土壤含水率多模型综合反演 |
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| 引用本文： | 王思楠,李瑞平,吴英杰,赵水霞,王秀青.基于模型平均法的表层土壤含水率多模型综合反演[J].农业工程学报,2022,38(5):87-94. |
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| 作者姓名： | 王思楠 李瑞平 吴英杰 赵水霞 王秀青 |
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| 作者单位： | 内蒙古农业大学水利与土木建筑工程学院,呼和浩特 010018,内蒙古农业大学水利与土木建筑工程学院,呼和浩特 010018;内蒙古自治区农牧业大数据研究与应用重点实验室,呼和浩特 010018,中国水利水电科学研究院牧区水利科学研究所,呼和浩特 010020,内蒙古自治区测绘地理信息中心,呼和浩特 010050 |
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| 基金项目： | 国家自然科学基金（52069021、51839006）；内蒙古自治区科技计划项目（201802123） |
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| 摘 要： | 土壤水分是农作物和牧草生长的关键因素，也是影响全球气候变化和水循环的重要因子，因此准确监测土壤水分对促进农牧业可持续管理至关重要。基于Landsat 8 OLI多光谱影像，在表面生物物理特性和地形参数的基础上，结合野外实测土壤含水率，采用经验模型法分别构建了反距离加权法（Inverse Distance Weighting，IDW）、多元线性回归（Multivariable Linear Regression，MLR）、随机森林（Random Forest，RF）的土壤含水率反演模型，并采用模型平均法（Granger-Ramanathan，GR）进行组合反演，结果显示，通过模型平均法组合单一模型后在该研究区的土壤含水率反演中表现出了更好的适用性，模型平均法GR对于土壤含水率的估算效果佳，建模集与验证集R2≥0.88，均方根误差均不大于1.42%，且模型性能远优于反距离加权法、多元线性回归和随机森林。
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| 关 键 词： | 土壤含水率 随机森林 模型 模型平均法 地质统计方法 |
| 收稿时间： | 2021/5/21 0:00:00 |
| 修稿时间： | 2021/11/11 0:00:00 |
Multi-model comprehensive inversion of surface soil moisture based on model averaging method |
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| Wang Sinan,Li Ruiping,Wu Yingjie,Zhao Shuixi,Wang Xiuqing.Multi-model comprehensive inversion of surface soil moisture based on model averaging method[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(5):87-94. |
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| Authors: | Wang Sinan Li Ruiping Wu Yingjie Zhao Shuixi Wang Xiuqing |
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| Institution: | 1. Water Conservancy and Civil Engineering, College of Inner Mongolia Agricultural University, Hohhot 010018, China;;1. Water Conservancy and Civil Engineering, College of Inner Mongolia Agricultural University, Hohhot 010018, China; 2. Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, Hohhot 010018, China;;3. Institute of Water Resources for Pastoral Area, China Institute of Water Resources and Hydropower Research, Hohhot 010020, China;; 4. Inner Mongolia Autonomous Region Surveying and Mapping Geographic Information Center, Hohhot 010050, China; |
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| Abstract: | Soil moisture has been one of the most important factors for crop and pasture growth, depending mainly on global climate change and the water cycle. It is very necessary to accurately monitor the soil moisture for the sustainable management of agriculture and livestock. In this study, a combined inversion of Multivariable Linear Regression (MLR) and Random Forest (RF) Soil moisture model was established to compare with the Inverse Distance Weighting (IDW) spatial interpolation of soil moisture using the Landsat 8 OLI multispectral imagery, surface biophysical properties, and topographic parameters. Field measurement of soil water content was also used to construct an empirical model before that. Two indicators were selected to evaluate the models using the Granger-Ramanathan (GR), including the coefficient of determination (R2) and Root Mean Square Error (RMSE). The inversion of the spatial distribution of soil moisture was then determined using single and combined models. There was a significant correlation between the different biophysical/topographic parameters and the surface soil moisture. In the surface biophysical characteristics, the correlation between the surface temperature, vegetation index, building index, water body index, albedo, brightness, greenness and surface soil moisture were significant. The correlation between the elevation/slope of topographic parameters and the soil surface moisture were extremely significant. There were different sensitivities of biophysical/topographic parameters to the surface soil moisture. Among them, there was the most significant correlation between the surface temperature and surface soil moisture. The interpolation model presented a higher accuracy in April and lower in August. The MLR and RF models presented higher accuracy in August and lower in April. The reason was that the vegetation growth was received a significant effect on the surface temperature in the sand area in August, where the model as the main variable was more sensitive to the prediction of surface soil moisture. The R2 of the RF model was above 0.8 in August and April, which was better than the MLR and IDW model, respectively, whereas the RMSE was the smallest. The RMSE of the RF model was roughly comparable to that of the MLR model, and differed significantly from that of the IDW model, which was lower than those of the IDW model, respectively. The highest inversion accuracy was achieved by the combined model, in which the R2 was above 0.85 in August and April, with the small increase compared with the optimal single RF model. The surface temperature and elevation were the dominant factors to determine the soil moisture content in different months. The overall soil moisture content was low, containing less than 20% (more than 95% of the total area) in most areas. The spatial distribution of soil moisture was closely related to the topographic landscape type, with the higher soil moisture content on the high and steep slopes in the north and southeast areas, whereas, the lower soil moisture content in the flat areas in the north-central region. |
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| Keywords: | soil moisture random forest models model averaging method geological statistics method |
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