基于无人机多光谱遥感的夏玉米叶面积指数估算方法

doi:10.12133/j.smartag.2020.2.3.202006-SA001

摘要/Abstract

摘要：

无人机多光谱遥感技术可以快速、无损地监测农作物叶面积指数（LAI）。为研究水分胁迫条件下，利用无人机多光谱植被指数估算夏玉米LAI的可行性，本研究基于无人机多光谱遥感系统，结合同时期实地采集的夏玉米LAI，选择5种植被指数，包括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）、绿度归一化植被指数（GNDVI）和抗大气指数（VARI），作为模型输入参数，使用随机森林回归算法建立全生育期不同灌溉条件下大田玉米冠层植被指数与LAI之间的关系模型，并与一元线性回归和多元线性回归算法建立的模型进行对比分析。结果表明，在充分灌溉条件下，植被指数的多元线性回归模型可以较好地估算LAI（R²= 0.83）；在水分胁迫条件下，植被指数的随机森林回归模型可以较好地估算LAI（R²= 0.74~0.87），水分胁迫因素对该模型影响较小，且NDVI和VARI对估算LAI的贡献最大。上述结果表明基于无人机多光谱遥感技术，使用随机森林回归算法估算多种灌溉条件下的夏玉米LAI是可行的。该研究为实现快速、准确地监测全生育期不同灌溉条件下的大田夏玉米LAI提供了技术和方法支持。

关键词: 无人机, 叶面积指数（LAI）, 植被指数, 多光谱遥感, 水分胁迫, 随机森林回归

Abstract:

Maize is an important food crop in China. In order to quickly and non-destructively estimate summer maize leaf area index (LAI) under different water stress conditions, in this study, maize samples with multiple irrigation treatments throughout the growth period were used for modeling analysis. Then, based on the unmanned aerial vehicle (UAV) multi-spectral remote sensing technology, combined with the summer maize LAI collected in the field during the same period, five kinds of vegetation indices, including the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI) and visible atmospherically resistant index (VARI) were selected in this research as model input parameters, and random forest regression algorithm was used to establish the relationship between the field maize canopy vegetation indices and LAI under different irrigation conditions during the entire growth period. The accuracies of the model were compared with that of the model established by the university linear regression and multiple linear regression algorithms. The results showed that under sufficient irrigation condition, the vegetation index using multiple linear regression model could well (R² = 0.83, RMSE = 0.05) estimate LAI; under water stress conditions, the vegetation index using random forest regression model could well estimate LAI (R² = 0.74~0.87, RMSE = 0.02~0.10), water stress factors had little effect on the random forest regression model, and NDVI and VARI contributed the LAI estimation model better. The spatial distribution map of LAI was generated based on the random forest regression algorithm. The above results showed that it was feasible to use the random forest regression algorithm to estimate the summer maize LAI under various irrigation conditions based on the UAV multi-spectral remote sensing technology. The results indicates that the model established has a good applicability. This research can provide technical and method support for the rapid and accurate monitoring of field summer maize LAI under different irrigation conditions during the entire growth period.

Key words: UAV, leaf area index, vegetation index, multispectral, water stress, random forest regression

中图分类号:

S127

邵国敏, 王亚杰, 韩文霆. 基于无人机多光谱遥感的夏玉米叶面积指数估算方法[J]. 智慧农业（中英文）, 2020, 2(3): 118-128.

SHAO Guomin, WANG Yajie, HAN Wenting. Estimation Method of Leaf Area Index for Summer Maize Using UAV-Based Multispectral Remote Sensing[J]. Smart Agriculture, 2020, 2(3): 118-128.

