无人机观测时间对玉米冠层叶绿素密度估算的影响

doi:10.3969/j.issn.1004-1524.20230284

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (1): 18-31.DOI: 10.3969/j.issn.1004-1524.20230284

无人机观测时间对玉米冠层叶绿素密度估算的影响

周丽丽¹^,²^,³(), 冯海宽⁴, 聂臣巍²^,³, 许晓斌⁵, 刘媛¹^,²^,³, 孟麟²^,³, 薛贝贝², 明博², 梁齐云¹, 苏涛¹^,^*(), 金秀良²^,³^,^*()

1.安徽理工大学空间信息与测绘工程学院,安徽淮南 232001
2.中国农业科学院作物科学研究所,北京 100081
3.中国农业科学院国家南繁研究院,海南三亚 572024
4.国家农业信息化工程技术研究中心,北京 100097
5.武汉大学测绘与遥感信息工程国家重点实验室,湖北武汉 430079

收稿日期:2023-03-06 出版日期:2024-01-25 发布日期:2024-02-18
作者简介:金秀良,E-mail:jinxiuliang@caas.cn;
周丽丽(1997—),女,安徽合肥人,硕士研究生,主要从事农业遥感方面的研究。E-mail:481398020@qq.com
通讯作者: * 苏涛,E-mail:st7162003@163.com
基金资助:
国家自然科学基金(42071426);国家自然科学基金(51922072);国家自然科学基金(51779161);国家自然科学基金(51009101);“十四五”国家重点研发计划(2021YFD1201602);中国农业科学院中央公益事业单位基础研究基金(Y2020YJ07);中国农业科学院中央公益事业单位基础研究基金(Y2022XK22);中国农业科学院科技创新工程—海南省崖州湾种子实验室(JBGS+B21HJ0221);中国农业科学院南繁研究院南繁专项(YJTC01);中国农业科学院南繁研究院南繁专项(YBXM01);水资源与水电工程科学国家重点实验室开放研究基金(2021NSG01);新疆农业科学院科技创新重点培育专项(xjkcpy-2020003)

Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize

ZHOU Lili¹^,²^,³(), FENG Haikuan⁴, NIE Chenwei²^,³, XU Xiaobin⁵, LIU Yuan¹^,²^,³, MENG Lin²^,³, XUE Beibei², MING Bo², LIANG Qiyun¹, SU Tao¹^,^*(), JIN Xiuliang²^,³^,^*()

1. School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, Anhui, China
2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, Hainan, China
4. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097,China
5. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Received:2023-03-06 Online:2024-01-25 Published:2024-02-18

摘要/Abstract

摘要：

为探讨不同时间获取的无人机多光谱数据对玉米冠层叶绿素密度(canopy chlorophyll density, CCD)估算的影响,分别在玉米抽雄吐丝期、籽粒建成期、乳熟期和蜡熟期选择同一天的10:00—10:59、11:00—11:59、13:00—13:59和14:00—14:59进行无人机多光谱观测试验,并结合PROSAIL模型模拟结果与实测CCD数据,分析一天中不同时刻典型植被指数的变化规律及CCD估算结果的差异。结果表明:在同一天中,无人机玉米冠层反射率和与实测CCD相关性较好的植被指数值均随时间变化,近红外波段的反射率变化最明显,越接近12:00,实测的植被指数值越低,而在一天的不同时间PROSAIL模型模拟的植被指数值几乎没有差异。在同一天,基于不同观测时间获取的同一植被指数与实测CCD的相关性存在较大差异,且不同生育时期和不同指数间的差异不一致;而模拟得到的同一植被指数与CCD的相关性在同一天不同时间的差异不明显。在不同生育时期,基于不同观测时间无人机数据构建的CCD估算模型均可以取得较好的精度,但不同观测时间的估算结果存在差异,决定系数最低的为0.53,最高的为0.80。这些结果表明,在传统的光谱数据获取时间范围内(10:00—14:00),无人机影像获取时间仍对玉米CCD估算有影响,越接近12:00,估算精度越高。研究结果可为后续作物的CCD精准估算提供基础支撑。

关键词: 冠层叶绿素密度, 观测时间, 机器学习, PROSAIL模型, 玉米

Abstract:

