Smart Agriculture ›› 2021, Vol. 3 ›› Issue (1): 29-39.doi: 10.12133/j.smartag.2021.3.1.202102-SA004
收稿日期:
2021-01-28
修回日期:
2021-03-11
出版日期:
2021-03-30
发布日期:
2021-06-01
基金资助:
作者简介:
束美艳(1993-),女,博士研究生,研究方向为数字农业。E-mail:通讯作者:
王喜庆,马韫韬
E-mail:2448858578@qq.com;wangxq21@cau.edu.cn;yuntao.ma@cau.edu.cn
SHU Meiyan1(), CHEN Xiangyang2, WANG Xiqing2(
), MA Yuntao1(
)
Received:
2021-01-28
Revised:
2021-03-11
Online:
2021-03-30
Published:
2021-06-01
corresponding author:
Xiqing WANG,Yuntao MA
E-mail:2448858578@qq.com;wangxq21@cau.edu.cn;yuntao.ma@cau.edu.cn
摘要:
利用高光谱技术获取玉米农学参数信息,有助于提升玉米精准管理水平。本研究基于3个种植密度和5份玉米材料的田间试验,获取玉米大喇叭口期的地面ASD高光谱数据与无人机高光谱影像,分析不同种植密度下不同遗传材料的叶面积指数(LAI)和单株地上部生物量,构建基于全波段、敏感波段和植被指数的LAI和单株地上部生物量高光谱估算模型,比较分析两类高光谱数据在玉米表型性状参数上的监测能力。结果表明,野生型玉米材料的冠层光谱反射率在近红外波段随着种植密度的增大而增大;同一种植密度下的野生型玉米材料的光谱反射率在可见光和近红外波段均为最低。在可见光波段550 nm的波峰处,4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高4.52%~19.9%,在近红外波段870 nm的波峰处,4种转基因材料的光谱反射率比野生型玉米材料的光谱反射率提高23.64%~57.05%。基于21个高光谱植被指数构建的模型对LAI的估算效果最好,测试集决定系数R2为0.70,均方根误差RMSE为0.92,相对均方根误差rRMSE为15.94%。敏感波段反射率(839~893 nm和1336~1348 nm)对玉米单株地上部生物量估算效果最佳,测试集R2为0.71,RMSE为12.31 g,rRMSE为15.89%。综上,田间非成像高光谱和无人机成像高光谱在玉米LAI及生物量估算方面具有较好的一致性,能够快速有效地提取地块尺度玉米农学参数信息,本研究可为高光谱技术在小区尺度的精准农业管理应用提供参考。
中图分类号:
束美艳, 陈向阳, 王喜庆, 马韫韬. 基于高光谱数据的玉米叶面积指数和生物量评估[J]. 智慧农业(中英文), 2021, 3(1): 29-39.
SHU Meiyan, CHEN Xiangyang, WANG Xiqing, MA Yuntao. Estimation of Maize Leaf Area Index and Aboveground Biomass Based on Hyperspectral Data[J]. Smart Agriculture, 2021, 3(1): 29-39.
