KMS Institute of soil and water conservation Chinese Academy of Sciences
|Place of Conferral||北京|
快速准确获取大田作物系数Kc是旱区作物日蒸散量估算的关键。本文以2017-2018年内蒙古达拉特旗昭君镇精准灌溉试验站内大田玉米为对象，通过调节中心支轴式喷灌机转速来实现各扇形区域内不同水分的处理，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大田玉米冠层光谱影像并同步采集地面数据，实现不同水分处理下玉米作物系数无人机遥感与地面传感器协同监测。同时，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4种植被指数（比值植被指数SR、归一化差值植被指数NDVI、土壤调节植被指数SAVI、增强型植被指数EVI）、叶面积指数（leaf area index, LAI）和表层土壤水分含量（soil water content, SWC）的相关关系，分析了不同气候条件和不同水分胁迫条件下玉米作物系数无人机遥感与地面传感器协同估算的可行性。本文的主要研究内容及结论如下：
（1）由不同深度土壤含水率与作物系数的相关性分析发现，在不同水分胁迫处理下，表层土壤水分（30cm）均处于强相关水平且在水分胁迫程度最严重的情况下相关性最高达到0.72（P<0.01）。在强降雨天气下，浅层土壤含水率与作物系数的相关性大小排序为：10 cm SWC>20 cm SWC>30 cm SWC。
（2）在不同水分胁迫条件下，作物叶面积指数LAI与作物系数相关性无明显规律性差异且均处于较高水平(r = 0.45~0.60, P<0.05)。但在强降雨条件下，LAI相关系数较低(r = 0.04~0.27)。
The rapid and accurate acquisition of the field crop coefficient Kc is the key to estimating the daily evapotranspiration of crops in dryland. Based on the field corn under different water treatments in Zhaojun town Experimental Station in Dalate Qi, Inner Mongolia, 2017-2018. This paper uses the self-developed UAV remote sensing platform to obtain the canopy spectral image of maize and adjust the rotational speed of the sprinkled irrigation to achieve different water treatment in each region. It simultaneously collect ground data to achieve different Co-monitoring of remote sensing and ground sensors. The crop coefficient of maize was calculated by the FAO-56 dual crop coefficient method calibrated by meteorological factors and crop coverage. The relationship between the crop coefficient and the four different types of vegetation indices were studied (ratio vegetation index SR, normalized difference vegetation index NDVI, soil adjusted vegetation index SAVI, enhanced vegetation index EVI). Meanwhile, the relationship between the crop coefficient and leaf area index (LAI) , surface soil water content (SWC) was analyzed. The corn crop coefficients under different climatic conditions and different water stress conditions and feasibility of co-estimation of UAV remote sensing and ground sensors were analyzed. The main research contents and conclusions of this paper are as follows:
(3) The correlation between the four different types of vegetation indices and crop coefficient under different water treatment and different climatic conditions was: SR>NDVI>EVI>SAVI. Moreover, as the degree of water stress is aggravated, the correlation gradually decreases. This is because the vegetation index has a certain hysteresis in the late growth stage, and the response to water stress is not high.
(4) The 2017 Kc stepwise regression model based on the ratio vegetation index, leaf area index and surface soil moisture was established and verified. The coefficient of determination, root mean square error and normalized root mean square error were 0.60, 0.21 and 23.35%, respectively. It indicates that the established Kc estimation model have better estimation accuracy under different water stresses in the arid area. However, under heavy rainfall conditions in 2018, the model has been verified. The coefficient of determination, root mean square error and normalized root mean square error were 0.24, 0.16 and 15.5%, respectively. It can be seen that the three variables of the model explain the crop coefficients is not high enough and the accuracy is not enough under heavy rainfall conditions, but the RMSE is reduced, indicating that the difference between the estimated amount and the estimated amount is relatively stable.
(5) Based on the comparison between the actual evapotranspiration ET calculated by the FAO-56 dual crop coefficient method and the simulated evapotranspiration established by the model, the simulated values will gradually overestimate the actual evapotranspiration of the crop as the degree of water stress increases. However, under heavy rainfall conditions, although the mean difference between the two growth stages was not significant, the difference in daily evapotranspiration was significant, indicating that the model has low estimation accuracy under heavy rainfall conditions.
Keywords: Soil moisture, Stresses, Unmanned aerial vehicle, Crop coefficient, Simple ratio index, Leaf area index
|Subject Area||农学 ; 农业工程|
|MOST Discipline Catalogue||农学|
|Table of Contents|
|张瑜. 大田玉米作物系数机地协同估算方法研究[D]. 北京. 中国科学院大学,2019.|
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