Welcome to Acta Agronomica Sinica,

Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (4): 1030-1042.doi: 10.3724/SP.J.1006.2024.33030

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Estimation of maize grain yield under drought stress based on continuous wavelet transform

ZOU Jia-Qi1(), WANG Zhong-Lin1,2, TAN Xian-Ming1, CHEN Liao-Yuan1, YANG Wen-Yu1, YANG Feng1,*()   

  1. 1College of Agronomy, Sichuan Agricultural University / Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture and Rural Affairs / Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu 611130, Sichuan, China
    2Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, Sichuan, China
  • Received:2023-05-13 Accepted:2023-10-23 Online:2024-04-12 Published:2023-11-15
  • Contact: * E-mail: f.yang@sicau.edu.cn, Tel: 028-86290867
  • Supported by:
    National Key Research and Development Program of China(2022YFD2300902)

Abstract:

The use of hyperspectral remote sensing technology to monitor crop water status and grain yield is important for regulating crop growth, optimizing water management and improving yield formation. Zhenghong 505 was selected as the maize variety in this study, to analyze the quantitative relationship between canopy water content (CWC) and grain yield of maize at jointing stage (V6), tasseling stage (VT), and filling stage (R2), four drought stress treatments (well-watered, mild, intermediate and severe drought) were conducted in the experimental fields of Ya’an and Renshou in Sichuan Province from 2018 to 2019. The spectral reflectance data were processed using vegetation indices and continuous wavelet transform, and a linear regression method was used to construct a quantitative CWC inversion model to explore the effectiveness of CWC as a bridge to establish a spectral inversion model for maize grain yield estimation. The results showed that the CWC estimation models using wavelet features was better than that of vegetation indices, and the linear regression models constructed with wavelet features gaus3770,64, rbio3.31635,2 and rbio3.3838,2 at the V6, VT, and R2 stages had high test accuracy with the R2 of 0.770, 0.291, and 0.233, respectively. The linear regression models established between CWC and maize grain yield all reached highly significant levels (P < 0.01), with R2 of 0.596, 0.366 and 0.439 at the V6, VT, and R2 stages, respectively. The yield prediction model based on the basis of spectral reflectance was the best validated with the wavelet feature gaus3770,64 (R2 = 0.577, RMSE = 1.625 t hm-2) at V6 stage, which can be used as the best period for predicting maize grain yield. Therefore, the “spectral reflectance-canopy water content-yield” modeling method proposed in this study can achieve an accurate estimation of maize grain yield and provide a theoretical basis for future large-scale monitoring of maize productivity.

Key words: maize, grain yield, canopy water content, vegetation indices, wavelet features

Table 1

Years, sites, experimental carrier, cultivars, treatments for three experiments"

试验编号Exp. No. 年份和地点
Year and site
试验载体
Experimental carrier
玉米品种
Maize cultivar
试验处理
Treatment
试验1
Exp. 1
2018, 雅安
2018, Ya’an
干旱池
Drought pool
正红505
Zhenghong 505
水分等级(占田间持水量百分比)
正常水分(60%-70%), 轻度干旱(45%-55%), 中度干旱(30%-40%), 重度干旱(15%-25%)
Moisture degree (percentage of field capacity)
WW (60%-70%), MD (45%-55%), ID (30%-40%), SD (15%-25%)
试验2
Exp. 2
2018, 仁寿
2018, Renshou
大田
Field
正红505
Zhenghong 505
倾斜农田(坡度/°)
斜坡顶部, 斜坡中间, 斜坡下方
Sloping farmland (gradient/°)
Top of the slope, middle of the slope, under the slope
试验3
Exp. 3
2019, 雅安
2019, Ya’an
干旱池
Drought pool
正红505
Zhenghong 505
水分等级(占田间持水量百分比)
正常水分(60%-70%), 轻度干旱(45%-55%), 中度干旱(30%-40%), 重度干旱(15%-25%)
Moisture degree (percentage of field capacity)
WW (60%-70%), MD (45%-55%), ID (30%-40%), SD (15%-25%)

Fig. 1

Spectral reflectance of maize under normal moisture treatment after band removal A: jointing stage; B: tasseling stage; C: filling stage."

