Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (11): 2302-2318.doi: 10.3864/j.issn.0578-1752.2021.11.005

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

Area Extraction and Growth Monitoring of Winter Wheat in Henan Province Supported by Google Earth Engine

ZHOU Ke1,3(),LIU Le1,3,ZHANG YanNa2(),MIAO Ru1,3,YANG Yang1,3   

  1. 1School of Computer and Information Engineering, Henan University, Kaifeng 475004, Henan
    2Department of Laboratory and Equipment Management, Henan University, Kaifeng 475004, Henan
    3Henan Key Laboratory of Big Data Analysis and Processing/ Henan University, Kaifeng 475004, Henan
  • Received:2020-08-01 Accepted:2020-09-27 Online:2021-06-01 Published:2021-06-09
  • Contact: YanNa ZHANG E-mail:zhouke@radi.ac.cn;zyn@henu.edu.cn

RichHTML

74

PDF

514

Abstract:

【Objective】 The aim of this study was to use remote sensing technology to extract the spatial distribution information of winter wheat in Henan province from 2017 to 2020, and then to monitor the growth of winter wheat in 2020 with high frequency and to analyze the meteorological conditions. 【Method】 Based on the cloud platform of Google Earth engine (GEE), the selected Landsat 8 image data were synthesized according to the maximum value of NDVI, and then the features were constructed to add terrain features, texture features, NDVI and a new feature NDVI amplification. Random forest classification method was used to train the sample data according to the constructed features to extract the winter wheat planting area in Henan province from 2017 to 2020. The accuracy of the extracted winter wheat sown area was verified by confusion matrix and Henan statistical yearbook data. After accuracy verification, a mask was generated for the extracted winter wheat planting area in Henan province in 2020. In the mask area (winter wheat planting area) combined with MODIS high time resolution image data, the NDVI synchronization difference method was used to monitor the winter wheat growth from February to April in 2020. 【Result】 The GEE cloud platform could be used to quickly map the spatial distribution information of winter wheat planting areas in Henan province. Using random forest method to add terrain feature, texture feature, NDVI and new feature NDVI could effectively improve the extraction accuracy of winter wheat and reduce the relative error with statistical data. Based on confusion matrix, the average overall classification accuracy was 95.2%, the average kappa coefficient was 0.909, and the average classification accuracy of winter wheat was 95.3%. Compared with the statistical yearbook data of Henan province, the relative errors of winter wheat sown area extracted by this method in Henan province from 2017 to 2019 were all less than 3%. The average relative error of winter wheat sown area in the main planting areas of winter wheat in Henan province was less than 6%. MODIS image data combined with NDVI difference model could be used to monitor the growth of winter wheat in Henan province in 2020. The growth of winter wheat in Henan province was better than that of previous years and 2019 during the return to green period. In the later growth stage of winter wheat, the growth of most areas was the same as that of previous years and 2019. On the whole, the growth of winter wheat in 2020 was better than that of previous years and 2019. 【Conclusion】 The method proposed in this paper could carry out high-precision extraction and high-frequency growth monitoring of winter wheat in Henan province, and could provide a scientific basis for local governments or some agricultural departments in arranging and guiding agricultural activities.

Key words: winter wheat, growth monitoring, Google Earth Engine, random forests, NDVI, Landsat, MODIS

Fig. 1

Topographic map of the study area"

Table 1

The growth cycle of winter wheat in Henan province"

月份Month 09 10 11 12 01 02 03 04 05 06
旬
Ten days		 上
First		 中Middle 下
Last		 上First 中Middle 下Last 上First 中Middle 下Last 上First 中Middle 下Last 上First 中Middle 下Last 上First 中
Middle		 下
Last		 上
First		 中Middle 下Last 上
First		 中Middle 下
Last		 上
First		 中
Middle		 下
Last		 上
First		 中Middle 下
Last
冬小麦生
育期
Winter wheat growth period		 播种期
Sowing stage		 出苗-三叶期
Regreening stage		 分蘖期
Tillering stage		 越冬期
Over-wintering stage		 返青期
Re-greening stage		 起身拔节期
Rising stage		 孕穗-抽穗期
Heading stage		 开花期
Flowering stage		 灌浆乳熟期
Mature stage

Table 2

The details of selected Landsat 8 image"

