欢迎访问作物学报，今天是
作物学报

作物学报 ›› 2021, Vol. 47 ›› Issue (12): 2532-2540.doi: 10.3724/SP.J.1006.2021.11002

• 研究简报 • 上一篇    

高分六号遥感影像植被特征及其在冬小麦苗期LAI反演中的应用

张矞勋1,2(), 齐拓野4, 孙源3, 璩向宁1,2, 曹媛1,2, 吴梦瑶1,2, 刘春虹1,2, 王磊1,2,*()   

  1. 1宁夏大学西北土地退化与生态系统恢复省部共建国家重点实验室培育基地, 宁夏银川 750021
    2宁夏大学西北退化生态系统恢复与重建教育部重点实验室, 宁夏银川 750021
    3中国科学院遥感与数字地球研究所, 北京 100101
    4宁夏大学生态环境学院, 宁夏银川 750021
  • 收稿日期:2021-01-06 接受日期:2021-04-26 出版日期:2021-12-12 网络出版日期:2021-06-15
  • 通讯作者: 王磊
  • 作者简介:E-mail: 17839221635@163.com
  • 基金资助:
    “西部之光”人才培养计划“西部青年学者”A类项目(XAB2017AW06);国家民用空间基础设施陆地观测卫星共性应用支撑平台(Y930280A2F);国家自然科学基金项目(31760707);宁夏回族自治区西部一流学科建设项目(NXYLXK2017B06)

Vegetation characteristics of GF-6 remote sensing image and application on LAI retrieval of winter wheat at seedling stage

ZHANG Yu-Xun1,2(), QI Tuo-Ye4, SUN Yuan3, QU Xiang-Ning1,2, CAO Yuan1,2, WU Meng-Yao1,2, LIU Chun-Hong1,2, WANG Lei1,2,*()   

  1. 1Breeding Base for State Key Laboratory of Land Degradation and Ecosystem Restoration in Northwest China, Ningxia University, Yinchuan 750021, Ningxia, China
    2Key Laboratory for Restoration and Reconstruction of Degenerated Ecosystem in Northwest China under Ministry of Education, Ningxia University, Yinchuan 750021, Ningxia, China
    3Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, Beijing, 100101, China
    4School of Ecology and Environment, Ningxia University, Yinchuan 750021, Ningxia, China
  • Received:2021-01-06 Accepted:2021-04-26 Published:2021-12-12 Published online:2021-06-15
  • Contact: WANG Lei
  • Supported by:
    West Light Foundation of the Chinese Academy of Sciences for Young Scholars in Western China A-class-project(XAB2017AW06);Common Application Support Platform for Land Observation Satellite of National Civil Space Infrastructure(Y930280A2F);National Natural Science Foundation of China(31760707);Ningxia Higher Education First-Class Discipline Construction Fund(NXYLXK2017B06)

RichHTML

37

PDF

1181

摘要：

高分六号(GF-6)遥感卫星是中国首颗精准农业观测的高分卫星, 与高分一号(GF-1)组网运行, 除具有与GF-1 WFV传感器相同的波段外, 国内首次增加了能够有效反映作物特有光谱特性的红边波段。为评价高分六号卫星影像的农作物监测能力, 以苗期冬小麦为研究对象, 结合地面同步观测的冠层光谱和LAI实测数据, 分析高分六号卫星影像的波段数量、波段光谱范围及新增的红边植被波段特征; 通过提取GF-6 WFV影像中的反射率数据构建植被指数, 借助人工神经网络, 对比GF-6 WFV传感器不同植被指数组合构建反演模型的精度, 以此探究GF-6 WFV红边波段在冬小麦苗期叶面积指数反演的应用能力。结果表明, 高分六号遥感影像能较好反映真实的植被特征; 同时在对冬小麦LAI反演时增加GF-6 WFV传感器的两个红边波段及红边植被指数数据, 对苗期冬小麦LAI反演模型精度有较大的提高, R2分别调高12.48%, RMSE降低14.75%。

关键词: 高分六号, 红边参数, 叶面积指数反演, 冬小麦, BP神经网络

Abstract:

As Chinese first high-resolution satellite for precision agricultural observations, the GF-6 remote sensing satellite operates in a network with GF-1. In addition to having the same waveband as the GF-1 WFV sensor, red-edge band is added to the Chinese satellite firstly, which can effectively reflect the unique spectral characteristics of the crop. In order to evaluate the monitoring capabilities of the GF-6 satellite imagery for crops, the seedling stage of winter wheat was selected as the research object. Combined with the ground synchronous observation canopy spectrum and the LAI measured data, we analyzed the quantity of bands, the band spectrum and the features of the added red edge vegetation band of the GF-6 satellite image. Furthermore, we constructed vegetation indices by extracting reflectance data from GF-6 remote sensing images and made comparison between the inversion accuracy of the model established by the combination of different wavebands of GF-6 WFV sensor with the help of artificial neural network. Finally, the application ability of GF-6 WFV red edge band in inversing LAI of winter wheat at seeding stage was explored. The results showed that the GF-6 remote sensing image reflected the characteristics of vegetation more realistically. When inverting the winter wheat LAI, the two red-edge bands and the red-edge vegetation index data of the GF-6 WFV sensor were added, which greatly improved the accuracy of the winter wheat LAI inversion model at seedling stage, with the increased R2 of 12.48% and the decreased RMSE of 14.75%.

Key words: GF-6, red edge parameter, LAI inversion, winter wheat, BP neural network

图1

研究区地理位置与样地分布图 GF-6 WFV传感器4、3、2波段合成。"

表1

植被指数及计算公式"

植被指数
Vegetation index		 名称
Name		 公式
Equation		 参考文献
Reference
NDVI 归一化植被指数
Normalized Difference Vegetation Index		 NDVI=(NIR-R)/(NIR+R) [30]
NDVI710 红边归一化植被指数710
Red Edge Normalized Vegetation Index 710		 NDVI710=(RE Ⅰ-R)/(NIR+R) [30]
NDVI750 红边归一化植被指数750
Red Edge Normalized Vegetation Index 750		 NDVI750=(REⅡ-R)/(NIR+R) [30]
ARVI 大气阻抗植被指数
Atmosphere Resistant Vegetation Index		 ARVI=(NIR-(2R-BLUE))/(NIR+(2R-BLUE)) [31]
SAVI 土壤调节植被指数
Soil-Adjusted Vegetation Index		 SAVI=(NIR-R)/(NIR+R+L)×(1+L) (L=0.5) [32]
CIgreen 叶绿素指数
Green chlorophyll index		 CIgreen=NIR/GREEN-1 [32]

表2

GF-1 WFV、GF-6 WFV、Landsat8-OLI影像对应波段参数对比"

传感器
Sensor		 空间分辨率
Spatial resolution (m)		 辐射分辨率
Radiometric
resolution (bit)		 蓝
Blue
(nm)		 绿
Green
(nm)		 红
Red
(nm)		 近红外
NIR
(nm)		 红边I
RE I
(nm)		 红边II
RE II
(nm)
GF-6 WFV 16 12 0.45-0.52 0.52-0.59 0.63-0.69 0.77-0.89 0.69-0.73 0.30-0.77
GF-1 WFV 16 10 0.45-0.52 0.52-0.59 0.63-0.69 0.77-0.89 — —
Landsat8-OLI 30 12 0.45-0.51 0.53-0.59 0.64-0.67 0.85-0.88 — —

图2

传感器光谱响应函数及冬小麦光谱曲线"

图3

植被指数与叶面积指数交互对比图"

表3

BP神经网络反演模型精度"

处理
Treatment		 精度Accuracy (R2)
训练样本
Training samples		 测试样本
Testing samples
组合1 Combination 1 0.7235 0.6250
组合2 Combination 2 0.7802 0.6862
组合3 Combination 3 0.7272 0.6338
组合4 Combination 4 0.7944 0.7126

图4

BP神经网络模型反演结果"

图5

BP神经网络模型反演精度"