Fig. 2

Flowchart of the CWT CWT: canopy water content."

Table 2

Vegetation indices used in this study"

植被指数
Vegetation indices
公式
Formula
参考文献
Reference
水分指数Water index (WI) $\frac{{{R}_{900}}}{{{R}_{970}}}$ [29]
水分胁迫指数Moisture stress index (MSI) $\frac{{{R}_{1599}}}{{{R}_{819}}}$ [30-31]
比值植被指数Ratio vegetation index (RVI) $\frac{{{R}_{i}}}{{{R}_{j}}}$ [32]
差值植被指数Difference vegetation index (DVI) ${{R}_{i}}-{{R}_{j}}$ [33]
归一化差值植被指数Normalized difference vegetation index (NDVI) $\frac{\left( {{R}_{i}}-{{R}_{j}} \right)}{\left( {{R}_{i}}+{{R}_{j}} \right)}$ [34]

Table 3

Descriptive statistics of maize canopy water content and grain yield"

参数
Parameters
数据集
Datasets
生育期
Growth stages
样本数
Number of samples
最大值Maximum 最小值Minimum 均值
Mean
标准差Standard deviation 变异系数Coefficient
of variation (%)
冠层含水量
Canopy water content
(kg m-2)
建模集
Calibration set
V6 44 0.468 0.051 0.222 0.115 51.7
VT 48 1.246 0.479 0.847 0.215 25.4
R2 38 1.198 0.581 0.889 0.151 16.9
验证集
Validation set
V6 46 0.836 0.056 0.347 0.227 65.4
VT 33 0.952 0.174 0.493 0.168 34.0
R2 36 0.993 0.275 0.642 0.197 30.6
籽粒产量
Grain yield
(t hm-2)
建模集
Calibration set
V6 12 5.051 3.353 4.060 0.547 13.5
VT 12 6.024 3.199 4.772 0.851 17.8
R2 10 6.200 3.420 5.403 0.828 15.3
验证集
Validation set
V6 12 8.761 3.940 5.964 1.249 20.9
VT 9 5.764 3.425 4.782 0.926 19.4
R2 9 11.101 6.402 9.022 1.413 15.7

Fig. 3

Correlation coefficient (r) matrix diagram between canopy water content and VIs of maize A: jointing stage; B: tasseling stage; C: filling stage. DVI: difference vegetation index; RVI: ratio vegetation index; NDVI: normalized difference vegetation index."

Table 4

Correlation between canopy water content and VIs of maize in different growth stages"

生育期Grown stages WI MSI DVI RVI NDVI
波长
W
相关系数
r
波长
W
相关系数
r
波长
W
相关系数
r
波长
W
相关系数
r
波长
W
相关系数
r
V6
n = 44
900, 970 0.691** 1599, 819 -0.766** 750, 751 0.881** 2150, 1189 0.933** 1471, 1209 0.937**
VT
n = 48
900, 970 0.684** 1599, 819 -0.395** 1667, 1583 0.793** 807, 761 -0.831** 761, 807 0.830**
R2
n = 38
900, 970 0.117 1599, 819 0.142 2409, 1065 0.506** 855, 856 0.538** 855, 856 0.538**

Fig. 4

Correlation coefficient (r) matrix diagram between canopy water content and wavelet features of maize A: jointing stage; B: tasseling stage; C: filling stage."

Table 5

Correlation between canopy water content and wavelet features of maize at specific scale"

生育期Grown stage db3 bior1.5 rbio3.3 gaus3
特征位置
Feature location
相关系数
r
特征位置
Feature location
相关系数
r
特征位置
Feature location
相关系数
r
特征位置
Feature location
相关系数
r
波长
W
尺度Scale 波长
W
尺度Scale 波长
W
尺度Scale 波长
W
尺度Scale
V6
n = 44
718 64 -0.926** 722 1 0.932** 795 128 -0.933** 770 64 -0.929**
VT
n = 48
1524 1 -0.805** 1530 1 -0.783** 1635 2 -0.786** 1641 2 -0.790**
R2
n = 38
840 2 -0.596** 466 1 0.548** 838 2 0.585** 466 1 0.557**