年份 Year 日期 Date (M-D) 选用影像数据年份Year of image data selected 筛选云量 Cloud covered 数量 Count
2017-2018 09-15—11-15 2015-2017 <20% 70
12-01—03-25 2015-2018 <20% 133
2018-2019 09-15—11-15 2016-2018 <20% 83
12-01—03-25 2016-2019 <20% 125
2019-2020 09-15—11-15 2017-2019 <20% 94
12-01—03-25 2019-2020 <10% 36

Table 3

Basis for sample selection"

样本种类 Sample type 解译标志 Interpretation mark 描述 Description
冬小麦 Winter wheat 研究区域冬小麦主要分布在平原地带的农村周边,在Google earth纹理较为清晰,成片出现,有较为规则的形状（矩形）,颜色为绿色或深绿色
Winter wheat in the study area is mainly distributed in the rural periphery of the plain area, the texture of Google earth is relatively clear, appears in pieces, has a more regular shape (rectangle), and the color is green or dark green
水体 Water 研究区域的水体区域主要由水库、湖泊、河流等组成,纹理上边缘明显,在颜色上水体区域在颜色上的表现为青色、淡蓝以及土黄色（黄河）
The water area of the study area is mainly composed of reservoirs, lakes, rivers and so on, and the upper edge of the texture is obvious. In color, the water area is cyan, light blue and earth yellow
不透水面Town 研究区域内的不透水面有城镇建筑以及城镇周边道路组成,在Google earth上纹理信息较明显,也是成片出现,能够清晰的识别
The impervious water surface in the study area is composed of urban buildings and roads around towns, and the texture information is more obvious on Google earth, and it also appears in pieces, which can be clearly identified
其他植被
Other vegetation		 其他植被由山体植被、城镇中的景观植物以及裸地植被等组成。在Google earth上此类地物具有清晰的特征,山体植被海拔较高成山体状;景观植物分布在城镇生活区域中;裸地植被也分布于城镇各处,表面较为稀疏
Other vegetation is composed of mountain vegetation, urban landscape plants and bare land vegetation. On Google earth, such features have clear characteristics, mountain vegetation is mountain-shaped at high altitude; Landscape plants are distributed in urban living areas; Bare land vegetation is also distributed throughout cities and towns, and the surface is relatively sparse

Fig. 2

Monitoring process of winter wheat growth"

Fig. 3

Time series NDVI changes of various features in the study area"

Table 4

Comparison of sown area extracted by remote sensing and statistical sown area"

特征
Feature		 年份
Year		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)
光谱+地形+纹理+NDVI
Spectrum + terrain + texture + NDVI		 2017-2018 5930815 5714640 216175 3.78
2018-2019 6043211 5739850 303361 5.28
光谱+地形+纹理+NDVI+NDVI增幅
Spectrum+topography+texture+NDVI+ NDVIincrease		 2017-2018 5548529 5714640 166111 2.91
2018-2019 5604970 5739850 134670 2.35
2019-2020 5843632

Table 5

Comparison between the sown area extracted by remote sensing and the statistical sown area in the main planting areas of winter wheat in Henan province"

特征 Feature 2017-2018 2018-2019
光谱+地形+纹理+NDVI
Spectrum + topography + texture + NDVI		 市
City		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)		 提取面积
Extraction area (hm2)		 统计面积
Statistical area (hm2)		 绝对误差
Absolute error (hm2)		 相对误差
Relative error (%)
南阳 Nanyang 803278 709020 94258 13.29 771926 724660 47266 4.09
驻马店 Zhumadian 929103 750520 178583 23.79 914174 781670 132504 16.95
周口 Zhoukou 846381 718350 128031 17.82 893891 734380 159511 21.72
商丘 Shangqiu 611644 588870 22774 3.87 705349 603380 101969 16.90
开封 Kaifeng 330408 299920 30488 10.16 322177 305200 16977 5.50
许昌 Xuchang 238458 228960 9498 4.15 226581 233010 6429 2.76
新乡 Xinxiang 442132 379410 62722 16.53 437147 387670 49477 12.76
安阳 Anyang 307150 320380 13230 7.22 305831 325730 19539 6.00
光谱+地形+纹理+ NDVI+NDVI增幅
Spectrum + topography +texture+NDVI+
NDVIincrease		 南阳 Nanyang 721819 709020 12799 1.81 695001 724660 29659 4.09
驻马店 Zhumadian 849960 750520 9944 13.25 834591 781670 52921 6.77
周口 Zhoukou 809734 718350 91384 12.72 808415 734380 74035 10.08
商丘 Shangqiu 575660 588870 13210 2.24 629586 603380 26206 4.34
开封 Kaifeng 300274 299920 354 0.12 316275 305200 11075 3.63
许昌 Xuchang 230127 228960 1167 0.51 224240 233010 8770 3.76
新乡 Xinxiang 427995 379410 485.85 12.80 41784 387670 30170 7.78
安阳 Anyang 313655 320380 6725 2.10 304353 325730 11377 3.49

