由明明,常庆瑞,田明璐,班松涛,余蛟洋,张卓然.基于随机森林回归的油菜叶片SPAD值遥感估算[J].干旱地区农业研究,2019,37(1):74~81
基于随机森林回归的油菜叶片SPAD值遥感估算
Estimation of rapeseed leaf SPAD value based on random forest regression
  
DOI:10.7606/j.issn.1000-7601.2019.01.10
中文关键词:  油菜  SPAD值  遥感  随机森林模型  估算
英文关键词:rapeseed  SPAD value  remote sensing  random forest model  estimation
基金项目:国家高技术研究发展计划(863计划)资助项目(2013AA102401)
作者单位
由明明 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
常庆瑞 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
田明璐 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
班松涛 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
余蛟洋 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
张卓然 College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China 
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中文摘要:
      以西北地区典型经济作物油菜为研究对象,利用SVC-1024i型便携式光谱仪和SPAD-502型叶绿素仪测定了油菜不同生育期的叶片光谱反射率和SPAD值。通过分析油菜原始光谱及10种光谱指数与SPAD值的相关关系,基于光谱指数构建了不同生育期油菜叶片SPAD值随机森林回归(RF)估算模型,并利用独立样本对所建模型进行验证,同时结合传统的一元线性回归模型和多元逐步回归模型与其进行比较。结果表明:油菜叶片SPAD值在全生育期内呈现出先上升后下降的趋势;各光谱指数在不同生育期及全生育期与SPAD值的相关性均达到0.01水平的显著相关;基于光谱指数构建的随机森林回归模型在油菜各个生育期及全生育期建模和预测结果明显优于同期的传统回归模型,建模R2达0.90以上,验证R2达0.81以上,RMSE在1.571~5.004,RE在2.66%~13.22%,是油菜叶片SPAD值的最优估算模型。
英文摘要:
      The chlorophyll content is an importance parameter for evaluating crop growth and real-time non-destructive and quick estimation of leaf chlorophyll content can provide important information about plant stress, nutritional status, and relationships between plants and their environment, which has great significance in guiding agricultural production and improving crop yield. The conclusions are as follows: 1) With the increase of SPAD value, the leaf spectral reflectance decreased in the visible light region, and the “red shift” phenomena were detected at the red edge position. 2) The correlations between rapeseed leaf SPAD values and spectral indices were significant in all growth periods. Among the spectral indices, TCARI, GRVI, and NPCI were negatively correlated with leaf SPAD values, while the rest of spectral indices were positively correlated with the leaf SPAD values. 3) Leaf SPAD value estimation models using the traditional simple linear regression method, multiple stepwise regression method, and Random Forest method base on the spectral indices all passed the significance tests. In order to evaluate each model’s estimation accuracy and to further compare the performances of the three models for each stage, the coefficient of determination (R2) of each model was calculated respectively for both modeling sets and validation sets. The results indicated that random forest model had the best modeling and verification accuracy in each growth period with the coefficient of determination higher than 0.91 for the modeling and greater than 0.74 for the validation set. Therefore, Random Forest Model is an optimal model for estimating rapeseed leaf SPAD values and may provide a theoretical basis and technical support to improve remote sensing inversion accuracy of rapeseed chlorophyll content.
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