In order to explore the effect of multispectral data of unmanned aerial vehicles (UAV) acquired at different time on the estimation of canopy chlorophyll density (CCD) of maize, the UAV multispectral observation experiment was conducted at 10:00—10:59, 11:00—11:59, 13:00—13:59, 14:00—14:59 at the tasseling silking stage, blister stage, milk stage and dough stage, respectively. The PROSAIL model simulation results and the measured CCD data were combined to analyze the changes of the typical vegetation indexes at different time of the day and the difference between the CCD estimation results. The results showed that both the canopy reflectance and the value of vegetation indexes strongly correlated with the measured CCD changed with the time on the same day. The most obvious change of reflectance was found in the near-infrared band on the same day. The vegetation index value decreased as it approached 12:00 in the noon, whereas the value of vegetation indexes simulated by PROSAIL model at different time of the day showed little difference. The correlation between the same vegetation index obtained at different observation time and the measured CCD was different on the same day, and the difference between growth stages and indexes was not consistent; while the correlation between the same simulated vegetation index and CCD was not obvious at different time on the same day. CCD estimation models based on UAV data at different observation time could achieve good accuracy at different growth stages, but the estimation results at different observation time were different, with the lowest determination coefficient (R²) of 0.53 and the highest R² of 0.80. These results showed that the traditional spectral data acquisition time range (10:00—14:00) had an impact on CCD estimation of maize, and the closer to the noon (12:00), the higher the estimation accuracy. These results provided basic support for the accurate CCD estimation of crops.

Key words: canopy chlorophyll density, observation time, machine learning, PROSAIL model, maize

中图分类号:

S127
TP72

周丽丽, 冯海宽, 聂臣巍, 许晓斌, 刘媛, 孟麟, 薛贝贝, 明博, 梁齐云, 苏涛, 金秀良. 无人机观测时间对玉米冠层叶绿素密度估算的影响[J]. 浙江农业学报, 2024, 36(1): 18-31.

ZHOU Lili, FENG Haikuan, NIE Chenwei, XU Xiaobin, LIU Yuan, MENG Lin, XUE Beibei, MING Bo, LIANG Qiyun, SU Tao, JIN Xiuliang. Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize[J]. Acta Agriculturae Zhejiangensis, 2024, 36(1): 18-31.

图/表 11

图1 研究区的地理位置与试验设计图

Fig.1 Diagram of study area and experimental design

表1 施肥方案

Table 1 Fertilization scheme kg·hm-2

处理 Treatment	不同种植时间的肥料施用量Application rate of fertilizer at different time
处理 Treatment	50 d	60 d	70 d	80 d	90 d	100 d
N_D1	0	0	0	72.92	72.92	72.92
N_D2	0	0	72.92	72.92	72.92	72.92
N_D3	0	0	145.83	72.92	72.92	72.92
N_D4	0	72.92	0	72.92	72.92	72.92
N_D5	0	72.92	72.92	72.92	72.92	72.92
N_D6	0	72.92	145.83	72.92	72.92	72.92
N_D7	0	145.83	0	72.92	72.92	72.92
N_D8	0	145.83	72.92	72.92	72.92	72.92
N_D9	0	145.83	145.83	72.92	72.92	72.92
N_D10	72.92	0	0	72.92	72.92	72.92
N_D11	72.92	0	72.92	72.92	72.92	72.92
N_D12	72.92	0	145.83	72.92	72.92	72.92
N_D13	72.92	72.92	0	72.92	72.92	72.92
N_D14	72.92	72.92	72.92	72.92	72.92	72.92
N_D15	72.92	72.92	145.83	72.92	72.92	72.92
N_D16	72.92	145.83	0	72.92	72.92	72.92
N_D17	72.92	145.83	72.92	72.92	72.92	72.92
N_D18	72.92	145.83	145.83	72.92	72.92	72.92
N_D19	145.83	0	0	72.92	72.92	72.92
N_D20	145.83	0	72.92	72.92	72.92	72.92
N_D21	145.83	0	145.83	72.92	72.92	72.92
N_D22	145.83	72.92	0	72.92	72.92	72.92
N_D23	145.83	72.92	72.92	72.92	72.92	72.92
N_D24	145.83	72.92	145.83	72.92	72.92	72.92
N_D25	145.83	145.83	0	72.92	72.92	72.92
N_D26	145.83	145.83	72.92	72.92	72.92	72.92
N_D27	145.83	145.83	145.83	72.92	72.92	72.92

表2 PROSAIL模型中太阳天顶角和方位角参数的具体设置

Table 2 Specific settings of solar zenith angle and azimuth angle of PROSAIL model

生育时期 Growth stage	观测时间 Observation time	太阳天顶角 Solar zenith angle(SZA)/(°)	太阳方位角 Solar azimuth angle(SAA)/(°)
抽雄吐丝期Tasseling silking stage (VT/R1)	10:00—10:59(10AM)	35.59	125.46
	11:00—11:59(11AM)	28.16	148.67
	13:00—13:59(1PM)	28.18	211.31
	14:00—14:59(2PM)	35.62	234.50
籽粒建成期Blister stage (R2)	10:00—10:59(10AM)	38.17	128.70
	11:00—11:59(11AM)	31.16	151.15
	13:00—13:59(1PM)	31.18	208.83
	14:00—14:59(2PM)	38.21	231.25
乳熟期Milk stage (R3)	10:00—10:59(10AM)	40.76	131.52
	11:00—11:59(11AM)	34.11	153.18
	13:00—13:59(1PM)	34.14	206.80
	14:00—14:59(2PM)	40.80	228.43
蜡熟期Dough stage (R5)	10:00—10:59(10AM)	44.30	134.84
	11:00—11:59(11AM)	38.09	155.45
	13:00—13:59(1PM)	38.12	204.54
	14:00—14:59(2PM)	44.35	225.11