Table 6

Prediction models of canopy water content at different growth stages"

生育期
Growth stage
参数
Parameter
回归模型
Regression model
决定系数
R2
均方根误差
RMSE (kg m-2)
V6 DVI750,751 y = -0.065-1.410 x 0.776** 0.054
RVI2150,1189 y = 0.543-0.837 x 0.814** 0.050
NDVI1471,1209 y = -0.105-0.805 x 0.877** 0.040
db3718,64 y = 0.097-0.017x 0.857** 0.044
bior1.5722,1 y = 0.087+3226.741x 0.868** 0.042
rbio3.3795,128 y = 0.181-0.020 x 0.870** 0.035
gaus3770,64 y = 0.097-0.018 x 0.862** 0.043
VT DVI1667,1583 y = 1.19-0.142 x 0.628** 0.131
RVI807,761 y = -15.049+15.193 x 0.688** 0.120
NDVI761,807 y = 0.127-31.888 x 0.689** 0.120
db31524,1 y = 1.194-3.761 x 0.648** 0.128
bior1.51530,1 y = 0.987-485.085 x 0.614** 0.134
rbio3.31635,2 y = 1.189-1.572 x 0.617** 0.133
gaus31641,2 y = 1.231-1.215 x 0.624** 0.132
R2 DVI2409,1065 y = 0.827-0.005 x 0.256** 0.130
RVI855,856 y = 582.227-581.635 x 0.290** 0.127
NDVI855,856 y = 0.592-1162.731 x 0.290** 0.127
db3840,2 y = 0.938-77.240 x 0.355** 0.121
bior1.5466,1 y = 0.817+45351.913 x 0.299** 0.126
rbio3.3838,2 y = 0.932+43.040 x 0.343** 0.122
gaus3466,1 y = 0.89+338.957 x 0.311** 0.125

Fig. 5

Relationship between measured and predicted canopy water content A: jointing stage; B: tasseling stage; C: filling stage; CWC: canopy water content."

Fig. 6

Prediction model of maize grain yield using canopy water content A: jointing stage; B: tasseling stage; C: filling stage."

Fig. 7

Testing of maize grain yield estimation model based on spectral reflectance data A: jointing stage; B: tasseling stage; C: filling stage."