Table 6

Comparison of extraction accuracy of winter wheat based on confusion matrix in the study area"

特征
Feature		 年份
Year		 总体精度
OA (%)		 Kappa系数
Kappa		 冬小麦分类精度
Classification accuracy of winter wheat (%)
光谱+地形+纹理+NDVI
Spectrum + topography + texture + NDVI		 2017-2018 93.3 0.862 93.5
2018-2019 94.8 0.891 95.1
2019-2020 94.7 0.892 94.5
光谱+地形+纹理+NDVI+NDVI增幅
Spectrum + topography + texture + NDVI+NDVIincrease		 2017-2018 94.8 0.893 94.6
2018-2019 95.6 0.921 95.3
2019-2020 95.8 0.913 95.9

Fig. 4

Distribution map of sown area of winter wheat in the study area from 2017 to 2020"

Fig. 5

NDVI changes of winter wheat planting areas in 2020 compared with previous years"

Fig. 6

The growth situation of winter wheat in the study area from February to April in 2020 compared with the usual year"

Fig. 7

The growth of winter wheat in the study area from February to April in 2020 compared with previous years"

Fig. 8

The growth distribution of winter wheat in the study area from February to April in 2020 compared with previous years"

Fig. 9

Mean temperature and sunshine from February to April in 2017-2020"

