基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

doi:10.13287/j.1001-9332.202005.022

应用生态学报 ›› 2020, Vol. 31 ›› Issue (5): 1636-1644.doi: 10.13287/j.1001-9332.202005.022

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

宋晓¹, 许端阳², 黄绍敏^1*, 黄晨晨³, 张水清¹, 郭斗斗¹, 张珂珂¹, 岳克¹

¹河南省农业科学院植物营养与资源环境研究所, 郑州 450002;
²中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室, 北京 100101;
³郑州大学生命科学院, 郑州 450002

收稿日期:2019-08-26 出版日期:2020-05-15 发布日期:2020-05-15
通讯作者: * E-mail: hsm503@sohu.com
作者简介:宋晓, 女, 1980年生, 博士研究生。主要从事作物栽培和高光谱遥感信息研究。E-mail: songxiao401@126.com
基金资助:
国家自然科学基金项目(31801261)和国家重点研发计划项目(2017YFD0301103,2016YFD0300809-3)资助

Nitrogen content inversion of wheat canopy leaf based on ground spectral reflectance data

SONG Xiao¹, XU Duan-yang², HUANG Shao-min^1*, HUANG Chen-chen³, ZHANG Shui-qing¹, GUO Dou-dou¹, ZHANG Ke-ke¹, YUE Ke¹

¹Institute of Plant Nutrient and Environmental Resources, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China;
²Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Sciences, Beijing 100101, China;
³Academy of Life Science, Zhengzhou University, Zhengzhou 450002, China

Received:2019-08-26 Online:2020-05-15 Published:2020-05-15
Contact: * E-mail: hsm503@sohu.com
Supported by:
This work was supported by the National Science Foundation of China (31801261) and the National Key Research and Development Program of China (2017YFD0301103,2016YFD0300809-3).

摘要/Abstract

摘要： 冬小麦冠层叶片氮含量是反映其产量与品质的重要指标,构建高普适性、高精准性冬小麦冠层叶片氮含量高光谱反演模型对提高其监测效率具有重要意义。以不同地点、品种、年份、施氮水平、生育期的大田试验数据为基础,基于两波段光谱植被指数NDRE和550 nm光谱反射率组合构建一个三波段植被指数NEW-NDRE,并与11个传统冬小麦冠层叶片氮素光谱指数进行比较。结果表明: NEW-NDRE及传统植被指数中NDRE、NDDA、RI-1dB与冬小麦冠层叶片氮含量的相关性较好;其中,灌浆初期NEW-NDRE与冬小麦冠层叶片氮含量相关性最好,决定系数R²为0.9,均方根误差(RMSE)为0.4;经独立数据检验,以NEW-NDRE为变量建立的冬小麦冠层叶片氮含量反演模型的平均相对误差(RE)为9.3%,明显低于以NDRE、NDDA、RI-1dB为变量的模型RE。总体上,新构建的NEW-NDRE对冬小麦冠层叶片氮含量的模拟能力显著优于传统指数,减弱了试验条件的限制性,可为精准施肥提供新的技术支撑。

关键词: 小麦, 氮素监测, 高光谱, 模型

Abstract: Canopy nitrogen content in wheat is a key indicator of wheat grain yield and quality. When using remote sensing technology to predict wheat canopy nitrogen content, a hyperspectral mode with high adaptability and high accuracy is needed to improve the inversion efficiency. We developed a new three-band spectral vegetation index (NEW-NDRE) by combining a two-band spectral index NDRE and the spectral reflectance at 550 nm based on field data collected from different sites, years, with different varieties and nitrogen levels and at multiple growth stages. The NEW-NDRE was compared with 11 traditional spectral vegetation indices in terms of wheat canopy nitrogen content inversion. NEW-NDRE and three traditional indices (NDRE, NDDA and RI-1dB) all closely correlated with wheat canopy nitrogen content. NEW-NDRE displayed the highest correlation with wheat canopy nitrogen content at early grain filling stage, with a coefficient (R²) of 0.9 and a root mean squared error (RMSE) of 0.4. The inversion model developed with the NEW-NDRE was validated with an independent dataset. The relative error (RE) of the model was 9.3%, which was significantly lower than that of NDRE, NDDA and RI-1dB. Generally, NEW-NDRE is a more robust index for wheat canopy nitrogen content inversion than traditional indices through eliminating environmental limitation, and it could be used as a new tool for precise fertilizer application.

Key words: wheat, nitrogen monitoring, hyperspectral, model

宋晓, 许端阳, 黄绍敏, 黄晨晨, 张水清, 郭斗斗, 张珂珂, 岳克. 基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型[J]. 应用生态学报, 2020, 31(5): 1636-1644.

