Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (14): 2965-2976.doi: 10.3864/j.issn.0578-1752.2021.14.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Assessment of Terrestrial Laser Scanning and Hyperspectral Remote Sensing for the Estimation of Rice Grain Yield

LI PengLei1(),ZHANG Xiao1,WANG WenHui1,ZHENG HengBiao1,YAO Xia1,2,ZHU Yan1,CAO WeiXing1,CHENG Tao1,2()   

  1. 1Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture (NETCIA) /Jiangsu Key Laboratory for Information Agriculture/Key Laboratory of Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Smart Agriculture, Ministry of Education, Nanjing 210095
    2Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095
  • Received:2020-09-01 Accepted:2020-11-20 Online:2021-07-16 Published:2021-07-26
  • Contact: Tao CHENG E-mail:2017101172@njau.edu.cn;tcheng@njau.edu.cn

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Abstract:

【Background】Non-destructive and accurate estimation of crop biomass and yield is crucial for the quantitative diagnosis of growth status and planning of national food policies. Hyperspectral and Terrestrial Laser Scanning (TLS) remote sensing provide convenient and effective ways to monitor crop condition.【Objective】The aim of this study was to examine the feasibility of developing models with various independent datasets to build a universal yield monitoring model. The expected results can provide theoretical and technical support for rice growth monitoring and scientific guidance of precision agriculture.【Method】Field plot experiments were conducted in 2016, 2017 and 2018 and involved different study sites, nitrogen (N) rates, planting techniques and rice varieties. Linear regression (LR) and random forest (RF) were evaluated in estimating yield with TLS and spectral data collected since the heading stage, and the feasibility of developing models with various independent datasets was examined to build a universal yield monitoring model.【Result】 The results showed that TLS models exhibited higher estimation accuracies for yield in the heading stage, early-filling stage and late-filling stage (R2: 0.64-0.69) than hyperspectral models (R2: 0.20-0.58). Compared to RF, LR modeling yielded significantly higher validation accuracies for growth stages after heading. While the predictive model was transferred to other datasets, the validation accuracies from the same site (RRMSE: 11.37%-12.41%) were higher than those from a different site (RRMSE: 16.69%-17.85%).【Conclusion】The results suggested that TLS was a promising technique to monitor yield at post-heading stages with high accuracy and to overcome the saturation of canopy reflectance signals encountered in optical remote sensing.

Key words: rice, yield, LiDAR, hyperspectral, regression model

Fig. 1

Geographic location of the experimental sites"

Fig. 2

The design of experimental plots and distribution of terrestrial laser scanning positions of Rugao in 2016 N rates: N0 (0), N1 (100 kg·hm-2), N2 (200 kg·hm-2); Varieties: V1 (Wuyunjing 24), V2 (Y Liangyou 1) "

Fig. 3

The experimental plots and distribution of terrestrial laser scanning positions of Xinghua in 2017 N rates: N0 (0), N1 (135 kg·hm-2), N2 (270 kg·hm-2), N3 (405 kg·hm-2); Varieties: V1 (Nanjing 9108), V2 (Yongyou 2640); Planting techniques: P1 (Direct seeding), P2 (Tray seeding transplanting), P3 (Blanket seeding transplanting) "

Table 1

Vegetation indices for estimating rice yield"

指数
Vegetation index 		计算公式
Equation 		文献
Reference
差值Deviation
DVI [1200,680] R1200-R680 [9]
DVI [1200,440] R1200-R440 [9]
DVI [800,550] R800-R550 [9]
比值Ratio
SR [609,518] (R609/R518)-1 [24]
SR [1971,2018] (R1971/R2018)-1 [25]
SR [750,673] R750/R673 [26]
归一化Normalization
NDVI [1200,550] (R1200-R550)/(R1200+R550) [27]
NDVI[800,680] (R800-R680)/(R800+R680) [28]
NDVI [608,518] (R609-R518)/(R609+R518) [29]

Table 2

Summary of height metrics derived from the canopy height model and their descriptions"