表 1
本研究中使用的高光谱植被指数
植被指数 | 计算公式 | 参考文献 |
---|---|---|
DDI | DDI= (R750-R720) - (R700-R670) (3) | [ |
GNDVI | GNDVI = (R750-R550)/(R750+R550) (4) | [ |
MCARI | MCARI = [(R750-R705)-0.2×(R750-R550)]×(R750/R705) (5) | [ |
MND705 | MND705 = (R750-R705)/(R750+R705-2×R445) (6) | [ |
MSR | MSR = (R750/R705-1)/sqrt(R750/R705+1) (7) | [ |
MSR705 | MSR705 = (R750-R445)/(R705-R445) (8) | [ |
MTCI | MTCI = (R750-R710)/(R710-R680) (9) | [ |
NDI [759,732] | NDI [759,732] = R759-R732 (10) | [ |
NDI [860,560] | NDI [860,560] = R860-R560 (11) | [ |
NDI [860,720] | NDI [860,720] = R860-R720 (12) | [ |
NDVI705 | NDVI705 = (R750-R705)/(R750+R705) (13) | [ |
NDVI780 | NDVI780 = (R780-R710)/(R780-R680) (14) | [ |
NDVI850 | NDVI850 = (R850-R710)/(R850-R680) (15) | [ |
NDVI760 | NDVI760 = (R760-R708)/(R760+R708) (16) | [ |
NDVI780 | NDVI780 = (R780-R550)/(R780+R550) (17) | [ |
NDVI800 | NDVI800 = (R800-R700)/(R800+R700) (18) | [ |
SRI [750,705] | SRI [750,705] = R750/R705 (19) | [ |
SRI [768,750] | SRI [768,750] = R768/R750 (20) | [ |
SRI [777,759] | SRI [777,759] = R777/R759 (21) | [ |
SRI [810,560] | SRI [810,560] = R810/R560 (22) | [ |
SRI [810,660] | SRI [810,660] = R810/R660 (23) | [ |
1 | 束美艳, 顾晓鹤, 孙林, 等. 倒伏胁迫下的玉米冠层结构特征变化与光谱响应解析[J]. 光谱学与光谱分析, 2019, 39(11): 3553-3559. |
SHU M, GU X, SUN L, et al. Structural characteristics change and spectral response analysis of maize canopy under lodging stress[J]. Spectroscopy and Spectral Analysis, 2019, 39(11): 3553-3559. | |
2 | 周龙飞, 张云鹤, 成枢, 等. 不同生育期倒伏胁迫下玉米叶面积指数高光谱响应解析[J]. 遥感技术与应用, 2019, 34(4): 766-774. |
ZHOU L, ZHANG Y, CHENG S, et al. Analysis of hyperspectral response of maize leaf area index under lodging stress under different growth stages[J]. Remote Sensing Technology and Application, 2019, 34(4): 766-774. | |
3 | 潘海珠, 陈仲新. 无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用[J]. 中国农业资源与区划, 2018, 9(3): 32-37 |
PAN H, CHEN Z. Application of UVA hyperspectral remote sensing in winter wheat leaf area index inversion[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2018, 9(3): 32-37 | |
4 | 束美艳, 顾晓鹤, 孙林, 等. 基于新型植被指数的冬小麦LAI高光谱反演[J]. 中国农业科学, 2018, 51(18): 3486-3496. |
SHU M, GU X, SUN L, et al. High spectral inversion of winter wheat LAI based on new vegetation index[J]. Scientia Agricultura Sinica, 2018, 51(18): 3486-3496. | |
5 | BENDING J, KANG YU, AASEN H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observations and Geoinformation, 2015, 39: 79-87. |
6 | 李天驰, 冯海宽, 朱贝贝, 等. 基于无人机高光谱和数码影像数据的冬小麦生物量反演[J]. 现代农业科技, 2020(20): 1-5. |
LI T, FENG H, ZHU B, et al. Winter wheat biomass inversion based on UAV hyperspectral and digital image data[J]. Modern Agricultural Science and Technology, 2020(20): 1-5. | |
7 | BODO M, URS S. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat[J]. Agronomy Journal, 2010, 102(2): 499-506. |
8 | 刘杨, 冯海宽, 孙乾, 等. 基于无人机高光谱分数阶微分的马铃薯地上生物量估算[J]. 农业机械学报, 2020, 51(12): 202-211. |
LIU Y, FENG H, SUN Q, et al. Estimation of potato above-ground biomass based on fractional differential of UAV hyperspectral[J]. Transactions of the CSAM, 2020, 51(12): 202-211. | |
9 | WU C, NIU Z, TANG Q, et al. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[J]. Agricultural and Forest Meteorology, 2008, 148(8): 1230-1241. |