[1] 郑文倩. 我国小麦价格形成机制及波动特征分析[D]. 北京: 中国农业科学院, 2016.
ZHENG W Q. Study on wheat price formation mechanism and price volatility characteristics in China[D]. Beijing: Chinese Academy of Agricultural Sciences, 2016. (in Chinese)
[2] 盛磊, 何亚娟, 吴全, 王飞. 河南省冬小麦产量遥感监测精度比较研究. 中国农业信息, 2018,30(2):95-102.
SHENG L, HE Y J, WU Q, WANG F. Comparative study on accuracy of winter wheat production by remote sensing monitoring in Henan province. China Agricultural Information, 2018,30(2):95-102. (in Chinese)
[3] HAN J C, ZHANG Z, CAO J, LUO Y C, ZHANG L L, LI Z Y, ZHANG J. Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sensing, 2020,12(2):236.
doi: 10.3390/rs12020236
[4] 刘新杰, 魏云霞, 焦全军, 孙奇, 刘良云. 基于时序定量遥感的冬小麦长势监测与估产研究. 遥感技术与应用, 2019,34(4):756-765.
LIU X J, WEI Y X, JIAO Q J, SUN Q, LIU L Y. Growth monitoring and yield prediction of winter wheat based on time-series quantitative remote sensing data. Remote Sensing Technology and Application, 2019,34(4):756-765. (in Chinese)
[5] 杨珺雯, 张锦水, 潘耀忠, 孙佩军, 朱爽. 基于遥感识别误差校正面积的农作物种植面积抽样高效分层指标研究——以冬小麦为例. 中国农业科学, 2018,51(4):675-687.
YANG J W, ZHANG J S, PAN Y Z, SUN P J, ZHU S. An efficient hierarchical indicator based on the correction area of remote sensing identification error for planting acreage sampling-A case study of winter wheat. Scientia Agricultura Sinica, 2018,51(4):675-687. (in Chinese)
[6] FRANCH B, VERMOTE E F, SKAKUN S, ROGER J C, BECKER- RESHEF I, MURPHY E, JUSTICE C. Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine. International Journal of Applied Earth Observation and Geoinformation, 2019,76:112-127.
doi: 10.1016/j.jag.2018.11.012
[7] HAO Z, ZHAO H L, ZHANG C, WANG H, JIANG Y, YI Z. Estimating winter wheat area based on an SVM and the variable fuzzy set method. Remote Sensing Letters, 2019,10(4):343-352.
doi: 10.1080/2150704X.2018.1552811
[8] ZHANG X W, LIU J F, QIN Z Y, QIN F. Winter wheat identification by integrating spectral and temporal information derived from multi- resolution remote sensing data. Journal of Integrative Agriculture, 2019,18(11):2628-2643.
doi: 10.1016/S2095-3119(19)62615-8
[9] MENG S Y, ZHONG Y F, LUO C, HU X, WANG X Y, HUANG S X. Optimal temporal window selection for winter wheat and wapeseed mapping with sentinel-2 images: A case study of Zhongxiang in China. Remote Sensing, 2020,12(2):226.
doi: 10.3390/rs12020226
[10] 谭昌伟, 罗明, 杨昕, 马昌, 严翔, 周健, 杜颖, 王雅楠. 用PLS算法由HJ-1A/1B遥感影像估测区域冬小麦理论产量. 中国农业科学, 2015,48(20):4033-4041.
TAN C W, LUO M, YANG X, MA C, YAN X, ZHOU J, DU Y, WANG Y N. Remote sensing estimation of winter wheat theoretical yield on regional scale using partial least squares regression algorithm based on HJ-1A/1B images. Scientia Agricultura Sinica, 2015,48(20):4033-4041. (in Chinese)
[11] DONG C, ZHAO G X, QIN Y W, WAN H. Area extraction and spatiotemporal characteristics of winter wheat-summer maize in Shandong province using NDVI time series. PLoS ONE, 2019,14(12):226508.
[12] 王利民, 刘佳, 姚保民, 季富华, 杨福刚. 基于GF-1影像NDVI年度间相关分析的冬小麦面积变化监测. 农业工程学报, 2018,34(8):184-191.
WANG L M, LIU J, YAO B M, JI F H, YANG F G. Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years. Transactions of the Chinese Society of Agricultural Engineering, 2018,34(8):184-191. (in Chinese)
[13] 申健, 常庆瑞, 李粉玲, 王力. 基于时序NDVI的关中地区冬小麦种植信息遥感提取. 农业机械学报, 2017,48(3):215-220, 260.
SHEN J, CHANG Q R, LI F L, WANG L. Extraction of winter wheat information based on time-series NDVI in Guanzhong area. Transactions of the Chinese Society for Agricultural Machinery, 2017,48(3):215-220, 260. (in Chinese)