[1] 王延仓, 张萧誉, 金永涛, 顾晓鹤, 冯华, 王闯. 基于连续小波变换定量反演冬小麦叶片含水量研究. 麦类作物学报, 2020, 40: 503-509.
Wang Y C, Zhang X Y, Jin Y T, Gu X H, Feng H, Wang C. Quantitative retrieval of water content in winter wheat leaves based on continuous wavelet transform. J Triticeae Crops, 2020, 40: 503-509. (in Chinese with English abstract)
[2] Ren B, Zhang J, Li X, Fan X, Dong S, Liu P, Zhao B. Effects of waterlogging on the yield and growth of summer maize under field conditions. Can J Plant Sci, 2014, 94: 23-31.
doi: 10.4141/cjps2013-175
[3] Zhou H, Zhou G, Song X, He Q. Dynamic characteristics of canopy and vegetation water content during an entire maize growing season in relation to spectral-based indices. Remote Sens, 2022, 14: 584.
doi: 10.3390/rs14030584
[4] 任传友, 姜卓群, 苏小琁, 米前川, 王婧, 李玥, 高西宁. 水分胁迫/复水对谷子光合特性及产量影响. 应用气象学报, 2021, 32: 456-467.
Ren C Y, Jiang Z Q, Su X X, Mi Q C, Wang J, Li Y, Gao X N. Effect of water stress/rewatering on photosynthetic characteristics and yield of cereals. J Appl Meteorol Sci, 2021, 32: 456-467. (in Chinese with English abstract)
[5] 马雅丽, 郭建平, 栾青, 刘文平, 李蕊. 持续性水分胁迫对冬小麦光合特性及产量的影响. 气象, 2022, 48: 1303-1311.
Ma Y L, Guo J P, Luan Q, Liu W P, Li R. Effects of persistent water stress on photosynthetic characteristics and yield of winter wheat. Meteorol Month, 2022, 48: 1303-1311. (in Chinese with English abstract)
[6] 唐源, 王小平, 鲁聪聪, 赵传燕. 基于PROSAIL模型与光谱指数的紫花苜蓿冠层含水量估算. 兰州大学学报(自然科学版), 2023, 59: 55-62.
Tang Y, Wang X P, Lu C C, Zhao C Y. Estimating the canopy water content of alfalfa based on the PROSAIL model and spectral index. J Lanzhou Univ (Nat Sci Edn), 2023, 59: 55-62. (in Chinese with English abstract)
[7] Zhang F, Zhou G. Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize. BMC Ecol, 2019, 19: 18.
doi: 10.1186/s12898-019-0233-0 pmid: 31035986
[8] 江海英, 柴琳娜, 贾坤, 刘进, 杨世琪, 郑杰. 联合PROSAIL模型和植被水分指数的低矮植被含水量估算. 遥感学报, 2021, 25: 1025-1036.
Jiang H Y, Chai L N, Jia K, Liu J, Yang S Q, Zheng J. Estimation of water content for short vegetation based on PROSAIL model and vegetation water indices. Nation Remote Sens Bull, 2021, 25: 1025-1036. (in Chinese with English abstract)
[9] Wang Z L, Chen J X, Fan Y F, Cheng Y J, Wu X L, Zhang J W, Wang B B, Wang X C, Yong T W, Liu W G, Liu J, Du J B, Yang W Y, Yang F. Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform. Comput Electron Agric, 2020, 169: 105160.
doi: 10.1016/j.compag.2019.105160
[10] Cheng T, Rivard B, Sánchez-Azofeifa A G, Féret J B, Jacquemoud S, Ustin S L. Predicting leaf gravimetric water content from foliar reflectance across a range of plant species using continuous wavelet analysis. J Plant Physiol, 2012, 169: 1134-1142.
doi: 10.1016/j.jplph.2012.04.006
[11] Cheng T, Riaño D, Ustin S L. Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis. Remote Sens Environ, 2014, 143: 39-53.
doi: 10.1016/j.rse.2013.11.018
[12] Liu H J, Li M Z, Zhang J Y, Gao D H, Sun H, Yang L W. Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method. Int J Agric Biol Eng, 2018, 11: 132-137.
[13] Liu Y, Sun Q, Feng H K, Yang F Q. Estimation of above-ground biomass of potato based on wavelet analysis. Spectrosc Spectral Anal, 2021, 41: 1205-1212.
[14] Liu M, Liu X, Ding W, Wu L. Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis. Int J Appl Earth Obs Geoinf, 2010, 13: 246-255.