10 | CROFT H, J M, ZHANG C. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures[J]. Ecological Complexity, 2014, 17: 119-130. |
11 | 程雪, 贺炳彦, 黄耀欢, 等. 基于无人机高光谱数据的玉米叶面积指数估算[J]. 遥感技术与应用, 2019, 34(4): 775-784. |
CHENG X, HE B, HUANG Y, et al. Estimation of corn leaf area index based on UAV hyperspectral image[J]. Remote Sensing Technology and Application, 2019, 34(4): 775-784. | |
12 | 常潇月, 常庆瑞, 王晓凡, 等. 基于无人机高光谱影像玉米叶绿素含量估算[J]. 干旱地区农业研究, 2019, 37(1): 66-73. |
CHANG X, CHANG Q, WANG X, et al. Estimation of maize leaf chlorophyll contents based on UAV hyperspectral drone image[J]. Agricultural Research in the Arid Areas, 2019, 37(1): 66-73. | |
13 | ZHOU Y, JIANG M. Comparison of inversion method of maize leaf area index based on UAV hyperspectral remote sensing[J]. Multimedia Tools and Applications, 2020(79): 16385-16401. |
14 | 田明璐, 班松涛, 常庆瑞, 等. 基于低空无人机成像光谱仪影像估算棉花叶面积指数[J]. 农业工程学报, 2016, 32(21): 102-108. |
TIAN M, BAN S, CHANG Q, et al. Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index[J]. Transactions of the CSAE, 2016, 32(21): 102-108. | |
15 | 田明璐, 班松涛, 常庆瑞, 等. 基于无人机成像光谱仪数据的棉花叶绿素含量反演[J]. 农业机械学报, 2016, 47(11): 285-293. |
TIAN M, BAN S, CHANG Q, et al. Estimation of SPAD value of cotton leaf using hyperspectral images from UAV-based imaging spectroradiometer[J]. Transactions of the CSAM, 2016, 47(11): 285-293. | |
16 | 陶惠林, 冯海宽, 杨贵军, 等. 基于无人机成像高光谱影像的冬小麦LAI估测[J]. 农业机械学报, 2020, 51(1): 176-187. |
TAO H, FENG H, YANG G, et al. Leaf area index estimation of winter wheat based on UAV imaging hyperspectral imagery[J]. Transactions of the CSAM, 2020, 51(1): 176-187. | |
17 | 陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱遥感的冬小麦株高和叶面积指数估算[J]. 农业机械学报, 2020, 51(12): 193-201. |
TAO H, XU L, FENG H, et al. Estimation of plant height and leaf area index of winter wheat based on UAV hyperspectral remote sensing[J]. Transactions of the CSAM, 2020, 51(12): 193-201. | |
18 | 陶惠林, 冯海宽, 徐良骥, 等. 基于无人机高光谱遥感数据的冬小麦生物量估算[J]. 江苏农业学报, 2020, 36(5): 1154-1162. |
TAO H, FENG H, XU L, et al. Winter wheat biomass estimation based on hyperspectral remote sensing data of unmanned aerial vehicle(UAV)[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(5): 1154-1162. | |
19 | 陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱长势指标的冬小麦长势监测[J]. 农业机械学报, 2020, 51(2): 180-191. |
TAO H, XU L, FENG H, et al. Monitoring of winter wheat growth based on UAV hyperspectral growth index[J]. Transactions of the CSAM, 2020, 51(2): 180-191. | |
20 | 陶惠林, 徐良骥, 冯海宽, 等. 基于无人机高光谱遥感数据的冬小麦产量估算[J]. 农业机械学报, 2020, 51(7): 146-155. |
TAO H, XU L, FENG H, et al. Winter wheat yield estimation based on UAV hyperspectral remote sensing data[J]. Transactions of the CSAM, 2020, 51(7): 146-155. | |
21 | 陶惠林, 冯海宽, 杨贵军, 等. 基于无人机数码影像和高光谱数据的冬小麦产量估算对比[J]. 农业工程学报, 2019, 35(23): 111-118. |
TAO H, FENG H, YANG G, et al. Comparison of winter wheat yields estimated with UAV digital image and hyperspectral data[J]. Transactions of the CASE, 2019, 35(23): 111-118. | |
22 | LI Z, LI Z, FAIRBAIRN D, et al. Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral[J]. Computers and Electronics in Agriculture, 2019, 162: 174-182. |
