光谱学与光谱分析 |
|
|
|
|
|
Retrieval Model for Subtle Variation of Contamination Stressed Maize Chlorophyll Using Hyperspectral Data |
WANG Ping1,LIU Xiang-nan2,HUANG Fang1 |
1.School of Urban and Environmental Sciences, Northeast Normal University, Changchun 130024, China 2.School of Information Engineering, China University of Geosciences, Beijing 100083, China |
|
|
Abstract Chlorophyll content is an important indicator of photosynthesis activity, stress and nutritional state.In the present paper, the hyperspectral data, foliar chlorophyll content and heavy metal contents in foliar and soil were measured for the maize growing in three natural fields.In most previous research, the contamination stress was controlled artificially in laboratory by adding chromium, zinc or copper pollutant etc.to the soil, and the pollutant concentration added was much higher than that in natural environment.The three sample fields were under different heavy mental contamination level, but all located at the Changchun region, Northeast China, where is called Golden Maize Belts in the world.After continuum removal (400-800 nm), ten spectral indices were computed including max absorption position, normalized reflectance at max absorption position, absorption depth, green peak, normalized reflectance at green peak, red edge, normalized reflectance at red edge, red peak, absorption width, and asymmetry degree.The physics meaning of the above indices and their correlation with maize foliar chlorophyll content were analyzed.It was found that there were close relationships between these indices and foliar chlorophyll content except max absorption position, green edge and asymmetry degree.Besides the asymmetry degree, five indices were selected in the stepwise multiple linear regression for estimating chlorophyll content and its determination coefficient (R2) is 0.702 7.Furthermore, in order to measure the weak change information of foliar chlorophyll content under the contamination stress, the BP artificial neural network (ANN-BP) was used.Several ANN-BP models were built and tried with different structure, namely five nodes, seven nodes or ten nodes in input layer, one hidden layer or two hidden layer, and different nodes number in hidden layers.It was found that the highest accuracy of estimates was obtained by the model with two hidden layers, ten nodes in input layer, seven nodes in first hidden layer and 4 nodes in second hidden layer (R2=0.975 8).
|
Received: 2009-02-02
Accepted: 2009-05-06
|
|
Corresponding Authors:
WANG Ping
E-mail: wangp666@nenu.edu.cn
|
|
[1] Gitelson Anatoly A, Vin Andre′s, Cigand Vero′nica, et al.Geophysical Research Letters, 2005, 32: L08403. [2] TANG Yan-lin, WANG Xiu-zhen, WANG Ren-chao (唐延林,王秀珍,王人潮).Journal of Mountain Agriculture and Biology(山地农业生物学报), 2003, 22(3): 189. [3] Sampson P H, Zarco-Tejada P J, Mohammed G H, et al.Forest Science, 2003, 49(3): 381. [4] Wu Chaoyang, Niu Zheng, Tang Quan, et al.Agricultural and Forest Meteorology, 2008, 148(8-9): 1230. [5] YI Qiu-xiang, HUANG Jing-feng, WANG Xiu-zhen, et al(易秋香, 黄敬峰, 王秀珍, 等).Bulletin of Science and Technology(科技通报), 2007, 23(1): 83. [6] Xue Lihong, Yang Linzhang.ISPRS Journal of Photogrammetry & Remote Sensing,2009,64(1): 97. [7] Haboudane D, Miller J, Tremblay N, et al.Remote Sensing of Environment, 2002, 81(2-3): 416. [8] Moorthy Inian, Miller John R, Noland Thomas L.Remote Sensing of Environment, 2008, 112(6): 2824. [9] Darvishzadeh Roshanak, Skidmore Andrew, Schlerf Martin, et al.ISPRS Journal of Photogrammetry & Remote Sensing, 2008,63(4): 409. [10] Zhang Yongqin, Chen Jing M, Miller John R, et al.Remote Sensing of Environment 2008, 112(7): 3234. [11] LIU Su-hong, LIU Xin-hui, HOU Juan, et al(刘素红, 刘新会, 侯 娟, 等), Science in China(E)(中国科学 E辑: 技术科学), 2007, 37(5): 693. [12] LI Qing-ting, YANG Feng-jie, ZHANG Bing, et al(李庆亭, 杨锋杰, 张 兵, 等).Journal of Remote Sensing(遥感学报), 2008, 12(2): 284. [13] Kooistra L, Salas E A L, Clevers J G P W, et al.Environmental Pollution, 2004, 127(2): 281. [14] PU Rui-liang, GONG Peng(浦瑞良, 宫 鹏).Hyperspectral Remote Sensing and Its Application(高光谱遥感及其应用).Beijing: Higher Education Press(北京: 高等教育出版社), 2000. [15] Uddling J, Gelang-Alfredsson J, Piikki K, et al.Photosynthesis Research, 2007, 91(1): 37. [16] ZHANG Xi-jie, LI Min-zan(张喜杰,李民赞).Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(10): 2404. [17] le Maire G, Francois C, Dufrene E.Remote Sensing of Environment, 2004, 89(1): 1.