SONG Xiao, XU Duan-yang, HUANG Shao-min, HUANG Chen-chen, ZHANG Shui-qing, GUO Dou-dou, ZHANG Ke-ke, YUE Ke. Nitrogen content inversion of wheat canopy leaf based on ground spectral reflectance data[J]. Chinese Journal of Applied Ecology, 2020, 31(5): 1636-1644.

参考文献

[1] 李长春, 陈鹏, 陆国政, 等. 基于无人机高清数码影像和高光谱遥感数据反演大豆典型生育期氮平衡指数. 应用生态学报, 2018, 29(4): 1225-1232 [Li C-C, Chen P, Lu G-Z, et al. The inversion of nitrogen balance index in typical growth period of soybean based on high definition digital image and hyperspectral data on unmanned aerial vehicles. Chinese Journal of Applied Ecology, 2018, 29(4): 1225-1232]
[2] 谭昌伟, 周清波, 齐腊, 等. 水稻氮素营养高光谱遥感诊断模型. 应用生态学报, 2008, 19(6): 1261-1268 [Tan C-W, Zhou Q-B, Qi L, et al. Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status. Chinese Journal of Applied Ecology, 2008, 19(6): 1261-1268]
[3] 谌俊旭, 黄山, 范元芳, 等. 单作套作大豆叶片氮素积累与光谱特征. 作物学报, 2017, 43(12): 1835-1844 [Chen J-X, Huang S, Fan Y-F, et al. Remote detection of canopy leaf nitrogen status in soybean by hyperspectral data under monoculture and intercropping systems. Acta Agronomica Sinica, 2017, 43(12): 1835-1844]
[4] 白丽敏, 李粉玲, 常庆瑞, 等. SPA和PLS法提高冬小麦冠层全氮高光谱估算的精确度. 植物营养与肥料学报, 2018, 24(5): 1178-1184 [Bai L-M, Li F-L, Chang Q-R, et al. Increasing accuracy of hyperspectral remote sensing for total nitrogen of winter wheat canopy by use of SPA and PLS methods. Journal of Plant Nutrition and Fertilizers, 2018, 24(5): 1178-1184]
[5] 徐道青, 刘小玲, 王维, 等. 淹水胁迫下棉花叶片高光谱特征及叶绿素含量估算模型. 应用生态学报, 2017, 28(10): 3289-3296 [Xu D-Q, Liu X-L, Wang W, et al. Hyper-spectral characteristics and estimation mode of leaf chlorophyll content in cotton under water logging stress. Chinese Journal of Applied Ecology, 2017, 28(10): 3289-3296]
[6] 宋晓. 小麦冠层反射光谱的角度效应及植株氮素监测研究. 博士论文. 郑州: 河南农业大学, 2016 [Song X. Study of Angle Effect on Spectral Reflectance of Wheat (Triticum aestivum L.) Canopy and Plant Nitrogen Monitoring. PhD Thesis. Zhengzhou: Henan Agricultural University, 2016]
[7] 孙小香, 王芳东, 赵小敏, 等. 基于冠层光谱和BP神经网络的水稻叶片氮素浓度估算模型. 中国农业资源与区划, 2019, 40(3): 35-44 [Sun X-X, Wang F-D, Zhao X-M, et al. The estimation models of rice leaf nitrogen concentration based on canopy spectrum and BP neural network. Journal of China Agricultural Resources and Regional Planning, 2019, 40(3): 35-44]
[8] He L, Zhang HY, Zhang YS, et al. Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy, 2016, 73: 170-185
[9] Feng W, Zhang HY, Zhang YS, et al. Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data. Field Crops Research, 2016, 198: 238-246
[10] Song X, Xu DY, He L, et al. Using multi-angle hyperspectral data to monitor canopy leaf nitrogen content of wheat. Precision Agriculture, 2016, 17: 721-736
[11] Guo BB, Qi SL, Heng YR, et al. Remotely assessing leaf N uptake in winter wheat based on canopy hyperspectral red-edge absorption. European Journal of Agro-nomy, 2017, 82: 113-124
[12] 冯伟. 基于高光谱遥感的小麦氮素营养及生长指标监测研究. 博士论文. 南京: 南京农业大学, 2007 [Feng W. Monitoring Nitrogen Status and Growth Cha-racters with Hyperspectral Remote Sensing in Wheat. PhD Thesis. Nanjing: Nanjing Agricultural University, 2007]
[13] Mistele B, Schmidhalter U. Estimating the nitrogen nutrition index using spectral canopy reflectance measu-rements. European Journal of Agronomy, 2008, 29: 184-190
[14] Erdle K, Mistele B, Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research, 2011, 124: 74-84
[15] Johnson LF, Billow CR. Spectrometric estimation of total nitrogen concentration in Douglas-fir foliage. Internatio-nal Journal of Remote Sensing, 1996, 17: 489-500
[16] Meroni M, Marinho E, Sghaier N, et al. Remote sensing based yield estimation in a Stochastic framework: Case study of durum wheat in Tunisia. Remote Sensing, 2013, 5: 539-557
[17] Gitelson AA. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 2004, 161: 165-173
[18] 鲍士旦. 土壤农化分析. 北京: 中国农业出版社, 2000: 42-44 [Bao S-D. Soil and Agricultural Chemistry Analysis. Beijing: China Agriculture Press, 2000: 42-44]
[19] Analytical Spectral Devices Inc. FieldSPec Pro User’s Guide. Boulder: Analytical Spectral Devices Inc., 2002
[20] Sims DA, Gamon JA. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 2002, 81: 337-354