[15] Chen J X, Li F, Wang R, Fan Y F, Ali Raza M, Liu Q L, Wang Z L, Cheng Y J, Wu X L, Yang F, Yang W Y. Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress. Comput Electron Agric, 2019, 156: 482-489.
doi: 10.1016/j.compag.2018.12.003
[16] Tao H L, Feng H K, Xu L J, Miao M K, Yang G J, Yang X D, Fan L L. Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors, 2020, 20: 1231.
doi: 10.3390/s20041231
[17] Yue J B, Feng H K, Yang G J, Li Z H. A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens, 2018, 10: 66.
doi: 10.3390/rs10010066
[18] Zhang F, Zhou G S. Estimation of canopy water content by means of hyperspectral indices based on drought stress gradient experiments of maize in the North Plain China. Remote Sens, 2015, 7: 15203-15223.
doi: 10.3390/rs71115203
[19] Atzberger C, Darvishzadeh R, Immitzer M, Martin S, Andrew S, Guerric I M. Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy. Int J Appl Earth Obs Geoinf, 2015, 43: 19-31.
[20] 陶惠林, 徐良骥, 冯海宽, 杨贵军, 杨小冬, 牛亚超. 基于无人机高光谱遥感数据的冬小麦产量估算. 农业机械学报, 2020, 51(7): 146-155.
Tao H L, Xu L J, Feng H K, Yang G J, Yang X D, Niu Y C. Winter wheat yield estimation based on UAV hyperspectral remote sensing data. Trans CSAM, 2020, 51(7): 146-155. (in Chinese with English abstract)
[21] 韩文霆, 彭星硕, 张立元, 牛亚晓. 基于多时相无人机遥感植被指数的夏玉米产量估算. 农业机械学报, 2020, 51(1): 148-155.
Han W T, Peng X S, Zhang L Y, Niu Y X. Summer maize yield estimation based on vegetation index derived from multi- temporal UAV remote sensing. Trans CSAM, 2020, 51(1): 148-155. (in Chinese with English abstract)
[22] Jiang Y, Wei H, Hou S, Yin X, Wei S, Jiang D. Estimation of maize yield and protein content under different density and N rate conditions based on UAV multi-spectral images. Agronomy, 2023, 13: 421.
doi: 10.3390/agronomy13020421
[23] Li Z H, Jin X L, Zhao C J, Wang J H, Xu X G, Yang G J, Li C J, Shen J X. Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. Eur J Agron, 2015, 71: 53-62.
doi: 10.1016/j.eja.2015.08.006
[24] Zhang C, Liu J G, Shang J L, Cai H J. Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation. Sci Total Environ, 2018, 631-632: 677-687.
[25] Clevers J G P W, Kooistra L, Schaepman M E. Estimating canopy water content using hyperspectral remote sensing data. Int J Appl Earth Obs Geoinf, 2010, 12: 119-125.
[26] Blackburn G A, Ferwerda J G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sens Environ, 2008, 112: 1614-1632.
doi: 10.1016/j.rse.2007.08.005
[27] Nourani V, Baghanam A H, Adamowski J, Gebremichael M. Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J Hydrol, 2013, 476: 228-243.
doi: 10.1016/j.jhydrol.2012.10.054
[28] Rivard B, Feng J, Gallie A, Sanchez-Azofeifa A. Continuous wavelets for the improved use of spectral libraries and hyperspectral data. Remote Sens Environ, 2008, 112: 2850-2862.
doi: 10.1016/j.rse.2008.01.016
[29] Penuelas J, Filella I, Biel C S, Serrano L, Save R. The reflectance at the 950-970 nm region as an indicator of plant water status. Int J Remote Sens, 1993, 14: 1887-1905.
doi: 10.1080/01431169308954010
[30] Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sens Environ, 2002, 82: 188-197.
doi: 10.1016/S0034-4257(02)00037-8
[31] Ceccato P, Flasse S, Grégoire J M. Designing a spectral index to estimate vegetation water content from remote sensing data: part 2. Validation and applications. Remote Sens Environ, 2002, 82: 198-207.
doi: 10.1016/S0034-4257(02)00036-6