图2 不同观测时间下的波段反射率变化 10AM、11AM、1PM、2PM对应的时间分别为10:00—10:59、11:00—11:59、13:00—13:59、14:00—14:59。下同。图a~d分别对应于抽雄吐丝期(VT/R1)、籽粒建成期(R2)、乳熟期(R3)、蜡熟期(R5)。

Fig.2 Changes of waveband reflectance at different observation time 10AM, 11AM, 1PM, 2PM represent 10:00—10:59, 11:00—11:59, 13:00—13:59, 14:00—14:59, respectively. The same as below. Fig. a-d represent the tasseling silking stage (VT/R1), blister stage (R2), milk stage (R3), and dough stage (R5), respectively.

表3 植被指数与冠层叶绿素密度(CCD)的相关性

Table 3 Correlations within vegetatino indexes and canopy chlorophyll density (CCD)

植被指数 Vegetation index	各生育时期的相关系数Correlation coefficient at different growth stages
植被指数 Vegetation index	VT/R1	R2	R3	R5
MTCI	0.65^**	0.74^**	0.72^**	0.68^**
NDVI_rededge	0.70^**	0.76^**	0.75^**	0.75^**
CI_rededge	0.68^**	0.75^**	0.74^**	0.73^**
NDVI	0.36^**	0.61^**	0.64^**	0.66^**
GNDVI	0.72^**	0.67^**	0.77^**	0.76^**
TCARI/OSAVI	0.26^**	0.46^**	0.13^*	0.41^**
OSAVI	0.28^**	0.43^**	0.16^*	0.55^**
SIPI	0.07	0.02	0.09	0.34^**
EVI	0.23^**	0.31^**	0.09	0.47^**
DVI	0.24^**	0.31^**	0.09	0.44^**
TCARI	0.20^**	0.37^**	0.08	0.33^**
MCARI	0.20^**	0.37^**	0.08	0.33^**
PPR	0.11^*	0.31^**	0.13^*	0.37^**

图3 基于PROSAIL模型模拟的植被指数在不同观测时间下的变化 NDVI,归一化植被指数;GNDVI,绿色归一化植被指数;CIrededge,红边叶绿素指数;NDVIrededge,红边归一化植被指数;MTCI,MERIS陆地叶绿素指数。下同。

Fig.3 Changes of vegetation indexes simulated by PROSAIL model under different observation time NDVI, Normalized difference vegetation index; GNDVI, Green normalized difference vegetation index; CIrededge, Red edge chlorophyll index; NDVIrededge, Red edge normalized difference vegetation index; MTCI, MERIS terrestrial chlorophyll index. The same as below.

图4 基于无人机影像实测的植被指数在不同观测时间下的变化

Fig.4 Changes of measured vegetation indexes based on unmanned aerial vehicle images under different observation time

图5 基于PROSAIL模型模拟的植被指数与冠层叶绿素密度(CCD)的绝对相关系数(r) a~e分别表示的是NDVI、GNDVI、CIrededge、NDVIrededge、MTCI与CCD的相关性。图6同。

Fig.5 Absolute correlation coefficient (r) within the vegetation indexes simulated by PROSAIL model and canopy chlorophyll density (CCD) a-e represent the correlation within NDVI, GNDVI, CIrededge, NDVIrededge, MTCI and CCD, respectively. The same as in Fig.6.

图6 基于无人机影像实测的植被指数与冠层叶绿素密度(CCD)的绝对相关系数

Fig.6 Absolute correlation coefficient within the measured vegetation indexes based on unmanned aerial vehicle images and canopy chlorophyll density (CCD)

图7 基于PROSAIL模型模拟的植被指数对冠层叶绿素密度(CCD)估算的影响 RMSE,均方根误差;NRMSE,归一化均方根误差。下同。

Fig.7 Influence of the vegetation indexes simulated by PROSAIL model on canopy chlorophyll density (CCD) estimation RMSE, Root mean square error; NRMSE, Normalized root mean squared error. The same as below.

图8 基于无人机影像实测的植被指数对冠层叶绿素密度(CCD)估算的影响 R2,决定系数。

Fig.8 Influence of the measured vegetation indexes based on unmanned aerial vehicle images on canopy chlorophyll density (CCD) estimation R2,Coefficient of determination.

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	XU X B. Nitrogen quantitative estimation of maize using hyperspectral remote sensing based on radiation transfer model[D]. Qingdao: Shandong University of Science and Technology, 2019. (in Chinese with English abstract)

无人机观测时间对玉米冠层叶绿素密度估算的影响

Influence of unmanned aerial vehicle observation time on estimation of canopy chlorophyll density of maize

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