[21] Fitzgerald GJ, Rodriguez D, Christensen LK, et al. Spectral and thermal sensing for nitrogen and water status in two wheat agroecosystems. Precision Agriculture, 2006, 7: 233-248
[22] Feng W, Guo BB, Wang ZJ, et al. Measuring leaf nitrogen concentration in winter wheat using double-peak spectral reflection remote sensing data. Field Crops Research, 2014, 159: 43-52
[23] Richardson AJ, Wiegand CL. Distinguishing vegetation from soil background information. Engineering and Remote Sensing, 1997, 43: 1541-1552
[24] Feng W, Yao X, Zhu Y, et al. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 2008, 28: 394-404
[25] Le Maire G, François C, Soudani K, et al. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sensing of Environment, 2008, 112: 3846-3864
[26] Li F, Mistele B, Hu YC, et al. Comparing hyperspectral index optimization algorithms to estimate aerial N uptake using multi-temporal winter wheat datasets from contrasting climatic and geographic zones in China and Ger-many. Agricultural and Forest Meteorology, 2013, 180: 44-57
[27] Tian YC, Yao X, Yang J, et al. Assessing newly deve-loped and published vegetation indices for estimating rice leaf nitrogen concentration with ground and space-based hyperspectral reflectance. Field Crops Research, 2011, 120: 299-310
[28] Gupta R, Vijayan D, Prasad T. Comparative analysis of red-edge hyperspectral indices. Advances in Space Research, 2003, 32: 2217-2222
[29] Huete AR. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 1988, 25: 295-309
[30] 武改红. 冬小麦不同叶位叶片高光谱特征及其对氮素的响应. 硕士论文. 太谷: 山西农业大学, 2018 [Wu G-H. Hyperspectral Characteristics and Response on Nitrogen Nutrition Diagnosis for Different Position Leaves of Winter Wheat. PhD Thesis. Taigu: Shanxi Agricultural University, 2018]
[31] 赵新飞, 冯嘉敏, 张大伟, 等. 不同生育期水稻光谱反射率的变化规律. 仲恺农学工程学院学报, 2015, 28(4): 26-31 [Zhao X-F, Feng J-M, Zhang D-W, et al. The changes spectral of reflectance of rice at different growth stages. Journal of Zhongkai University of Agriculture and Engineering, 2015, 28(4): 26-31]
[32] He L, Zhang HY, Zhang YS, et al. Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing. European Journal of Agronomy, 2016, 73: 170-185
[33] Song X, Feng W, He L, et al. Examining view angle effects on leaf N estimation in wheat using field reflectance spectroscopy. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122: 57-67
[34] 张俊华, 张佳宝. 不同生育期冬小麦光谱特征对叶绿素和氮素的响应研究. 土壤通报, 2008, 39(3): 586-592 [Zhang J-H, Zhang J-B. Response of winter wheat spectral reflectance to leaf chlorophyll, total nitrogen of above ground. Chinese Journal of Soil Science, 2008, 39(3): 586-592]
[35] Moges SM, Raun WR, Mullen RW, et al. Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield. Journal of Plant Nutrition, 2004, 27: 1431-1441
[36] 程一松, 胡春胜, 郝二波, 等. 氮素胁迫下的冬小麦高光谱特征提取与分析. 资源科学, 2003, 25(1): 86-93 [Cheng Y-S, Hu C-S, Hao E-B, et al. Analysis and extraction of hyperspectral information feature of winter wheat under stress condition. Resources Science, 2003, 25(1): 86-93]
[37] Thomas JR, Oerther GF. Estimating nitrogen content of sweet pepper leaves by reflectance measurements. Agro-nomy Journal, 1972, 64: 11-13
[38] Hatfleld JL, Gitelson AA, Schepers JS, et al. Application of spectral remote sensing for agronomic decisions. Agronomy Journal, 2008, 100: 117-131
[39] Haboudane D, Miller JR, Tremblay N, et al. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 2002, 81: 416-426
[40] Ryu C, Suguri M, Umeda M. Model for predicting the nitrogen content of rice at panicle initiation stage using data from airborne hyperspectral remote sensing. Biosystems Engineering, 2009, 104: 465-475
[41] 杨长明, 杨林章, 韦朝领, 等. 不同品种水稻群体冠层光谱征比较研究. 应用生态学报, 2006, 13(6): 689-692 [Yang C-M, Yang L-Z, Wei C-L, et al. Ca-nopy spectral characteristics of different rice varieties. Chinese Journal of Applied Ecology, 2006, 13(6): 689-692]
[42] 贾学勤, 冯美臣, 王超, 等. 冬小麦籽粒氮积累量高光谱监测研究. 山西农业科学, 2018, 46(3): 335-338 [Jia X-Q, Feng M-C, Wang C, et al. Study on hyperspectral monitor of grain nitrogen accumulation in winter wheat. Journal of Shanxi Agricultural Sciences, 2018, 46(3): 335-338]
[43] Carter G, Knapp A. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 2001, 88: 677-684