[32] Pearson R L, Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In: Proceeding of the 8th International Symposium on Remote Sensing of the Environment, Michigan, Ann Arbor, Michigan USA, 1972. pp 1355-1397.
[33] Jordan C F. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 1969, 50: 663-666.
doi: 10.2307/1936256
[34] Rouse J W, Haas R H, Schell J A, Deering D W. Monitoring vegetation systems in the great plains with erts. NASA Sp Publ, 1974, 351: 309-313.
[35] Mutanga O, Skidmore A K. Red edge shift and biochemical content in grass canopies. ISPRS J Photogr Remote Sens, 2007, 62: 34-42.
doi: 10.1016/j.isprsjprs.2007.02.001
[36] Oki K, Yasuoka Y. Estimation of chlorophyll-a concentration in rich chlorophyll water area from near-infrared and red Spectral signature. J Remote Sens Soc Jpn, 2009, 16: 315-323.
[37] Cheng T, Rivard B, Sánchez-Azofeifa A. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ, 2010, 115: 659-670.
doi: 10.1016/j.rse.2010.11.001
[38] 林波, 杨玉静. 利用连续统去除方法遥感反演冠层水分含量的比较研究. 气象研究与应用, 2012, 33: 181-184.
Lin B, Yang Y J. A comparative study of remote sensing inversion of canopy moisture content using the continuum removal method. J Meteorol Res Appl, 2012, 33: 181-184. (in Chinese with English abstract)
[39] Sims D A, Gamon J A. Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens Environ, 2003, 84: 526-537.
doi: 10.1016/S0034-4257(02)00151-7
[40] 苏涛, 王鹏新, 刘翔舸, 杨博. 基于熵值组合预测和多时相遥感的春玉米估产. 农业机械学报, 2011, 42(1): 186-192.
Su T, Wang P X, Liu X G, Yang B. Spring maize yield estimation based on combination of forecasting of entropy method and multi-temporal remotely sensed data. Trans CSAM, 2011, 42(1): 186-192. (in Chinese with English abstract)
[41] 任丽雯, 刘明春, 王兴涛, 丁文魁, 王润元. 拔节和抽雄期水分胁迫对春玉米生长和产量的影响. 中国农学通报, 2019, 35(1): 17-22.
doi: 10.11924/j.issn.1000-6850.casb17100010
Ren L W, Liu M C, Wang X T, Ding W K, Wang R Y. Water stress at jointing and tasseling stage: effect on growth and yield of spring maize. Chin Agric Sci Bull, 2019, 35(1): 17-22. (in Chinese with English abstract)
doi: 10.11924/j.issn.1000-6850.casb17100010
[42] 白向历, 孙世贤, 杨国航, 刘明, 张振平, 齐华. 不同生育时期水分胁迫对玉米产量及生长发育的影响. 玉米科学, 2009, 17(2): 60-63.
Bai X L, Sun S X, Yang G H, Liu M, Zhang Z P, Qi H. Effect of water stress on maize yield during different growing stages. J Maize Sci, 2009, 17(2): 60-63. (in Chinese with English abstract)
[43] 李叶蓓, 陶洪斌, 王若男, 张萍, 吴春江, 雷鸣, 张巽, 王璞. 干旱对玉米穗发育及产量的影响. 中国生态农业学报, 2015, 23: 383-391.
Li Y B, Tao H B, Wang R N, Zhang P, Wu C J, Lei M, Zhang X, Wang P. Effect of drought on ear development and yield of maize. Chin J Eco-Agric, 2015, 23: 383-391. (in Chinese with English abstract)
[44] 郭松, 常庆瑞, 郑智康, 蒋丹垚, 高一帆, 宋子怡, 姜时雨. 基于无人机高光谱影像的玉米叶绿素含量估测. 江苏农业学报, 2022, 38: 976-984.
Guo S, Chang Q R, Zheng Z K, Jiang D Y, Gao Y F, Song Z Y, Jiang S Y. Estimation of maize chlorophyll content based on unmanned aerial vehicle (UAV) hyperspectral images. Jiangsu J Agric Sci, 2022, 38: 976-984. (in Chinese with English abstract)
[45] Mirzaie M, Darvishzadeh R, Shakiba A, Matkan A A, Atzberger C, Skidmore A. Comparative analysis of different uni-and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. Int J Appl Earth Obs Geoinf, 2014, 26: 1-11.
[46] Jiang Y, Wei H J, Hou S X, Yin X B, Wei S S, Jiang D. Estimation of maize yield and protein content under different density and N rate conditions based on UAV multi-spectral images. Agronomy, 2023, 13: 421.
