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"

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