[1] 毛博慧, 李民赞, 孙红, 刘豪杰, 张俊逸, Zhang Q. 冬小麦苗期叶绿素含量检测光谱学参数寻优. 农业工程学报, 2017, 33(增刊1):164-169.
Mao B H, Li M Z, Sun H, Liu H J, Zhang J J, Zhang Q. Optimization of spectroscopy parameters and prediction of chlorophyll content at seeding stage of winter wheat. Trans CSAE, 2017, 33(S1):164-169 (in Chinese with English abstract).
[2] 张领先, 陈运强, 李云霞, 马浚诚, 杜克明, 郑飞翔, 孙忠富. 可见光光谱的冬小麦苗期地上生物量估算. 光谱学与光谱分析, 2019, 39:2501-2506.
Zhang L X, Chen Y Q, Li Y X, Ma J C, Du K M, Zheng F X, Sun F Z. Estimating above ground biomass of winter wheat at early growth stages based on visual spectral. Spectr Spectr Anal, 2019, 39:2501-2506 (in Chinese with English abstract).
[3] 马培培, 李静, 柳钦火, 何彬彬, 赵静. 中国区域MuSyQ叶面积指数产品验证与分析. 遥感学报, 2019, 23:1232-1252.
Ma P P, Li J, Liu Q H, He B B, Zhao J. Multisensor synergistic quantitative leaf area index product of China. J Remote Sens, 2019, 23:1232-1252 (in Chinese with English abstract).
[4] 龙泽昊, 秦其明, 张添源, 许伟. 基于长短期记忆网络的冬小麦连续时序叶面积指数预测. 光谱学与光谱分析, 2020, 40:898-904.
Long Z H, Qin Q M, Zhang T Y, Xu W. Prediction of continuous time series leaf area index based on long short-term memory network: a case study of winter wheat. Spectr Spectr Anal, 2020, 40:898-904 (in Chinese with English abstract).
[5] 栾青, 郭建平, 马雅丽, 张丽敏, 王婧瑄. 玉米叶面积指数估算通用模型. 中国农业气象, 2020, 41:506-519.
Luan Q, Guo J P, Ma Y L, Zhang L M, Wang J X. A general model for estimating leaf area index of maize. Chin J Agrometeorol, 2020, 41:506-519 (in Chinese with English abstract).
[6] 李振洲, 贺正, 贾彪, 刘志, 付江鹏, 刘慧芳. 滴灌玉米叶面积指数归一化建模与特征分析. 中国土壤与肥料, 2020, (3):189-195.
Li Z Z, He Z, Jia B, Liu Z, Fu J P, Liu H F. LAI normalization modeling and characteristic analysis of maize with drip irrigation. Soil Fert Sci China, 2020, (3):189-195 (in Chinese with English abstract).
[7] 常文涛, 王浩, 宁晓刚, 张翰超. 融合Sentinel-2红边波段和Sentinel-1雷达波段影像的扎龙湿地信息提取. 湿地科学, 2020, 18(1):10-19.
Chang W T, Wang H, Ning X G, Zhang H C. Extraction of Zhalong wetlands information based on images of sentinel-2 red-edge bands and sentinel-1 radar bands. Wetland Sci, 2020, 18(1):10-19 (in Chinese with English abstract).
[8] 吾木提·艾山江, 买买提·沙吾提, 陈水森, 李丹. 基于GF-1/2卫星数据的冬小麦叶面积指数反演. 作物学报, 2020, 46:787-797.
Umut H, Mamat S, Chen S S, Li D. Inversion of leaf area index of winter wheat based on GF-1/2 image. Acta Agron Sin, 2020, 46:787-797 (in Chinese with English abstract).
[9] 刘明星, 李长春, 李振海, 冯海宽, 杨贵军, 陶惠林. 基于高光谱遥感与SAFY模型的冬小麦地上生物量估算. 农业机械学报, 2020, 51(2):192-202.
