摘要
该文研究不同水分胁迫条件下无人机遥感与地面传感器协同估算玉米作物系数的可行性。利用自主研发的六旋翼无人机遥感平台搭载多光谱传感器获取内蒙古达拉特旗昭君镇试验站不同水分胁迫下大田玉米冠层光谱影像,计算植被指数,采用经气象因子和作物覆盖度校正后的FAO-56双作物系数法计算玉米的作物系数,研究作物系数与简单比值植被指数(simple ratio index,SR)、叶面积指数(leaf area index,LAI)和表层土壤含水率(surface soil moisture,SM)的相关关系,结果表明,作物系数与SR、LAI和SM的相关程度与水分胁迫程度有关,但均呈现出显著或极显著的线性关系,说明了基于这些指标建立作物系数估算模型的可能性。利用逐步回归分析方法建立了作物系数的估算模型,其估算模型,修正的决定系数、均方根误差和归一化的均方根误差分别为0.63、0.21、25.16%。经验证,模型决定系数、均方根误差和归一化的均方根误差分别为0.60、0.21、23.35%。研究结果可为利用无人机多光谱遥感平台进行作物系数估算提供技术参考。
The rapid and accurate acquisition of evapotranspiration in field crops is an urgent issue to be solved in crop evapotranspiration researches. In this paper, the feasibility of estimating crop coefficient by unmanned aerial vehicle(UAV) remote sensing in maize under different water stresses was analyzed. The experiment was conducted in Zhaojun town Experimental Station in Dalate Qi, Inner Mongolia. Full irrigation(TR1) was designed as 50% of field water holding capacity based on previous research results and local situation, which was considered as the base. The water stress condition(80% of the base soil moisture) was designed in the fast growth stage for TR2-TR4. The 82% and 43% of the base soil moisture were also designed for the late growth stage of TR2 and TR3, respectively. In addition, in the middle growth stage, the water stress with 65% of the base soil moisture was designed for TR4. The maize was planted on May 20 th,2017. The whole growth period lasted 110 days. The sprinkled irrigation was used for the experiment. The experimental area was partitioned into 4 regions for the different treatments. In each region, a squared area with the side length of 12 m was chosen for plant height and leaf area index measurements. The measurements were carried out every 2-5 days. Soil moisture at 30-cm depth was determined in each area as the surface soil moisture. Meanwhile, climatic parameters such as precipitation, air temperature, relative humidity and so on were collected from local meteorological station. Crop coefficient was calculated by dual crop coefficient method proposed by FAO56 based on meteorological parameters and plant height. The UAV multispectral monitoring system was to obtain the canopy spectral vegetation index of field maize under different water stress conditions. The UAS images were obtained at the same time with the plant height measurement. The simple ratio index was calculated based on reflectivity of red and near infrared band. The dynamic change of leaf area index, soil moisture, simple ratio index, and crop coefficient was analyzed. The results showed that the water stress heavily affected the leaf area index, the simple ratio index and the surface soil moisture was decreased in the late growth stage of maize, and the crop coefficient was low in the late growth stage. The correlation analysis between the crop coefficient, simple ratio index, leaf area index and surface soil moisture showed that the surface soil moisture had the highest correlation with the crop coefficient for the treatment of TR1, TR2 and TR4 but the leaf area index was highly correlated with the crop coefficient in the TR3. By stepwise regression analysis, the 3-variable model had the highest accuracy with the adjusted determination coefficient of 0.63, root mean square error of 0.21 and the normalized root mean square of error of 25.16%. The validation showed the determination coefficient of 0.60, root mean square error of 0.21 and the normalized root mean square of error of 23.35%. It indicated that the 3-variable model was well to estimate crop coefficient. The results provide a method support for crop coefficient estimation by UAV.
引文
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