结构参数 Structural parameter 描述 Description
Height mean ( Hmean) 高度平均值 Mean of height
Height min (Hmin) 高度最小值 Minimum of height
Height max (Hmax) 高度最大值 Maximum of height
Height standard deviation (Hstd) 高度标准偏差 Standard deviation of height
Height coefficient of variation (Hcov) 高度变异系数 Variable coefficient of height
Height kurtosis (Hk) 高度峰度 Kurtosis of height
Height skewness (Hs) 高度偏度 Skewness of height
Height percentile (H1st, H5th, H10th, H25th, H50th, H75th, H95th, H99th) 高度1st, 5th, 10th, 25th, 50th, 75th, 95th, 和99th 百分位
Percent of 1st, 5th, 10th, 25th, 50th, 75th, 95th, and 99th height

Table 3

Description of datasets"

数据集
Dataset 		年份
Year 		试验地点
Site 		样本数
Number of samples 		品种
Variety 		播栽方式
Planting technique
训练数据集
Training dataset 		2017 兴化Xinghua 72 南粳9108 & 甬优2640
Nanjing 9108 & Yongyou 2640 		钵苗移栽Tray seeding transplanting、
毯苗移栽Blanket seeding transplanting、
直播Direct seeding
验证数据集1
Validation dataset 1 		2016 如皋Rugao 36 武运粳24 & Y两优1号
Wuyunjing 24 & Y Liangyou 1 		旱育秧人工移栽
Dried-seedling manual transplanting
验证数据集2
Validation dataset 2 		2018 兴化Xinghua 48 南粳9108 & 甬优2640
Nanjing 9108 & Yongyou 2640 		钵苗移栽Tray seedling transplanting、
毯苗移栽Blanket seeding transplanting

Table 4

Coefficient of determination (R2) values for the relationships between rice yield and published vegetation indices "

植被指数
Vegetation index 		抽穗期
Heading 		灌浆前期
Early filling 		灌浆后期
Late filling
R1200-R680 0.12* 0.41** 0.23**
R1200-R440 0.10* 0.46** 0.24**
R800-R550 0.17** 0.41** 0.52**
(R609/R518）-1 0.20** ns 0.01
R750/R673 0.16** 0.29** 0.27**
(R1971/R2018）-1 0.03 0.04 0.02
(R1200-R550)/(R1200+R550) 0.17** 0.31** 0.28**
(R800-R680)/(R800+R680) 0.12** 0.32** 0.23**
(R609-R518)/(R609+R518) 0.16** ns 0.01

Table 5

Coefficient of determination (R2) values for the relationships between wavelet features and yield in rice "

抽穗期
Heading 		灌浆前期
Early filling 		灌浆后期
Late filing
最优小波特征
Optimal wavelet feature 		WF730,3 WF1200,3 WF1185,3
R2 0.34** 0.58** 0.58**

Fig. 4

Relationships of optimal spectral features with rice yield (vegetation indices: A, B, C; wavelet features: D, E, F)"

Fig. 5

Three-dimensional view of the point cloud data collected by the TLS instrument for a rice plot at post-heading stages in Rugao (A, B, C) and Xinghua (D, E, F). The data points are color coded by height relative to the soil background in the plot"

Fig. 6

The height histogram of a rice plot in Xinghua (A) and Rugao (B)"

Fig. 7

The distribution of structural metrics Hmin、H1st、H95th、H99th、Hmax in height histogram "

Fig. 8

Relationships between structural parameters and rice yield"

Table 6

Accuracy assessments of RMSE(t·hm-2) and RRMSE(%) values for the estimation of yield in different datasets based on hyperspectral models "