Liu M X, Li C C, Li Z H, Feng H K, Yang G J, Tao H L. Estimation of dry aerial mass of winter wheat based on coupled hyperspectral remote sensing and SAFY model. Trans CSAM, 2020, 51(2):192-202 (in Chinese with English abstract).
[10] 陶惠林, 徐良骥, 冯海宽, 杨贵军, 苗梦珂, 林博文. 基于无人机高光谱长势指标的冬小麦长势监测. 农业机械学报, 2020, 51(2):180-191.
Tao H L, Xu L J, Feng H K, Yang G J, Miao M K, Lin B W. Monitoring of winter wheat growth based on UAV hyperspectral growth index. Trans CSAM, 2020, 51(2):180-191 (in Chinese with English abstract).
[11] 王磊, 耿君, 杨冉冉, 田庆久, 杨闫君, 周洋. 高分一号卫星影像特征及其在草地监测中的应用. 草地学报, 2015, 23:1093-1100.
Wang L, Geng J, Yang R R, Tian Q J, Yang Y J, Zhou Y. Characteristics and application of GF-1 image in glassland monitoring. Acta Agrest Sin. 2015, 23:1093-1100 (in Chinese with English abstract).
[12] 张漫, 苗艳龙, 仇瑞承, 季宇寒, 李寒, 李民赞. 基于车载三维激光雷达的玉米叶面积指数测量. 农业机械学报, 2019, 50(6):12-21.
Zhang M, Miao Y L, Qiu R C, Ji Y H, Li H, Li M Z. Maize leaf area index measurement based on vehicle 3D LiDAR. Trans CSAM, 2019, 50(6):12-21 (in Chinese with English abstract).
[13] 程雪, 贺炳彦, 黄耀欢, 孙志刚, 李鼎, 朱婉雪. 基于无人机高光谱数据的玉米叶面积指数估算. 遥感技术与应用, 2019, 34:775-784.
Cheng X, He B Y, Huang Y H, Sun Z G, Li D, Zhu W X. Estimation of corn leaf area index based on UAV hyperspectral image. Remote Sens Technol Appl, 2019, 34:775-784 (in Chinese with English abstract).
[14] 江杰, 张泽宇, 曹强, 田永超, 朱艳, 曹卫星, 刘小军. 基于消费级无人机搭载数码相机监测小麦长势状况研究. 南京农业大学学报, 2019, 42:622-631.
Jiang J, Zhang Z Y, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Use of a digital camera mounted on a consumer-grade unmanned aerial vehicle to monitor the growth status of wheat. J Nanjing Agric Univ, 2019, 42:622-631 (in Chinese with English abstract).
[15] Xing N C, Huang W J, Xie Q Y, Shi Y, Ye H C, Dong Y Y, Wu M Q, Sun G, Jiao Q J. A transformed triangular vegetation index for estimating winter wheat leaf area index. Remote Sens, 2019, 12:16-34.
doi: 10.3390/rs12010016
[16] 孙红, 刘宁, 邢子正, 张智勇, 李民赞, 吴静珠. 马铃薯冠层光谱响应特征参数优化与生长期判别. 光谱学与光谱分析, 2019, 39:1870-1877.
Sun H, Liu N, Xing Z Z, Zhang Z Z, Li M Z, Wu J S. Parameter optimization of potato spectral response characteristics and growth stage identification. Spectr Spectr Anal, 2019, 39:1870-1877 (in Chinese with English abstract).
[17] 刘洁, 李静, 柳钦火, 何彬彬, 于文涛. 全球典型植被叶片光谱特征及其对LAI反演的影响分析. 遥感技术与应用, 2019, 34(1):155-165.
Liu J, Li J, Liu Q H, He B B, Yu W T. Global leaf spectral characteristics of typical vegetation and it’s impacts on LAI inversion. Remote Sens Technol Appl, 2019, 34(1):155-165 (in Chinese with English abstract).
[18] 葛元梅, 陈翔宇, 洪帅, 马露露, 吕新, 张泽. 基于红边参数不同品种的估算模型. 新疆农业科学, 2019, 56:1032-1040.
