| 基于植被指数的猕猴桃根域土壤水分反演影响因素研究 |
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| 引用本文： | 张军,邓俊涛,倪国威,牛子杰,潘时佳,韩文霆.基于植被指数的猕猴桃根域土壤水分反演影响因素研究[J].农业机械学报,2022,53(12):223-230. |
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| 作者姓名： | 张军 邓俊涛 倪国威 牛子杰 潘时佳 韩文霆 |
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| 作者单位： | 西北农林科技大学 |
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| 基金项目： | 陕西省重点研发计划项目（2022NY-220）和陕西省自然科学基础研究计划项目（2021JQ-156） |
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| 摘 要： | 针对现有监测方式难以大面积准确监测植株个体水分状况，且猕猴桃果园的郁闭性导致根域土壤含水率（Root domain soil water content，RSWC）监测方法匮乏的问题，使用多层感知机（Multi-layer perceptron，MLP）和冠层植被指数来预测果实膨大期（5—9月）徐香猕猴桃植株40cm深度的RSWC。在MLP训练数据的预处理中，采用Pearson相关系数作为输入（植被指数）与输出（RSWC）的相关性评价指标，采用单因素方差分析作为输入与输出的显著性评价指标。进一步考虑冠层采集范围可能对模型精度造成的影响，将数据分割为不同尺度对MLP进行训练评估。结果表明，重归一化植被指数（Renormalized difference vegetation index，RDVI）与RSWC具有最高的相关性与显著性，相关系数和P分别为0.744和0.007，该指数可以作为RSWC反演的输入量。对不同尺度RDVI的建模数据表明，模型精度与RDVI采样面积A及对角线长度L有着较强的相关性（R2分别为0.991和0.993），为了使模型精度最大化，采样面积应在2.540~3.038m2之间。通过使用该尺度的RDVI建立的MLP模型达到最大精度（R2为0.638，RMSE为0.016）。本研究可为建立非接触性猕猴桃果园土壤含水率估算方法与果园灌溉系统设计提供依据。
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| 关 键 词： | 多光谱 无人机遥感 植被指数 猕猴桃根域 作物水分胁迫 土壤水分反演 |
| 收稿时间： | 2022/7/7 0:00:00 |
Influencing Factors of Soil Moisture Content Inversion in Kiwifruit Root Region Based on Vegetation Index |
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| ZHANG Jun,DENG Juntao,NI Guowei,NIU Zijie,PAN Shiji,HAN Wenting.Influencing Factors of Soil Moisture Content Inversion in Kiwifruit Root Region Based on Vegetation Index[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(12):223-230. |
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| Authors: | ZHANG Jun DENG Juntao NI Guowei NIU Zijie PAN Shiji HAN Wenting |
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| Institution: | Northwest A&F University |
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| Abstract: | Aiming at the problems that the existing monitoring methods are difficult to accurately monitor the individual water status of plants in a large area, and the canopy closure of kiwifruit orchard leads to lack of root domain soil water content (RSWC) monitoring methods. Multi-layer perceptron (MLP) and canopy vegetation index were used to predict RSWC at 40cm depth of kiwifruit Xuxiang during fruit expansion period (May-September). In the preprocessing of MLP training data, Pearson correlation coefficient was used as the correlation evaluation index between input (vegetation index) and output (RSWC), and one-way ANOVA was used as the significance evaluation index between input and output. Further considering the possible impact of canopy acquisition range on model accuracy, the data were divided into different scales for training and evaluation of MLP. The results showed that renormalized difference vegetation index (RDVI) and RSWC had the highest correlation and significance, the correlation coefficient and P value were 0.744 and 0.007, respectively. This index could be used as the input of RSWC inversion. The modeling data of RDVI at different scales showed that the model accuracy was strongly correlated with RDVI sampling area A and diagonal length L(R2 was 0.991 and 0.993, respectively). In order to maximize the model accuracy, the sampling area should be between 2.540m2 and 3.038m2. The MLP model established by using RDVI of this scale achieved the maximum accuracy (R2 was 0.638, RMSE was 0.016). The research result can provide a basis for the establishment of soil water content estimation method and orchard irrigation system design of non-contact kiwifruit orchard. |
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| Keywords: | multispectral unmanned aerial vehicle remote sensing vegetation index kiwifruit root domain crop water stress soil moisture retrieval |
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