doi: 10.3390/agronomy13020421
[47] 祝榛, 李天胜, 崔静, 陈建华, 史晓艳, 姜孟豪, 王海江. 基于高光谱成像估测冬小麦不同生育时期水分状况. 新疆农业科学, 2022, 59: 521-532.
doi: 10.6048/j.issn.1001-4330.2022.03.001
Zhu Z, Li T S, Cui J, Chen J H, Shi X Y, Jiang M H, Wang H J. Study on estimation of water status of winter wheat in different growth stages based on hyperspectral imaging. Xinjiang Agric Sci, 2022, 59: 521-532. (in Chinese with English abstract)
doi: 10.6048/j.issn.1001-4330.2022.03.001
[48] Zhang Y, Yang Y Z, Zhang Q W, Duan R Q, Liu J Q, Qin Y C, Wang X Z. Toward multi-stage phenotyping of soybean with multimodal UAV sensor data: a comparison of machine learning approaches for leaf area index estimation. Remote Sens, 2022, 15: 7.
doi: 10.3390/rs15010007
[1] HAN Jie-Nan, ZHANG Ze, LIU Xiao-Li, LI Ran, SHANG-GUAN Xiao-Chuan, ZHOU Ting-Fang, PAN Yue, HAO Zhuan-Fang, WENG Jian-Feng, YONG Hong-Jun, ZHOU Zhi-Qiang, XU Jing-Yu, LI Xin-Hai, LI Ming-Shun. Analysis of differential accumulation of starch in waxy maize grain caused by the o2 mutation gene [J]. Acta Agronomica Sinica, 2024, 50(5): 1207-1222.
[2] WANG Yong-Liang, XU Zi-Hang, LI Shen, LIANG Zhe-Ming, BAI Ju, YANG Zhi-Ping. Effects of different mulching measures on moisture and temperature of soil and yield and water use efficiency of spring maize [J]. Acta Agronomica Sinica, 2024, 50(5): 1312-1324.
[3] TIAN Hong-Li, YANG Yang, FAN Ya-Ming, YI Hong-Mei, WANG Rui, JIN Shi-Qiao, JIN Fang, ZHANG Yun-Long, LIU Ya-Wei, WANG Feng-Ge, ZHAO Jiu-Ran. Development of an optimal core SNP loci set for maize variety genuineness identification [J]. Acta Agronomica Sinica, 2024, 50(5): 1115-1123.
[4] SU Shuai, LIU Xiao-Wei, NIU Qun-Kai, SHI Zi-Wen, HOU Yu-Wei, FENG Kai-Jie, RONG Ting-Zhao, CAO Mo-Ju. Identification and gene cloning of leafy dwarf mutant lyd1 in maize [J]. Acta Agronomica Sinica, 2024, 50(5): 1124-1135.
[5] WU Xia-Yu, LI Pan, WEI Jin-Gui, FAN Hong, HE Wei, FAN Zhi-Long, HU Fa-Long, CHAI Qiang, YIN Wen. Effect of reduced irrigation and combined application of organic and chemical fertilizers on photosynthetic physiology, grain yield and quality of maize in northwestern irrigation areas [J]. Acta Agronomica Sinica, 2024, 50(4): 1065-1079.
[6] LOU Fei, ZUO Yi-Ping, LI Meng, DAI Xin-Meng, WANG Jian, HAN Jin-Ling, WU Shu, LI Xiang-Ling, DUAN Hui-Jun. Effects of organic fertilizer substituting chemical fertilizer nitrogen on yield, quality, and nitrogen efficiency of waxy maize [J]. Acta Agronomica Sinica, 2024, 50(4): 1053-1064.
[7] ZHANG Zhen, ZHAO Jun-Ye, SHI Yu, ZHANG Yong-Li, YU Zhen-Wen. Effects of different sowing space on photosynthetic characteristics after anthesis and grain yield of wheat [J]. Acta Agronomica Sinica, 2024, 50(4): 981-990.
[8] YUE Hai-Wang, WEI Jian-Wei, LIU Peng-Cheng, CHEN Shu-Ping, BU Jun-Zhou. Comprehensive evaluation of maize hybrids in the mega-environments of Huanghuaihai plain based on GYT biplot analysis [J]. Acta Agronomica Sinica, 2024, 50(4): 836-856.
[9] XUE Ming, WANG Chen-Chen, JIANG Lu-Guang, LIU Hao, ZHANG Lu-Yao, CHEN Sai-Hua. Mapping and functional analysis of maize inflorescence development gene AFP1 [J]. Acta Agronomica Sinica, 2024, 50(3): 603-612.
[10] WEI Huan-He, ZHANG Xiang, ZHU Wang, GENG Xiao-Yu, MA Wei-Yi, ZUO Bo-Yuan, MENG Tian-Yao, GAO Ping-Lei, CHEN Ying-Long, XU Ke, DAI Qi-Gen. Effects of salinity stress on grain-filling characteristics and yield of rice [J]. Acta Agronomica Sinica, 2024, 50(3): 734-746.