Ge Y M, Chen X Y, Hong S, Ma L L, Lyu X, Zhang Z. Establishment of estimation model for different varieties based on red edge parameters. Xinjiang Agric Sci, 2019, 56:1032-1040 (in Chinese with English abstract).
[19] 余蛟洋, 常庆瑞, 班松涛, 田明璐, 由明明. 猕猴桃叶片SPAD值高光谱估算模型构建. 干旱地区农业研究, 2018, 36(6):168-174.
Yu J Y, Chang Q R, Ban S T, Tian M L, You M M. Hyperspectral models for estimating SPAD values of kiwifruit leaves. Agric Res Arid Areas, 2018, 36(6):168-174 (in Chinese with English abstract).
[20] Jiang S, Wang F, Shen L M, Liao G P. Local detrended fluctuation analysis for spectral red-edge parameters extraction. Nonlin Dynam, 2018, 93:995-1008.
doi: 10.1007/s11071-018-4241-y
[21] 罗丹, 常庆瑞, 齐雁冰. 基于红边参数和人工神经网络的苹果叶片叶绿素含量估算. 西北农林科技大学学报(自然科学版), 2019, 47(1):107-115.
Luo D, Chang Q R, Qi Y B. Estimation of chlorophyll content in apple leaves based on red edge parameters and artificial neural network. J Northwest A&F Univ (Nat Sci Edn), 2019, 47(1):107-115 (in Chinese with English abstract).
[22] 刘俊, 孟庆岩, 葛小三, 刘顺喜, 陈旭, 孙云晓. 基于BP神经网络的夏玉米多生育期叶面积指数反演研究. 遥感技术与应用, 2020, 35(1):174-184.
Liu J, Meng Q Y, Ge X S, Liu S X, Chen X, Sun Y X. Leaf area index inversion of summer maize at multiple growth stages based on BP neural network. Remote Sens Technol Appl, 2020, 35(1):174-184 (in Chinese with English abstract).
[23] Asha V, Anil K, Priyadarshi U. Effect of red-edge region in fuzzy classification: a case study of sunflower crop. J Indian Soc Remote Sens, 2020, 48:645-657.
doi: 10.1007/s12524-020-01109-4
[24] 李雪玲, 董莹莹, 朱溢佞, 黄文江. 基于EnMAP卫星和深度神经网络的LAI遥感反演方法. 红外与毫米波学报, 2020, 39(1):111-119.
Li X L, Dong Y Y, Zhu Y N, Huang W J. Leaf area index estimation with EnMAP hyperspectral data based on deep neural network. J Infr Millim Waves, 2020, 39(1):111-119 (in Chinese with English abstract).
[25] 王强, 舒清态, 罗洪斌, 王冬玲, 字李, 谢福明. 基于机载LiDAR和光学遥感数据的热带橡胶林叶面积指数反演. 西北林学院学报, 2020, 35(4):132-139.
Wang Q, Shu Q T, Luo H B, Wang D L, Zi L, Xie F M. Inversion of leaf area index of tropical Hevea brasiliensis forest based on airborne LiDAR and optical remote sensing data. J Northwest For Univ, 2020, 35(4):132-139 (in Chinese with English abstract).
[26] 郭云开, 许敏, 张晓炯, 刘雨玲. 结合PRO-4SAIL和BP神经网络的叶绿素含量高光谱反演. 测绘通报, 2020, (3):21-24.
Guo Y K, Xu M, Zhang X J, Liu Y L. Chlorophyll hyperspectral inversion with PRO-4SAIL and BP neural networks. Bull Surv Map, 2020, (3):21-24 (in Chinese with English abstract).
[27] 陈士城, 王宝水. 基于神经网络的LAI遥感反演影响因素分析. 地理空间信息, 2019, 17(12):72-74.