模型
Models 		如皋Rugao 兴化Xinghua
抽穗期Heading 灌浆前期Early filling 灌浆后期Late filling 抽穗期Heading 灌浆前期Early filling 灌浆后期Late filling
RMSE RRMSE RMSE RRMSE RMSE RRMSE RMSE RRMSE RMSE RRMSE RMSE RRMSE
M11 (VI & LR) 2.97 31.70 2.01 21.05 1.91 20.01 1.84 17.45 1.70 16.14 2.04 19.31
M2 (VI & RF) 3.07 32.20 2.18 22.90 2.16 22.65 2.14 20.30 3.04 28.84 2.25 21.30
M3 (WF & LR) 1.91 20.04 1.84 19.33 1.67 17.54 1.69 16.02 1.50 14.29 1.42 13.45
M4 (WF & RF) 3.04 31.92 2.37 24.88 2.10 21.10 2.11 20.00 2.05 19.42 1.93 18.29

Fig. 9

The scatter plots of measured and predicted yield of rice with the optimal wavelet feature at different growth stages for the datasets collected in Rugao (A, B, C)and Xinghua (D, E, F)"

Fig. 10

The scatter plots of measured and predicted yield of rice with TLS-derived height variable (H95th) at different growth stages in Rugao (A, B, C) and Xinghua (D, E, F) "

Fig. 11

The scatter plots of measured and predicted yield of rice from the structural metrics with random forest regression in Rugao (A, B, C) and Xinghua (D, E, F)"

Fig. 12

Height histogram for an example plot at different growth stages in Rugao and Xinghua"