[14] 安塞, 沈彦俊, 赵彦茜, 郭英, 郭硕, 肖捷颖. 基于NDVI时间序列数据的冬小麦种植面积提取. 江苏农业科学, 2019,47(15):236-240.
AN S, SHEN Y J, ZHAO Y Q, GUO Y, GUO S, XIAO J Y. Extraction of winter wheat planting area based on NDVI time series data. Jiangsu Agricultural Science, 2019,47(15):236-240. (in Chinese)
[15] SHAN J, WANG Z M, SUN L, QIU L, YU K, WNAG J G. Study on extraction methods of winter wheat area based on GF-1 satellite images//2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE, 2019: 1-5.
[16] 罗桓, 李卫国, 景元书, 徐向华, 陈华. 基于SVM的县域冬小麦种植面积遥感提取. 麦类作物学报, 2019,39(4):81-88.
LUO H, LI W G, JING Y S, XU X H, CHEN H. Remote sensing extraction of winter wheat planting area based on SVM. Journal of Triticeae Crops, 2019,39(4):81-88. (in Chinese)
[17] 李旭青, 刘世盟, 李龙, 金永涛, 范文磊, 吴怜. 基于RF算法优选多时相特征的冬小麦空间分布自动解译. 农业机械学报, 2019,50(6):218-225.
LI X Q, LIU S M, LI L, JIN Y T, FAN W L, WU L. Automatic interpretation of spatial distribution of winter wheat based on random forest algorithm to optimize multi-temporal features. Transactions of the Chinese Society for Agricultural Machinery, 2019,50(6):218-225. (in Chinese)
[18] 贺原惠子, 王长林, 贾慧聪, 陈方. 基于随机森林算法的冬小麦提取研究. 遥感技术与应用, 2018,33(6):1132-1140.
HE Y H Z, WANG C L, JIA H C, CHEN F. Research on extraction of winter wheat based on random forest. Remote Sensing Technology and Application, 2018,33(6):1132-1140. (in Chinese)
[19] LIU J T, FENG Q L, GONG J H, ZHOU J P, LIANG J M, LI Y. Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data. International Journal of Digital Earth, 2018,11(8):783-802.
doi: 10.1080/17538947.2017.1356388
[20] 周志华. 机器学习. 北京: 清华大学出版社, 2016: 179-180.
ZHOU Z H. Machine Learning. Beijing: Tsinghua University Press, 2016: 179-180. (in Chinese)
[21] YOU J, PEI Z Y, WANG F, WU Q, GUO L. Area extraction of winter wheat at county scale based on modified multivariate texture and G satellite images. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(13):131-139.
[22] 杨蕙宇, 王征强, 白建军, 韩红珠. 基于多特征提取与优选的冬小麦面积提取. 陕西师范大学学报(自然科学版), 2020,48(1):40-49.
YANG H Y, WANG Z Q, BAI J J, HAN H Z. Winter wheat area extraction based on multi-feature extraction and feature selection. Journal of Shaanxi Normal University(Natural Science Edition), 2020,48(1):40-49. (in Chinese)
[23] 何昭欣, 张淼, 吴炳方, 邢强. Google Earth Engine支持下的江苏省夏收作物遥感提取. 地球信息科学学报, 2019,21(5):752-766.
doi: 10.12082/dqxxkx.2019.180420
HE Z X, ZHANG M, WU B F, XING Q. Extraction of summer crop in Jiangsu based on Google Earth Engine. Journal of Geo-information Science, 2019,21(5):752-766. (in Chinese)
doi: 10.12082/dqxxkx.2019.180420
[24] GORELICK N, HANCHER M, DIXON M, ILYUSHCHENKO S, THAU D, MOORE R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 2017,202:18-27.
doi: 10.1016/j.rse.2017.06.031
[25] 朱德海, 刘逸铭, 冯权泷, 欧聪, 郭浩, 刘建涛. 基于GEE的山东省近30年农业大棚时空动态变化研究. 农业机械学报, 2020,51(1):168-175.
ZHU D H, LIU Y M, FENG Q L, OU C, GUO H, LIU J T. Spatial- temporal dynamic changes of agricultural greenhouses in Shandong province in recent 30 years based on Google Earth Engine. Transactions of the Chinese Society for Agricultural Machinery, 2020,51(1):168-175. (in Chinese)
[26] LIU X P, HU G H, CHEN Y M, LI X, XU X C, LI S Y, PEI F S, WANG S J. High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote sensing of environment, 2018,209:227-239.
doi: 10.1016/j.rse.2018.02.055
[27] 郝斌飞, 韩旭军, 马明国, 刘一韬, 李世卫. Google Earth Engine在地球科学与环境科学中的应用研究进展. 遥感技术与应用, 2018,33(4):600-611.
HAO B F, HAN X J, MA M G, LIU Y T, LI S W. Research progress on the application of Google Earth Engine in geoscience and environmental sciences. Remote Sensing Technology and Application, 2018,33(4):600-611. (in Chinese)