Chen S C, Wang B S. Analysis of influence factors for LAI inversion based on neural network. Geosp Inf, 2019, 17(12):72-74 (in Chinese with English abstract).
[28] 赵磊. 东营市土地资源遥感综合评价. 遥感信息, 2008, (1):82-86.
Zhao L. An integrated assessment of land resources in Dongying city by remote sensing. Remote Sens Inf, 2008, (1):82-86 (in Chinese with English abstract).
[29] Wang L, Yang R R, Tian Q J, Yang Y J, Zhou Y, Sun Y, Mi X F. Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications. Remote Sens, 2015, 7:2089-2108.
doi: 10.3390/rs70202089
[30] 梁继, 郑镇炜, 夏诗婷, 张晓彤, 唐媛媛. 高分六号红边特征的农作物识别与评估. 遥感学报, 2020, 24:1168-1179.
Liang J, Zheng Z W, Xia S T, Zhang X T, Tang Y Y. Crop recognition and evaluation-using red edge features of GF-6 satellite. J Remote Sens, 2020, 24:1168-1179 (in Chinese with English abstract).
[31] 张仁华, 饶农新, 廖国男. 植被指数的抗大气影响探讨. 植物学报, 1996, 38(1):53-62.
Zhang R H, Rao N X, Liao G N. Approach for a vegetation index resistant to atmospheric effect. J Integr Plant Biol, 1996, 38(1):53-62 (in Chinese with English abstract).
[32] Ma Y, Fang S H, Peng Y, Gong Y, Wang D. Remote estimation of biomass in winter oilseed rape ( Brassica napus L.) using canopy hyperspectral data at different growth stages. Appl Sci, 2019, 9:545-563.
doi: 10.3390/app9030545
[33] 吴晓萍, 徐涵秋, 蒋乔灵. GF-1、GF-2与Landsat-8卫星多光谱数据的交互对比. 武汉大学学报(信息科学版), 2020, 45(1):150-158.
Wu X P, Xu H Q, Jiang Q L. Cross-comparison of GF-1, GF-2 and Landsat-8 OLI sensor data. Geom Inf Sci Wuhan Univ, 2020, 45(1):150-158 (in Chinese with English abstract).
[34] 郭建茂, 王星宇, 李淑婷, 谢晓燕, 刘荣花, 于庚康. 基于冠层光谱红边参数和植被指数的冬小麦水分胁迫监测. 江苏农业科学, 2019, 47(10):88-94.
Guo J M, Wang X Y, Li S T, Xie X Y, Liu R H, Yu K K. Study on water stress monitoring of winter wheat based on canopy spectral red-edge parameters and vegetation index. Jiangsu Agric Sci, 2019, 47(10):88-94 (in Chinese with English abstract).
[35] 张沁雨, 李哲, 夏朝宗, 陈健, 彭道黎. 高分六号遥感卫星新增波段下的树种分类精度分析. 地球信息科学学报, 2019, 21:1619-1628.
doi: 10.12082/dqxxkx.2019.190116
Zhang Q Y, Li Z, Xia C Z, Chen J, Peng D L. Tree species classification based on the new bands of GF-6 remote sensing satellite. J Geo-inf Sci, 2019, 21:1619-1628 (in Chinese with English abstract).
[36] 李文杰, 郭晓雷, 杨玲波, 闫鸣, 邹晨曦, 方亚华, 孙涵, 黄敬峰. 基于GF-6卫星影像多特征优选的酿酒葡萄精准识别. 农业工程学报, 2020, 36(18):165-173.
Li W J, Guo X L, Yang L B, Yan M, Zou C X, Fang Y H, Sun H, Huang J F. Accurate recognition of wine grapes using multi-feature optimization based on GF-6 satellite images. Trans CSAE, 2020, 36(18):165-173 (in Chinese with English abstract).