[1] ALOLA A, ALOLA U V. The dynamic nexus of crop production and population growth: housing market sustainability pathway. Environmental Science and Pollution Research, 2018, 26:6472-6480.
doi: 10.1007/s11356-018-04074-1
[2] UN Food Agriculture Organization. How to feed the word in 2050. Discussion paper prepared for expert forum: 12-13. October 2009, released 23.
[3] INTERPRETERS S. FAO-food and agriculture organization of the united nations. Science, 2013, 118(3077):13-23.
[4] JULIAN P, MARK B, SARAH M. Food waste within food supply chains: quantification and potential for change to 2050. Philosophical Transactions of the Royal Society of London, 2010, 365(1554):3065-3081.
[5] LIU J G, PATTEY E, MILLER J R, MCNAIRN H, SMITH A, HU B X. Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sensing of Environment, 2010, 114(6):1167-1177.
doi: 10.1016/j.rse.2010.01.004
[6] MUTANGA O, ADAM E, CHO M A. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observations and Geoinformation, 2012, 18(1):399-406.
doi: 10.1016/j.jag.2012.03.012
[7] 亓雪勇, 田庆久. 光学遥感大气校正研究进展. 国土资源遥感, 2005, 45(4):4-9.
QI X Y, TIAN Q J. The advance in the study of atmospheric correction for optical remote sensing. Remote Sensing for Land and Resources, 2005, 45(4):4-9. (in Chinese)
[8] 唐延林, 黄敬峰, 王人潮, 王福民. 水稻遥感估产模拟模式比较. 农业工程学报, 2004, 3(1):167-172.
TANG Y L, HUANG J F, WANG R C, WANG F M. Comparsion of yield estimation simulated models of rice by remote sensing. Transactions of The Chinese Society of Agricultural Engineering, 2004, 3(1):167-172. (in Chinese)
[9] OWERS, C J, ROGERS K, WOODROFFE C D. Terrestrial laser scanning to quantify above-ground biomass of structurally complex coastal wetland vegetation. Estuarine Coastal and Shelf Science, 2018, 204:164-176.
doi: 10.1016/j.ecss.2018.02.027
[10] EITEL J U H, MAGNEY T S, VIERLING L A, BROWN T T, HUGGINS D R. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research, 2014, 159:21-32.
doi: 10.1016/j.fcr.2014.01.008
[11] TILLY N, HOFFMEISTER D, CAO Q, LENZ-WIEDEMANN V, BARETH G. Precise plant height monitoring and biomass estimation with Terrestrial Laser Scanning in paddy rice. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, II-5/W2:295-300.
doi: 10.5194/isprsannals-II-5-W2-295-2013
[12] TILLY N, AASEN H, BARETH G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sensing, 2015, 7:11449-11480.
doi: 10.3390/rs70911449
[13] GUO Q H, WU F F, PANG S X, ZHAO X Q, CHEN L H, LIU J, XUE B L, XU G C, LI L, JING H C. Crop 3D: a platform based on LiDAR for 3D high-throughput crop phenotyping. Scientia Sinica, 2016, 12:121-141.
[14] LUMME J, KARJALAINEN M, KAARTINEN H, KUKKO A, HYYPPÄ J, HYYPPÄ H, JAAKKOLA A, KLEEMOLA J. Terrestrial laser scanning of agricultural crops. International Journal of Remote Sensing, 2008, 26(7):563-566.
doi: 10.1080/01431160512331299270
[15] MCKINION J M, WILLERS J L, JENKINS J N. Comparing high density LIDAR and medium resolution GPS generated elevation data for predicting yield stability. Computers and Electronics in Agriculture, 2010, 74(2):244-249.
doi: 10.1016/j.compag.2010.08.011
[16] LI Z H, WANG J H, XU X G, ZHAO C J, JIN X L, YANG G J, FENG H K. Assimilation of two variables derived from hyperspectral data into the DSSAT-CERES model for grain yield and quality estimation. Remote Sensing, 2015, 35(9):12400-12418.
[17] 王延颐. 植被指数与水稻长势及产量结构要素关系的研究. 国土资源遥感, 1996, 2(1):56-59.
WANG Y Y. The relationship between vegetation index and rice growth and rice yield components. Remote Sensing for Land and Resources, 1996, 2(1):56-59. (in Chinese)
[18] 李卫国, 王纪华, 赵春江, 李存军, 王永华. 基于生态因子的冬小麦产量遥感估测研究. 麦类作物学报, 2009, 29(5):213-220.
LI W G, WANG J H, ZHAO C J, LI C J, WANG Y H. Study on remote sensing estimation of winter wheat yield based on eco-environmental factors. Journal of Triticeae Crop, 2009, 29(5):213-220. (in Chinese)
[19] 薛利红, 曹卫星, 罗卫红. 基于冠层反射光谱的水稻产量预测模型. 遥感学报, 2005, 7(1):102-107.
XUE L H, CAO W X, LUO W H. Rice yield forecasting model with canopy reflectance spectra. Journal of Remote Sensing, 2005, 7(1):102-107. (in Chinese)
[20] CHENG T, RIVARD B, SÁNCHEZ-AZOFEIFA A G, FÉRET J B, JACQUEMOUD S, USTIN S L. Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87:28-38.