|
[1] |
ZHANG Jun-yi1, 2, GAO De-hua1, SONG Di1, QIAO Lang1, SUN Hong1, LI Min-zan1*, LI Li1. Wavelengths Optimization and Chlorophyll Content Detection Based on PROSPECT Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1514-1521. |
[2] |
ZHANG Zhao1, 2, 3, 4, YAO Zhi-feng1, 3, 4, WANG Peng1, 3, 4, SU Bao-feng1, 3, 4, LIU Bin3, 4, 5, SONG Huai-bo1, 3, 4, HE Dong-jian1, 3, 4*, XU Yan5, 6, 7, HU Jing-bo2. Early Detection of Plasmopara Viticola Infection in Grapevine Leaves Using Chlorophyll Fluorescence Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1028-1035. |
[3] |
AN Ying1, 2, 4, DING Jing3, LIN Chao2, LIU Zhi-liang1, 4*. Inversion Method of Chlorophyll Concentration Based on
Relative Reflection Depths[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1083-1091. |
[4] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
[5] |
YANG Xu, LU Xue-he, SHI Jing-ming, LI Jing, JU Wei-min*. Inversion of Rice Leaf Chlorophyll Content Based on Sentinel-2 Satellite Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 866-872. |
[6] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
[7] |
TANG Yu-zhe, HONG Mei, HAO Jia-yong, WANG Xu, ZHANG He-jing, ZHANG Wei-jian, LI Fei*. Estimation of Chlorophyll Content in Maize Leaves Based on Optimized Area Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 924-932. |
[8] |
ZHU Hai-jun1, FU Hong-yu1, 2, WANG Xue-hua1*, CUI Guo-xian1, 2*,SHI Ai-long1, XUE Wei-chun3. Preliminary Study on the Intertemporal Predictability of the Physiological Index of Early Rice Based on Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 170-175. |
[9] |
PING Li1, ZHAO Rong1, YANG Bin1*, YANG Yang1, CHEN Xiao-long2, WANG Ying1. Inversion of Particle Size Distribution in Spectral Extinction Measurements Using PCA and BP Neural Network Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3639-. |
[10] |
LI Li-jie1,2, YUE Yan-bin2, WANG Yan-cang3, ZHAO Ze-ying2, LI Rui-jun2, NIE Ke-yan2, YUAN Ling1*. The Quantitative Study on Chlorophyll Content of Hylocereus polyrhizus Based on Hyperspectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3538-3544. |
[11] |
LIU Tan1, 2, XU Tong-yu1, 2*, YU Feng-hua1, 2, YUAN Qing-yun1, 2, GUO Zhong-hui1, XU Bo1. Chlorophyll Content Estimation of Northeast Japonica Rice Based on Improved Feature Band Selection and Hybrid Integrated Modeling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2556-2564. |
[12] |
Nigela Tuerxun1, Sulei Naibi2, GAO Jian3, SHEN Jiang-long1, ZHENG Jiang-hua1*, YU Dan-lin4. Chlorophyll Content Estimation of Jujube Leaves Based on GWLS-SVR Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1730-1736. |
[13] |
LIU Ting-yue1, DAI Jing-jing2*, TIAN Shu-fang1. A Neural Network Recognition Method for Garnets Subclass Based on Hyper Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1758-1763. |
[14] |
LIU Shuang, YU Hai-ye, ZHANG Jun-he, ZHOU Hai-gen, KONG Li-juan, ZHANG Lei, DANG Jing-min, SUI Yuan-yuan*. Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1912-1919. |
[15] |
HE Shao-fang1, SHEN Lu-ming1, XIE Hong-xia2*. Hyperspectral Estimation Model of Soil Organic Matter Content Using Generative Adversarial Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1905-1911. |
|
|
|
|