参考文献

1	QIAO K, ZHU W, XIE Z, et al. Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases[J]. Remote Sensing, 2019, 11: ID 689.
2	刘珺, 庞鑫, 李彦荣, 等. 夏玉米叶面积指数遥感反演研究[J]. 农业机械学报, 2016, 47(9): 309-317.
2	LIU J, PANG X, LI Y, et al. Inversion study on leaf area index of summer maize using remote sensing[J]. Transactions of the CSAM, 2016, 47(9): 309-317.
3	ALEXANDRIDIS T K, OVAKOGLOU G, CLEVERS J G P W. Relationship between MODIS EVI and LAI across time and space[J]. Geocarto International, 2019: 1-14.
4	阎广建, 胡容海, 罗京辉, 等. 叶面积指数间接测量方法[J]. 遥感学报, 2016, 20(5): 958-978.
4	YAN G, HU R, LUO J, et al. Review of indirect methods for leaf area index measurement[J]. Journal of Remote Sensing, 2016, 20(5): 958-978.
5	YAN G, HU R, LUO J, et al. Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives[J]. Agricultural and Forest Meteorology, 2019, 265: 390-411.
6	JIN X, ZARCO-TEJADA P, SCHMIDHALTER U, et al. High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms[J]. IEEE Geoscience and Remote Sensing Magazine, 2020. doi: 10.1109/MGRS.2020.2998816.
7	PASQUALOTTO N, DELEGIDO J, SVAN WITTENBERGHE, et al. Multi-crop green LAI estimation with a new simple Sentinel-2 LAI Index (SeLI)[J]. Sensors, 2019, 19(4): ID 904.
8	ZHANG Y, YANG J, LIU X, et al. Estimation of multi-species leaf area index based on Chinese GF-1 satellite data using look-up table and Gaussian process regression methods[J]. Sensors, 2020, 20(9): ID 2460.
9	OVAKOGLOU G, ALEXANDRIDIS T K, CLEVERS J G, et al. Spatial enhancement of modis leaf area index using regression analysis with landsat vegetation index[C]// IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium. Piscataway, New York, USA: IEEE, 2018.
10	AMANI M, MOBASHERI M R. A parametric method for estimation of leaf area index using landsat ETM+ data[J]. GIence & Remote Sensing, 2015, 52(4): 478-497.
11	LI X, DU H, MAO F, et al. Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms[J]. Agricultural and Forest Meteorology, 2018, 256: 445-457.
12	陈健, 王文君, 盛世杰, 等. 基于机载MASTER数据的果园叶面积指数遥感反演[J]. 国土资源遥感, 2015, 27(2): 69-74.
12	CHEN J, WANG W, SHENG S, et al. Leaf area index retrieval of orchards based on airborne MASTER data[J]. Remote Sensing for Land & Resources, 2015, 27(2): 69-74.
13	DUVEILLER G, DEFOURNY P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing[J]. Remote Sensing of Environment, 2010, 114(11): 2637-2650.
14	梁栋, 管青松, 黄文江, 等. 基于支持向量机回归的冬小麦叶面积指数遥感反演[J]. 农业工程学报, 2013, 29(7): 117-123.
14	LIANG D, GUAN Q, HUANG W, et al. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat[J]. Transactions of the CSAE, 2013, 29(7): 117-123.
15	刘洋, 刘荣高, 陈镜明, 等. 叶面积指数遥感反演研究进展与展望[J]. 地球信息科学学报, 2013, 15(5): 734-743.
15	LIU Y, LIU R, CHEN J, et al. Current status and perspectives of leaf area index retrieval from optical remote sensing data[J]. Journal of Geo-Information Science, 2013, 15(5): 734-743.
16	LIANG L, HUANG T, DI L, et al. Influence of different bandwidths on LAI estimation using vegetation indices[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020(99): ID 1.
17	高林, 杨贵军, 王宝山, 等. 基于无人机遥感影像的大豆叶面积指数反演研究[J]. 中国生态农业学报, 2015, 23(7): 868-876.
17	GAO L, YANG G, WANG B, et al. Soybean leaf area index retrieval with UAV (unmanned aerial vehicle) remote sensing imagery[J]. Chinese Journal of Eco-Agriculture, 2015, 23(7): 868-876.
18	褚洪亮, 肖青, 柏军华, 等. 基于无人机遥感的叶面积指数反演[J]. 遥感技术与应用, 2017, 32(1): 140-148.
18	CHU H, XIAO Q, BO J, et al. The retrieval of leaf area index based on remote sensing by unmanned aerial vehicle[J]. Remote Sensing Technology and Application, 2017, 32(1): 140-148.
19	张宏鸣, 刘雯, 韩文霆, 等. 基于梯度提升树算法的夏玉米叶面积指数反演[J]. 农业机械学报, 2019, 50(5): 251-259.
19	ZHANG H, LIU W, HAN W, et al. Inversion of summer maize leaf area index based on Gradient boosting decision tree algorithm[J]. Transactions of the CSAM, 2019, 50(5): 251-259.
20	XIE Q, HUANG W, LIANG D, et al. Leaf area index estimation using vegetation indices derived from airborne hyperspectral images in winter wheat[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3586-3594.
21	HOUBORG R, MCCABE M F. A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning[J]. Isprs Journal of Photogrammetry & Remote Sensing, 2017, 135: 173-188.
22	韩文霆, 邵国敏, 马代健, 等. 大田玉米作物系数无人机多光谱遥感估算方法[J]. 农业机械学报, 2018, 49(7): 134-143.
22	HAN W, SHAO G, MA D, et al. Estimating method of crop coefficient of maize based on UAV multispectral remote sensing[J]. Transactions of the CSAM, 2018, 49(7): 134-143.
23	BANNARI A, MORIN D, BONN F, et al. A review of vegetation indices[J]. Remote Sensing Reviews, 1995, 13(1-2): 95-120.
24	HUETE A R. A soil-adjusted vegetation index (SAVI)[J]. Remote sensing of Environment, 1988, 25(3): 295-309.
25	WANG Z, LIU C, HUETE A. From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index research[Z]. 2003, 23: 979-987.
26	WANG F, HUANG J, et al. New vegetation index and its application in estimating leaf area index of rice[J]. Rice Science, 2007, 14(3): 195-203.
27	SCHNEIDER P, ROBERTS D A, KYRIAKIDIS P C. A VARI-based relative greenness from MODIS data for computing the fire potential index[J]. Remote Sensing of Environment, 2008, 112(3): 1151-1167.
28	高林, 杨贵军, 于海洋, 等. 基于无人机高光谱遥感的冬小麦叶面积指数反演[J]. 农业工程学报, 2016, 32(22): 113-120.
28	GAO L, YANG G, YU H, et al. Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing[J]. Transactions of the CSAE, 2016, 32(22): 113-120.
29	QIAO K, ZHU W, XIE Z. Application conditions and impact factors for various vegetation indices in constructing the LAI seasonal trajectory over different vegetation types[J]. Ecological Indicators, 2020, 112: ID 106153.
30	谢巧云, 黄文江, 蔡淑红, 等. 冬小麦叶面积指数遥感反演方法比较研究[J]. 光谱学与光谱分析, 2014, 34(5): 1352-1356.
30	XIE Q, HUANG W, CAI S, et al. Comparative study on remote sensing invertion methods for estimating winter wheat leaf area index[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1352-1356.
31	林卉, 梁亮, 张连蓬, 等. 基于支持向量机回归算法的小麦叶面积指数高光谱遥感反演[J]. 农业工程学报, 2013, 29(11): 139-146.
31	LIN H, LIANG L, ZHANG L, et al. Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm[J]. Transactions of the CSAE, 2013, 29(11): 139-146.

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