[37] Darvishzadeh R, Atzberger C, Skidmore A K, Abkar A A. Leaf Area Index derivation from hyperspectral vegetation indices and the red edge position. Int J Remote Sens, 2009, 30:6199-6218.
doi: 10.1080/01431160902842342
[38] 郑踊谦, 董恒, 张城芳, 黄鹏. 植被指数与作物叶面积指数的相关关系研究. 农机化研究, 2019, 41(10):1-6.
Zheng Y Q, Dong H, Zhang C F, Huang P. Study on the relationship between vegetation indices and leaf area index of crop. J Agric Mech Res, 2019, 41(10):1-6 (in Chinese with English abstract).
[39] 徐卫星, 薛华柱, 靳华安, 李爱农. 融合遥感先验信息的叶面积指数反演. 遥感技术与应用, 2019, 34:1235-1244.
Xu W X, Xue H Z, Jin H A, Li A N. Retrieval of leaf area index by fusing prior information from remote sensing data. Remote Sens Technol Appl, 2019, 34:1235-1244 (in Chinese with English abstract).
[1] 王壮壮, 武紫君, 张永新, 张芯源, 袁丽雪, 陈如雪, 刘世举, 段剑钊, 冯伟, 王同朝, 王永华. 豫东南黏壤潮土区水氮优化协同提高冬小麦产量和氮素利用效率[J]. 作物学报, 2026, 52(5): 1501-1521.
[2] 乔宇馨, 李程越, 康晓玉, 张鑫琪, 贾绍辉, 刘倩, 曹亚丽, 史鑫蕊, 郝兴宇, 李萍. 基于APSIM模型的长期免耕秸秆覆盖对旱地小麦增产效应研究[J]. 作物学报, 2026, 52(4): 1181-1192.
[3] 吴柳格, 陈坚, 张鑫, 邓艾兴, 宋振伟, 郑成岩, 张卫建. 近二十年国审冬小麦品种的产量与品质性状变化趋势研究[J]. 作物学报, 2025, 51(7): 1814-1826.
[4] 陈如雪, 孙丽芳, 张芯源, 牟海萌, 张永新, 袁丽雪, 彭仕乐, 王壮壮, 王永华. 秸秆还田与微生物菌剂配施对冬小麦旗叶碳氮代谢及产量形成的影响[J]. 作物学报, 2025, 51(7): 1901-1913.
[5] 赵刚, 张建军, 党翼, 樊廷录, 王磊, 周刚, 王淑英, 李兴茂, 倪胜利, 米文博, 周旭姣, 程万莉, 李尚中. 黄土旱塬区秸秆覆盖量对不同降雨年型土壤水温效应和冬小麦产量的影响[J]. 作物学报, 2025, 51(6): 1643-1653.
[6] 王东, 王森, 尚丽, 冯浩伟, 张永巧, 崔佳鸣, 李爽, 章佳聪, 车欢. 补灌对黄土高原半湿润区冬小麦产量和水分利用效率的影响[J]. 作物学报, 2025, 51(5): 1312-1325.
[7] 王娇, 白海霞, 韩语燕, 梁惠, 冯雅楠, 张东升, 李萍, 宗毓铮, 史鑫蕊, 郝兴宇. CO2浓度升高、升温及其交互作用对良星99冬小麦叶片碳氮代谢的影响[J]. 作物学报, 2025, 51(4): 1061-1076.
[8] 张妍妍, 李迎, 刘栩辰, 黄超, 吕佳宁, 周海佳, 马守田, 秦安振, 高子乐, 吴光辉, 陈丹, 姬夏楠, 刘战东. 深松耕作条件下滴灌对冬小麦花后干物质积累及灌浆特性的影响[J]. 作物学报, 2025, 51(11): 3065-3079.
[9] 张军, 胡川, 周起晖, 任开明, 董誓言, 刘傲寒, 吴金芝, 黄明, 李友军. 减氮及有机肥替代对旱地冬小麦干物质积累、转运、分配和产量的影响[J]. 作物学报, 2025, 51(1): 207-220.
[10] 马艳明, 娄鸿耀, 王威, 孙娜, 颜国荣, 张胜军, 刘杰, 倪中福, 徐麟. 新疆冬小麦籽粒品质性状遗传差异与关联分析[J]. 作物学报, 2024, 50(6): 1394-1405.
[11] 黄宏胜, 张馨月, 居辉, 韩雪. 大气CO2浓度升高背景下冬小麦冠层光谱特征和地上生物量估算[J]. 作物学报, 2024, 50(4): 991-1003.
[12] 赵荣荣, 丛楠, 赵闯. 基于Landsat 8影像提取豫中地区冬小麦和夏玉米分布信息的最佳时相选择[J]. 作物学报, 2024, 50(3): 721-733.
[13] 谢炜, 贺鹏, 马宏亮, 雷芳, 黄秀兰, 樊高琼, 杨洪坤. 秋闲期秸秆覆盖与施磷对冬小麦氮素吸收利用的影响[J]. 作物学报, 2024, 50(2): 440-450.
[14] 杨晓慧, 王碧胜, 孙筱璐, 侯靳锦, 徐梦杰, 王志军, 房全孝. 冬小麦对水分胁迫响应的模型模拟与节水滴灌制度优化[J]. 作物学报, 2023, 49(8): 2196-2209.
[15] 刘世洁, 杨习文, 马耕, 冯昊翔, 韩志栋, 韩潇杰, 张晓燕, 贺德先, 马冬云, 谢迎新, 王丽芳, 王晨阳. 灌水和施氮对冬小麦根系特征及氮素利用的影响[J]. 作物学报, 2023, 49(8): 2296-2307.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备15008789号-2