| 徐灿,胡笑涛,陈滇豫,甄晶博,王文娥,彭雪莲,汝晨.基于无人机多光谱遥感估算西北半湿润区葡萄基础作物系数研究[J].干旱地区农业研究,2023,(4):106~117 |
| 基于无人机多光谱遥感估算西北半湿润区葡萄基础作物系数研究 |
| Estimation of grape basal crop coefficient in northwestern semi\|humid zone based on UAV multispectral remote sensing |
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| DOI：10.7606/j.issn.1000-7601.2023.04.11 |
| 中文关键词: 葡萄 无人机多光谱遥感 植被指数 基础作物系数 蒸散量 |
| 英文关键词:grape UAV multi\|spectral remote sensing vegetation index basal crop coefficient evapotranspiration |
| 基金项目:国家重点研发计划课题（2017YFD0201508）；国家自然科学基金（U224320036） |
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| 中文摘要: |
| 为提高西北半湿润区葡萄园蒸散量的估算精度，以波文比系统实测蒸散量ETc为基础，基于彭曼公式法计算参考作物蒸散量ETo，得到葡萄作物系数Kc后，采用FAO-56双作物系数法计算土壤蒸发系数Ke与水分胁迫系数Ks，获得基础作物系数Kcb；同时利用无人机多光谱遥感影像获取葡萄光谱数据，提取多个波段反射率计算4种植被指数（归一化植被指数NDVI、土壤调节植被指数SAVI、比值植被指数RVI、差值植被指数DVI），建立葡萄Kcb与植被指数的关系模型（一元线性回归、多项式回归、多元线性回归），从而计算葡萄园实际蒸散量用以验证无人机多光谱遥感估算葡萄Kcb的精度。结果表明：（1）相同建模方法下，植被指数与Kcb的模型拟合精度受到其种类与葡萄生长时期的影响。在生育前期，利用一元线性回归建模得到的Kcb-VIs模型拟合精度表现为NDVI>RVI>SAVI>DVI；在生育后期，拟合精度表现为RVI>DVI>SAVI>NDVI；在全生育阶段，拟合精度则表现为SAVI>NDVI>DVI>RVI。不同建模方法对Kcb的拟合精度不同，多元线性回归模型拟合效果最佳。（2）生育阶段、植被指数种类及建模方法是影响蒸散量估算精度的3个重要因素。在生育前期，利用DVI与Kcb建立的多项式回归模型的验证精度最高（EF=0.79）；在生育后期，多元线性回归模型验证精度最高（EF=0.80）；在全生育阶段，利用DVI与Kcb建立的一元线性回归模型的验证精度最高（EF=0.73）。（3）分生育阶段建立Kcb与植被指数的关系模型，反演得到的Kcb值较FAO-56双作物系数法推荐的Kcb值（EF=0.58）对蒸散量的估算精度提高了6%以上。 |
| 英文摘要: |
| To improve the estimation accuracy of vineyard evapotranspiration in northwestern semi\|humid area, this study calculated the actual crop evapotranspiration ETc by the Bowen Ratio System and the reference crop evapotranspiration ETo based on Penman Formula. The grape crop coefficient Kc was obtained by the division of the two. The FAO-56 double crop coefficient method was used to calculate soil evaporation coefficient Ke and water stress coefficient Ks and obtain the basal crop coefficient Kcb. The spectral data of grape were obtained by using UAV multi\|spectral remote sensing image. Reflectance of multiple bands was extracted to calculate four vegetation indexes (Normalized difference vegetation index NDVI, soil adjusted vegetation index SAVI, ratio vegetation index RVI, and difference vegetation index DVI). The relationship model (unary linear regression, polynomial regression and multiple linear regression) between the coefficient of Kcb and vegetation index was established, so as to calculate the actual evapotranspiration of vineyard to verify the accuracy of UAV multi\|spectral remote sensing estimation of grape Kcb. The results showed that (1) Under the same modeling method, the model fitting accuracy of vegetation index and Kcb was affected by the species and grape growth period. In the early stage of growth, the fitting accuracy of Kcb-VIs model obtained by unitary linear regression modeling was NDVI>RVI>SAVI>DVI. In the later growth period, the fitting accuracy was RVI>DVI>SAVI>NDVI. In the whole growth stage, the fitting accuracy was SAVI>NDVI>DVI>RVI. The fitting accuracy of Kcb differed with modeling methods, and the fitting effect of multiple linear regression model was the best. (2) Growth stage, vegetation index type and modeling method were three important factors affecting the accuracy of evapotranspiration estimation. In the early growth stage, the accuracy of the polynomial regression model established by DVI and Kcb was the highest (EF=0.79). In the later growth stage, the accuracy of the multiple linear regression model was the highest (ET=0.80). In the whole growth stage, the validation accuracy of the unitary linear regression model based on DVI and Kcb was the highest (EF=0.73). (3) The relationship model between Kcb and vegetation index was established at different growth stages. Compared with the Kcb value recommended by FAO-56 double crop coefficient method (EF=0.58), the inversion Kcb value improved the estimation accuracy of evapotranspiration by more than 6%. |
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