[11] ZHAO Rong-Rong, CONG Nan, ZHAO Chuang. Optimal phase selection for extracting distribution of winter wheat and summer maize over central subregion of Henan Province based on Landsat 8 imagery [J]. Acta Agronomica Sinica, 2024, 50(3): 721-733.
[12] LIANG Xing-Wei, YANG Wen-Ting, JIN Yu, HU Li, FU Xiao-Xiang, CHEN Xian-Min, ZHOU Shun-Li, SHEN Si, LIANG Xiao-Gui. Is cob color variation in maize accidental or incidental to any agronomic traits? —An example of nationally approved common hybrids over the years [J]. Acta Agronomica Sinica, 2024, 50(3): 771-778.
[13] MAO Yan, ZHENG Ming-Min, MOU Cheng-Xiang, XIE Wu-Bing, TANG Qi. Function analysis of the promoter of natural antisense transcript cis- NATZmNAC48 in maize under osmotic stress [J]. Acta Agronomica Sinica, 2024, 50(2): 354-362.
[14] MA Juan, CAO Yan-Yong. Genome-wide association study of yield traits and special combining ability in maize hybrid population [J]. Acta Agronomica Sinica, 2024, 50(2): 363-372.
[15] YANG Jing-Lei, WU Bing-Jie, WANG An-Zhou, XIAO Ying-Jie. Genomic prediction of maize agronomic and quality traits using multi-omics data [J]. Acta Agronomica Sinica, 2024, 50(2): 373-382.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Li Shaoqing, Li Yangsheng, Wu Fushun, Liao Jianglin, Li Damo. Optimum Fertilization and Its Corresponding Mechanism under Complete Submergence at Booting Stage in Rice[J]. Acta Agronomica Sinica, 2002, 28(01): 115 -120 .
[2] Wang Lanzhen;Mi Guohua;Chen Fanjun;Zhang Fusuo. Response to Phosphorus Deficiency of Two Winter Wheat Cultivars with Different Yield Components[J]. Acta Agron Sin, 2003, 29(06): 867 -870 .
[3] YANG Jian-Chang;ZHANG Jian-Hua;WANG Zhi-Qin;ZH0U Qing-Sen. Changes in Contents of Polyamines in the Flag Leaf and Their Relationship with Drought-resistance of Rice Cultivars under Water Deficiency Stress[J]. Acta Agron Sin, 2004, 30(11): 1069 -1075 .
[4] Yan Mei;Yang Guangsheng;Fu Tingdong;Yan Hongyan. Studies on the Ecotypical Male Sterile-fertile Line of Brassica napus L.Ⅲ. Sensitivity to Temperature of 8-8112AB and Its Inheritance[J]. Acta Agron Sin, 2003, 29(03): 330 -335 .
[5] Wang Yongsheng;Wang Jing;Duan Jingya;Wang Jinfa;Liu Liangshi. Isolation and Genetic Research of a Dwarf Tiilering Mutant Rice[J]. Acta Agron Sin, 2002, 28(02): 235 -239 .
[6] WANG Li-Yan;ZHAO Ke-Fu. Some Physiological Response of Zea mays under Salt-stress[J]. Acta Agron Sin, 2005, 31(02): 264 -268 .
[7] TIAN Meng-Liang;HUNAG Yu-Bi;TAN Gong-Xie;LIU Yong-Jian;RONG Ting-Zhao. Sequence Polymorphism of waxy Genes in Landraces of Waxy Maize from Southwest China[J]. Acta Agron Sin, 2008, 34(05): 729 -736 .
[8] HU Xi-Yuan;LI Jian-Ping;SONG Xi-Fang. Efficiency of Spatial Statistical Analysis in Superior Genotype Selection of Plant Breeding[J]. Acta Agron Sin, 2008, 34(03): 412 -417 .
[9] WANG Yan;QIU Li-Ming;XIE Wen-Juan;HUANG Wei;YE Feng;ZHANG Fu-Chun;MA Ji. Cold Tolerance of Transgenic Tobacco Carrying Gene Encoding Insect Antifreeze Protein[J]. Acta Agron Sin, 2008, 34(03): 397 -402 .
[10] ZHENG Xi;WU Jian-Guo;LOU Xiang-Yang;XU Hai-Ming;SHI Chun-Hai. Mapping and Analysis of QTLs on Maternal and Endosperm Genomes for Histidine and Arginine in Rice (Oryza sativa L.) across Environments[J]. Acta Agron Sin, 2008, 34(03): 369 -375 .