基于地面观测光谱数据的冬小麦冠层叶片氮含量反演模型

Nitrogen content inversion of wheat canopy leaf based on ground spectral reflectance data

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	肖晨, 田栋元, 马榕, 董灵波. 兴安落叶松天然林更新数量相容性预测模型 [J]. 应用生态学报, 2023, 34(9): 2345-2354.
[2]	李长青, 纪萌, 马萌萌, 王硕, 刘欢, 孙志梅. 天然增效剂与化学抑制剂复配对小麦/玉米轮作体系产量、氮素利用及氮平衡的影响 [J]. 应用生态学报, 2023, 34(9): 2391-2397.
[3]	杨振康, 杨婉蓉, 刘志娟, 高伟达, 任图生, 沈彦俊, 杨晓光. 气候变化对东北三省土壤风蚀的影响 [J]. 应用生态学报, 2023, 34(9): 2429-2435.
[4]	孙龙, 马灵感, 郭妍, 范佳乐, 陈伯轩, 胡同欣. 基于卷积神经网络及气象要素回归法的地表死可燃物含水率预测模型 [J]. 应用生态学报, 2023, 34(9): 2453-2461.
[5]	谢卓洪, 雷敏, 刘利杰, 莫燕卿, 战国强, 刘萍. 珠三角城市群景观生态格局评价与优化 [J]. 应用生态学报, 2023, 34(9): 2481-2488.
[6]	祁育汀, 张平, 刘雷, 马雪楠, 王欢, 赵娟. 关中平原城市群土地利用结构多情景优化和生态系统服务价值预测 [J]. 应用生态学报, 2023, 34(9): 2507-2517.
[7]	任孟林, 郭妍, 陈伯轩, 范佳乐, 胡同欣, 孙龙. 红松人工林地表可燃物火蔓延速率预测模型 [J]. 应用生态学报, 2023, 34(8): 2091-2100.
[8]	杨涛, 于颖, 杨曦光, 杜红萱. 无人机高光谱联合LiDAR估测林分与单木尺度叶绿素含量 [J]. 应用生态学报, 2023, 34(8): 2101-2112.
[9]	邢衍阔, 康斌, 鹿志创, 高祥刚, 王震, 田甲申. 气候变化条件下太平洋丽龟的适宜生境及其变化 [J]. 应用生态学报, 2023, 34(8): 2267-2273.
[10]	贾肖肖, 肖辉杰, 辛智鸣, 范光鹏, 李俊然, 杨玉丽, 汪立韬. 农田防护林不同树种三维模型构建与风场模拟 [J]. 应用生态学报, 2023, 34(7): 1892-1900.
[11]	陈红莲, 李瑞, 张玉珊, 吴清林, 袁江, 高家勇. 赤水河流域不同地貌区生态系统健康对比 [J]. 应用生态学报, 2023, 34(7): 1912-1922.
[12]	王复标, 杨小龙, 康华靖, 叶子飘. 不同环境条件下光合作用对光响应模型的研究进展 [J]. 应用生态学报, 2023, 34(7): 1995-2005.
[13]	夏卓异, 苏杰, 尹海伟, 孔繁花. 气候变化背景下中国朱鹮适宜生境时空格局 [J]. 应用生态学报, 2023, 34(6): 1467-1473.
[14]	李久林, 胡大卫, 储金龙, 尹海伟. 基于未来情景模拟的安徽省潜山市生态网络构建与优化 [J]. 应用生态学报, 2023, 34(6): 1474-1482.
[15]	郭蓉, 吴旭东, 王占军, 蒋齐, 俞鸿千, 贺婧, 刘文娟, 马琨. 荒漠草原土壤细菌和真菌群落对降水变化的响应 [J]. 应用生态学报, 2023, 34(6): 1500-1508.