[28] 孙丽, 王蔚丹, 陈媛媛, 董沫. 2019年美国冬小麦长势遥感监测分析. 安徽农业科学, 2020,48(1):241-244.
SUN L, WANG W D, CHEN Y Y, DONG M. Analysis of winter wheat growth of united states with remote sensing data in 2019. Journal of Anhui Agricultural Sciences, 2020,48(1):241-244. (in Chinese)
[29] 黄青, 李丹丹, 陈仲新, 刘佳, 王利民. 基于MODIS数据的冬小麦种植面积快速提取与长势监测. 农业机械学报, 2012,43(7):163-167.
HUNAG Q, LI D D, CHEN Z X, LIU J, WANG L M. Monitoring of planting area and growth condition of winter wheat in China based on MODIS data. Transactions of the Chinese Society for Agricultural Machinery, 2012,43(7):163-167. (in Chinese)
[30] 何亚萍. 河南省循环农业发展研究[D]. 北京: 中国农业科学院, 2018.
HE Y P. Study on the development of circular agriculture in Henan province[D]. Beijing: Chinese Academy of Agricultural Sciences, 2018. (in Chinese)
[31] FARR T G, ROSEN P A, CARO E. The shuttle radar topography mission. Reviews of geophysics, 2007,45(2):1-13.
[32] ULABY F T, KOUYATE F, BRISCO B, WILLIAMS T L. Textural Infornation in SAR Images. Geoscience and Remote Sensing, IEEE Transactions on GE-24, 1986,24(2):235-245.
[33] 王九中, 田海峰, 邬明权, 王力, 王长耀. 河南省冬小麦快速遥感制图. 地球信息科学学报, 2017,19(6):846-853.
doi: 10.3724/SP.J.1047.2017.00846
WANG J Z, TIAN H F, WU M Q, WANG L, WANG C Y. Rapid mapping of winter wheat in Henan province. Journal of Geo- information Science, 2017,19(6):846-853. (in Chinese)
doi: 10.3724/SP.J.1047.2017.00846
[34] BREIMAN L. Random forests. Machine Learning, 2001,45(1):5-32.
doi: 10.1023/A:1010933404324
[35] 王利民, 刘佳, 唐鹏钦, 姚保民, 刘荣高. 农作物长势遥感监测需求、系统框架及业务应用. 中国农业信息, 2019,31(2):1-10.
WANG L M, LIU J, TANG P Q, YAO B M, LIU R G. Demands, system framework, and operational application of crop growth remote sensing monitoring. China Agricultural Information, 2019,31(2):1-10. (in Chinese)
[36] 千怀遂. 农作物遥感估产最佳时相的选择研究——以中国主要粮食作物为例. 生态学报, 1998,18(1):48-55 .
QIAN H S. Selection of the optimum temporal for crop estimation using remote sensing data-Main food crops in China. Acta Ecologica Sinica, 1998,18(1):48-55. (in Chinese)
[37] 李方杰, 任建强, 吴尚蓉, 陈仲新, 张宁丹. 河南省冬小麦种植频率时空变化及影响因素分析. 中国农业科学, 2020,53(9):1773-1794.
LI F J, REN J Q, WU S R, CHEN Z X, ZHANG N D. Spatio-temporal variations of winter wheat planting frequency and their analysis of influencing factors in Henan province. Scientia Agricultura Sinica, 2020,53(9):1773-1794. (in Chinese)
[38] 邓荣鑫, 王文娟, 魏义长, 张富, 李春静, 刘文玉. 河南省冬小麦种植面积遥感监测及其时空特征研究. 灌溉排水学报, 2019,38(9):49-54.
DENG R X, WANG W Q, WEI Y C, ZHANG F, LI C J, LIU W Y. Remote estimation of winter wheat area and its spatio-temporal characteristics in Henan province. Journal of Irrigation and Drainage, 2019,38(9):49-54. (in Chinese)
[1] WEI YongKang, YANG TianCong, ZANG ShaoLong, HE Li, DUAN JianZhao, XIE YingXin, WANG ChenYang, FENG Wei. Monitoring Wheat Lodging Based on UAV Multi-Spectral Image Feature Fusion [J]. Scientia Agricultura Sinica, 2023, 56(9): 1670-1685.
[2] MA ShengLan, KUANG FuHong, LIN HongYu, CUI JunFang, TANG JiaLiang, ZHU Bo, PU QuanBo. Effects of Straw Incorporation Quantity on Soil Physical Characteristics of Winter Wheat-Summer Maize Rotation System in the Central Hilly Area of Sichuan Basin [J]. Scientia Agricultura Sinica, 2023, 56(7): 1344-1358.
[3] CHANG ChunYi, CAO Yuan, GHULAM Mustafa, LIU HongYan, ZHANG Yu, TANG Liang, LIU Bing, ZHU Yan, YAO Xia, CAO WeiXing, LIU LeiLei. Effects of Powdery Mildew on Photosynthetic Characteristics and Quantitative Simulation of Disease Severity in Winter Wheat [J]. Scientia Agricultura Sinica, 2023, 56(6): 1061-1073.