23 | 秦占飞, 常庆瑞, 谢宝妮, 等. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77-85. |
QIN Z, CHANG Q, XIE B, et al. Rice leaf nitrogen content estimation based on hyperspectral imagery of UAV in Yellow River diversion irrigation district[J]. Transactions of the CSAE, 2016, 32(23): 77-85. | |
24 | DRISS H, JOHN R. MILLER N,et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture[J]. Remote Sensing of Environment, 2002, 81(2): 416-426. |
25 | ZARCO-TFJADA P, MILLER J, MORALES A, et al. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops[J]. Remote Sensing of Environment, 2004, 90(4): 463-476. |
26 | LE G, MAIRE C, FRANCOIS E, et al. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements[J]. Remote Sensing of Environment, 2003, 89(1): 1-28. |
27 | ANATOLY A, GITRLSION Y, KAUFMAN M, et al. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298. |
28 | CWU C, NIU Z, TANG Q, et al. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation[J]. Agricultural and Forest Meteorology, 2008, 148(8): 1230-1241. |
29 | SIMS D, GANMON J. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81(2): 337-354. |
30 | DASH J, CURRAN P. The MERIS terrestrial chlorophyll index[J]. International Journal of Remote Sensing, 2004, 25(23): 5403-5413. |
31 | HANSEN P, SCHJOERRING J. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003, 86(4): 542-553. |
32 | CHU X, GUO Y, HE J, et al. Comparison of different hyperspectral vegetation indices for estimating canopy leaf nitrogen accumulation in rice[J]. Agronomy Journal, 2014, 106(5): 1911-1920. |
33 | FAVA F, COLOMBO R, BOCCHI S, et al. Identification of hyperspectral vegetation indices for Mediterranean pasture characterization[J]. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(4): 233-243. |
34 | BISUN D. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in eucalyptus leaves[J]. Remote Sensing of Environment, 1998, 66(2): 111-121. |
35 | STEDDOM K, HEIDEL G, JONES D, et al. Remote detection of rhizomania in sugar beets[J]. Phytopathology, 2003, 93(6): 720-726. |
36 | GITELSON A, MERZLYAK M N. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves[J]. Journal of Photochemistry and Photobiology B Biology, 1994, 22(3): 247-252. |
37 | MAIRAJ D, JIN M, SADEED H, et al. Estimation of dynamic canopy variables using hyperspectral derived vegetation indices under varying N rates at diverse phenological stages of rice[J]. Frontiers in Plant Science, 2019, 9: ID 1883. |
38 | 薛利红, 曹卫星, 罗卫红, 等.小麦叶片氮素状况与光谱特性的相关性研究[J]. 植物生态学报, 2004, 28(2): 172-177. |
XUE L, CAO W, LUO W, et al. Correlation between leaf nitrogen status and canopy spectral characteristics in wheat[J]. Acta Phytoecologica Sinica, 2004, 28(2): 172-177. | |
39 | QUENOUILLE M. Approximate tests of correlation in time-series[J]. Journal of the Royal Statistical Society, Series B (Methodological), 1949, 11(1): 68-84. |
40 | MENG B, SKIDMORE A K, SCHLERF M, et al. Predicting foliar biochemistry of tea (Camellia sinensis) using reflectance spectra measured at powder, leaf and canopy levels[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 78: 148-156. |
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