doi: 10.1016/j.isprsjprs.2013.10.009
[21] 宋开山, 刘殿伟, 王宗明, 吕冬梅, 张柏, 任春颖, 杜嘉. 基于小波分析的玉米叶绿素a与LAI高光谱反演模型研究. 农业系统科学与综合研究, 2011, 3(2):28-34.
SONG K S, LIU D W, WANG Z M, LÜ D M, ZHANG B, REN C Y, DU J. Corn chlorophyll-a concentration and LAI estimation models based on wavelet transformed canopy hyperspectral reflectance. System Sciences and Comprehensive Studies in Agriculture, 2011, 3(2):28-34. (in Chinese)
[22] 宋开山, 张柏, 王宗明, 刘殿伟, 刘焕军. 基于小波分析的大豆叶绿素a含量高光谱反演模型. 植物生态学报, 2008, 2(1):157-165.
SONG K S, ZHANG B, WANG Z M, LIU D W, LIU H J. Soybean chlorophyll a concentration estimation models based on wavelet- transformed, in situ collected, canopy hyperspectral data. Journal of Plant Ecology, 2008, 2(1):157-165. (in Chinese)
[23] 洪雪. 基于水稻高光谱遥感数据的植被指数产量模型研究[D]. 沈阳: 沈阳农业大学, 2017.
HONG X. Rice yield model research based on vegetation index of hyperspectral remote sensing data[D]. Shenyang: Shenyang Agricultural University, 2017. (in Chinese)
[24] 王娣. 高光谱与多光谱遥感水稻估产研究[D]. 武汉: 武汉大学, 2017.
WANG D. Hyperspectral and multispectral remote sensing study on yield estimation of rice[D]. Wuhan: Wuhan University, 2017. (in Chinese)
[25] 黄春燕, 王登伟, 陈冠文, 袁杰, 祁亚琴, 陈燕, 程诚. 基于高光谱植被指数的棉花干物质积累估算模型研究. 棉花学报, 2006, 18(2):115-119.
HUANG C Y, WANG D W, CHEN G W, YUAN J, QI Y Q, CHEN Y, CHENG C. Estimation modeling of cotton dry matter accumulation based on hyperspectral vegetation index. Cotton Science, 2006, 18(2):115-119. (in Chinese)
[26] 许童羽, 洪雪, 陈春玲, 周云成, 曹英丽, 于丰华, 李娜. 基于冠层NDVI数据的北方粳稻产量模型研究. 浙江农业学报, 2016, 28(10):1790-1795.
XU T Y, HONG X, CHEN C L, ZHOU Y C, CAO Y L, YU F H, LI N. Study on northern japonica rice yield model based on canopy date of NDVI. Acta Agriculturae Zhejiangensis, 2016, 28(10):1790-1795. (in Chinese)
[27] 宋红燕, 胡克林, 彭希. 基于高光谱技术的覆膜旱作水稻植株氮含量及籽粒产量估算. 中国农业大学学报, 2016, 4(8):27-34.
SONG H Y, HU K L, PENG X. Crop nitrogen content diagnosis and yield estimation in ground cover rice production system based on hyperspectral data. Journal of China Agricultural University, 2016, 4(8):27-34. (in Chinese)
[28] SHIBAYAMA M. AKIYAMA T. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing of Environment, 1991, 36(1):45-53.
doi: 10.1016/0034-4257(91)90029-6
[29] BAI J H, LI S K, WANG K R, SUI X Y, CHEN B, WANG F Y. Estimating aboveground fresh biomass of different cotton canopy types with homogeneity models based on hyper spectrum parameters. Agricultural Sciences in China, 2007, 6(4):437-445.
doi: 10.1016/S1671-2927(07)60067-4
[30] LI P, ZHANG X, WANG W H, ZHENG H B, YAO X, TIAN Y C, ZHU Y, CAO W X, CHEN Q, CHENG T. Estimating aboveground and organ biomass of plant canopies across the entire season of rice growth with terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 2020, 91:102-132.
[31] GITELSON A A, VIÑA A, ARKEBAUER T J, RUNDQUIST D C, KEYDAN G P, LEAVITT B, KEYDAN G. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters, 2003, 30:335-343.
[32] HUDAK A T, LEFSKY M A, COHEN W B, BERTERRETCHE M. Integration of LiDAR and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment, 2015, 82(2/3):397-416.
doi: 10.1016/S0034-4257(02)00056-1
[33] WEI F, HUI F, YANG W N, DUAN L F, CHEN G X, XIONG L Z, LIU Q. High-throughput volumetric reconstruction for 3D wheat plant architecture studies. Journal of Innovative Optical Health Sciences, 2016, 9(5):1650037.
doi: 10.1142/S1793545816500371
[34] HUANG J F, BLACKBURN G A. Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data. International Journal of Remote Sensing, 2011, 32(24):9375-9396.
doi: 10.1080/01431161.2011.558130
[35] CHEN Q. Modeling aboveground tree woody biomass using national- scale allometric methods and airborne LiDAR. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106:95-106.
doi: 10.1016/j.isprsjprs.2015.05.007
[36] DAI B, GU C S, ZHAO E, QIN X N. Statistical model optimized random forest regression model for concrete dam deformation monitoring. Structural Control and Health Monitoring, 2018, 25(6):1-15.
[37] TILLY N, HOFFMEISTER D, CAO Q, LENZ-WIEDEMANN V, MIAO Y X, BARETH G. Transferability of models for estimating paddy rice biomass from spatial plant height data. Agriculture, 2015, 5:538-552.
doi: 10.3390/agriculture5030538
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