[4] WANG XiaoXuan, ZHANG Min, ZHANG XinYao, WEI Peng, CHAI RuShan, ZHANG ChaoChun, ZHANG LiangLiang, LUO LaiChao, GAO HongJian. Effects of Different Varieties of Phosphate Fertilizer Application on Soil Phosphorus Transformation and Phosphorus Uptake and Utilization of Winter Wheat [J]. Scientia Agricultura Sinica, 2023, 56(6): 1113-1126.
[5] GUO Yan, JING YuHang, WANG LaiGang, HUANG JingYi, HE Jia, FENG Wei, ZHENG GuoQing. UAV Multispectral Image-Based Nitrogen Content Prediction and the Transferability Analysis of the Models in Winter Wheat Plant [J]. Scientia Agricultura Sinica, 2023, 56(5): 850-865.
[6] LIN JiangYun, YIN BenSu, WANG XingShu, LIU ChenRui, SUN Qing, XIE XingXing, CHENG LingLing, SUN LiWei, SHI Mei, WANG ZhaoHui. The Accumulation of Iron and Manganese in Wheat and Its Relationship with Soil Nutrients Under Long-Term Application of Nitrogen Fertilizer [J]. Scientia Agricultura Sinica, 2023, 56(17): 3372-3382.
[7] MU HaiMeng, SUN LiFang, WANG ZhuangZhuang, WANG Yu, SONG YiFan, ZHANG Rong, DUAN JianZhao, XIE YingXin, KANG GuoZhang, WANG YongHua, GUO TianCai. Effect of Nitrogen Application Rate and Planting Density on the Lodging Resistance and Grain Yield of Two Winter Wheat Varieties [J]. Scientia Agricultura Sinica, 2023, 56(15): 2863-2879.
[8] DONG YiFan, REN Yi, CHENG YuKun, WANG Rui, ZHANG ZhiHui, SHI XiaoLei, GENG HongWei. Genome-Wide Association Study of Grain Main Quality Related Traits in Winter Wheat [J]. Scientia Agricultura Sinica, 2023, 56(11): 2047-2063.
[9] LÜ LiHua, HAN JiangWei, ZHANG JingTing, DONG ZhiQiang, MENG Jian, JIA XiuLing. Analysis of Common Characteristics of Widely Adaptation Wheat Cultivars [J]. Scientia Agricultura Sinica, 2023, 56(11): 2064-2077.
[10] WANG YangYang,LIU WanDai,HE Li,REN DeChao,DUAN JianZhao,HU Xin,GUO TianCai,WANG YongHua,FENG Wei. Evaluation of Low Temperature Freezing Injury in Winter Wheat and Difference Analysis of Water Effect Based on Multivariate Statistical Analysis [J]. Scientia Agricultura Sinica, 2022, 55(7): 1301-1318.
[11] YI YingJie, HAN Kun, ZHAO Bin, LIU GuoLi, LIN DianXu, CHEN GuoQiang, REN Hao, ZHANG JiWang, REN BaiZhao, LIU Peng. The Comparison of Ammonia Volatilization Loss in Winter Wheat- Summer Maize Rotation System with Long-Term Different Fertilization Measures [J]. Scientia Agricultura Sinica, 2022, 55(23): 4600-4613.
[12] LIU Feng,JIANG JiaLi,ZHOU Qin,CAI Jian,WANG Xiao,HUANG Mei,ZHONG YingXin,DAI TingBo,CAO WeiXing,JIANG Dong. Analysis of American Soft Wheat Grain Quality and Its Suitability Evaluation According to Chinese Weak Gluten Wheat Standard [J]. Scientia Agricultura Sinica, 2022, 55(19): 3723-3737.
[13] YANG JingYa,HU Qiong,WEI HaoDong,CAI ZhiWen,ZHANG XinYu,SONG Qian,XU BaoDong. Consistency Analysis of Classification Results for Single and Double Cropping Rice in Southern China Based on Sentinel-1/2 Imagery [J]. Scientia Agricultura Sinica, 2022, 55(16): 3093-3109.
[14] HAN ShouWei,SI JiSheng,YU WeiBao,KONG LingAn,ZHANG Bin,WANG FaHong,ZHANG HaiLin,ZHAO Xin,LI HuaWei,MENG Yu. Mechanisms Analysis on Yield Gap and Nitrogen Use Efficiency Gap of Winter Wheat in Shandong Province [J]. Scientia Agricultura Sinica, 2022, 55(16): 3110-3122.
[15] GAO RenCai, CHEN SongHe, MA HongLiang, MO Piao, LIU WeiWei, XIAO Yun, ZHANG Xue, FAN GaoQiong. Straw Mulching from Autumn Fallow and Reducing Nitrogen Application Improved Grain Yield, Water and Nitrogen Use Efficiencies of Winter Wheat by Optimizing Root Distribution [J]. Scientia Agricultura Sinica, 2022, 55(14): 2709-2725.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd