ISWC OpenIR  > 水保所2018--2019届毕业生论文
大田玉米作物系数机地协同估算方法研究
张瑜
Subtype硕士
Thesis Advisor韩文霆
2019-05-17
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Name农学硕士
Abstract

    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0.72P<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)

34种植被指数与作物系数在不同水分处理以及不同气候条件下的相关性大小排序为:SR>NDVI>EVI>SAVI。而且随着水分胁迫程度加重,相关性逐渐降低,这是由于植被指数生长后期有一定的滞后性,对于水分胁迫响应程度不高。

4)基于比值植被指数、叶面积指数和表层土壤水分3个指标建立的2017Kc逐步回归模型且经验证,其决定系数、均方根误差和归一化的均方根误差分别为0.600.2123.35%,表明利用无人机监测的比值植被指数和地面监测的叶面积指数以及表层土壤含水率建立的Kc估算模型在干旱区不同水分胁迫下具有较好的估算精度。然而在2018年强降雨条件下该模型经验证,其决定系数、均方根误差和归一化的均方根误差分别为0.240.1615.5%,可见该模型三个变量对于作物系数的解释度不够高且在强降雨条件下精度不够,但RMSE减少,说明估计量与被估计量之间差异程度较稳定。

5基于FAO-56双作物系数法计算的实际蒸散量ET和根据模型建立的模拟蒸散量进行对比发现,随着水分胁迫程度加重,模拟值会逐渐高估作物实际蒸散量。然而在强降雨条件下,虽然各生育阶段内两者平均值差异不显著但在每日蒸散量估算上差异显著,说明该模型在强降雨条件下估算精度较低。

 

关键词:土壤水分,胁迫,无人机,作物系数,植被指数,叶面积指数

Other Abstract

      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:
(1) Correlation analysis between soil moisture content and crop coefficient at different depths showed that under different water stress treatments, surface soil moisture (30cm) was at a strong correlation level and the highest correlation was achieved 0.72 (P < 0.01) under the most severe water stress. Under heavy rainfall conditions, the correlation between shallow soil moisture content and crop coefficient is: 10 cm SWC>20 cm SWC>30 cm SWC.
(2) Under different water stress conditions, there was no significant difference between the leaf area index LAI and the crop coefficient. They were all at a high level (r = 0.45~0.60, P<0.05). However, under strong rainfall conditions, the LAI correlation coefficient is low (r = 0.04~0.27).

(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

1 绪论... 1

1.1 选题背景及意义... 1

1.2 国内外研究进展... 2

1.2.1 作物系数估算方法研究... 2

1.2.2 基于光谱遥感技术作物系数估算研究... 3

1.2.3 农情信息无人机遥感监测技术研究... 5

1.2.4 存在的问题... 6

1.3 研究内容与方法... 6

1.3.1 研究内容... 6

1.3.2 研究方法... 7

2 研究区域与方法... 9

2.1 研究区域... 9

2.1.1 试验区概况... 9

2.1.2 试验方案设计... 11

2.2 无人机多光谱数据采集... 13

2.2.1 无人机遥感平台及传感器... 13

2.2.2 无人机航线轨迹规划及遥感影像数据获取方法... 14

2.2.3 植被指数提取... 15

2.3 地面数据采集... 16

2.3.1 作物生长参数数据采集... 16

2.3.2 土壤水分数据采集... 17

2.3.3 标准气象站数据采集... 18

2.4 作物系数计算及反演模型建立方法... 19

2.4.1 参考作物蒸散量计算方法... 19

2.4.2 FAO双作物系数法... 22

2.4.2.1 基础作物系数 Kcb的修正及确定... 22

2.4.2.2 水分胁迫系数Ks的确定... 23

2.4.2.3 土壤蒸发系数 Ke的确定... 24

2.4.3 作物系数反演模型建立方法... 26

2.4.3.1 逐步回归分析... 26

2.4.3.2 模型模拟精度评价... 26

3 地面数据预处理... 28

3.1 气象数据预处理... 28

3.2 玉米生长参数数据预处理... 32

3.3 土壤水分数据预处理... 34

3.4 本章小结... 37

4 地面数据与作物系数的关系... 39

4.1 作物系数计算... 39

4.2 土壤水分与作物系数的关系... 42

4.3 玉米生长指标与作物系数的关系... 44

4.4 本章小结... 45

5 遥感影像数据与作物系数的关系... 47

5.1 光谱数据采集结果... 47

5.2 植被指数与作物系数的关系... 50

5.3 本章小结... 51

6 作物系数机地协同估算模型的建立与精度评价... 52

6.1 作物系数机地协同估算模型的建立... 52

6.2 作物系数机地协同估算模型精度评价... 53

6.3 模型计算蒸散量与实际蒸散量的对比... 55

6.4 本章小结... 58

7 结论与展望... 59

7.1 结论... 59

7.2 创新点... 60

7.3 展望... 60

参考文献... 61

... 68

作者简历及攻读学位期间发表的学术论文与研究成果 70

Pages70
Language中文
Document Type学位论文
Identifierhttp://ir.iswc.ac.cn/handle/361005/8795
Collection水保所2018--2019届毕业生论文
Recommended Citation
GB/T 7714
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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