|
|
|
|
|
|
Estimating the Corn Canopy Chlorophyll Content Using the Sentinel-2A Image |
SU Wei1, 2, ZHAO Xiao-feng3, SUN Zhong-ping4*, ZHANG Ming-zheng1, 2, ZOU Zai-chao1, 2, WANG Wei1, 2, SHI Yuan-li4 |
1. College of Land Science and Technology, China Agricultural University,Beijing 100083, China
2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
3. China Meteorological Center, Beijing 100081, China
4. Satellite Environment Center, Ministry of Ecology and Environment, Beijing 100094, China |
|
|
Abstract The chlorophyll within crop leaves and crop canopy produces energy and participates in photosynthesis process by absorbing sunlight. Therefore, it is important to estimate the crop canopy chlorophyll content timely and accurately for crop growth monitoring, nutrient content monitoring and crop quality evaluation. Sentinel-2 has a wide-swath sensor with 5-days revisit period, so the Sentinel-2 image is produced with high spatial resolution (10 m) and 13 spectral bands. Specially, there are three red edge bands in Sentinel-2 image, which are sensitive to crop canopy chlorophyll content and its change. So the Sentinel-2 image is an ideal remote sensing data source for chlorophyll content estimation. Vegetation indexes depict the difference for the crop between different growth conditions and different chlorophyll contents, through the band combinations based on the reflection characteristics of crops at different spectral bands. So the vegetation indexes from Sentinel-2 image can be used to estimate the corn canopy chlorophyll content timely and accurately in a regional area. Therefore, this study is focusing on estimating the corn canopy chlorophyll content using 10 kinds of vegetation indexes computing from Sentinel-2A remote sensing images. And the study area is located in three counties of Baoding City, Hebei Province, ranging from 115°29′E to 116°14′E, 39°5′N to 39°35′N. We measured the corn plant chlorophyll content in 24 sampling areas distributed randomly in the whole study area from 6 August to 11 August, 2016. And each sampling area was located using Huace i80 real-time kinematic (RTK) GPS receiver (Huace Ltd., Shanghai, China). The Sentinel-2A image was preprocessed including geometric correction, radiometric calibration and atmospheric correction, and Sen2Cor model and SNAP were used to do atmospheric correction. 10 vegetation indexes were computed including CIgreen(Green Chlorophyll Index), CIred-edge(Red-edge Chlorophyll Index), DVI(Difference Vegetation Index), LCI(Leaf Chlorophyll Index), MTCI(MERIS Terrestrial Chlorophyll Index), NAVI(Normalized Area Vegetation Index), NDRE(Normalized Difference Red-Edge), NDVI(Normalized Difference Vegetation Index), RVI(Ratio Vegetation Index), SIPI(Structure Insensitive Pigment Index). Secondly, the statistical correlativity was analyzed between these 10 vegetation indexes and measured chlorophyll content value for every sampling area. So the corn canopy chlorophyll content estimating was developed using this correlation analysis results. Lastly, the optimal chlorophyll content estimation model was selected to estimate the chlorophyll content in the whole study area. This study was focusing on (1) developing the estimation model for corn canopy chlorophyll content in the study area, and the accuracy was assessed using R2, RMSE and RE; (2) deciding the optimal band combination; (3)deciding the optimal amount of red edge band participating in vegetation indexes calculation. The accuracy assessment results indicated that (1) there was polynomial correlation between measured chlorophyll content and the selected 10 vegetation indexes in this study, and the accuracy of estimated chlorophyll content using the vegetation indexes considering the red edge bands is better than the ones without red edge bands. The CIgreen(560, 705)and DVI which were all considering red edge bands improved the chlorophyll content estimation accuracy, and the R2 improved 0.516 for CIgreen(560, 705). The statistical relationship between the measured chlorophyll content and the vegetation index in the field work was established, and the relationship was extended to the whole study area. This study was about the estimation of corn canopy LAI and chlorophyll content using these ten vegetation indexes, which was focusing on the following four parts. Firstly, we compared if the vegetation with or without red-edge band could get accurate LAI and chlorophyll content estimated result. Secondly, we added two red-edge bands to the vegetation indexes without red-edge band originally. Thirdly, we added two red-edge bands to the vegetation indexes with one red-edge band originally only. Fourthly, we set up the vegetation index with two red-edge bands. The results showed that there are polynomial regression between the selection of multi-VI and the field survey of canopy chlorophyll content. Because the introductions of the red edge band, the fitting accuracy improved more than 0.3 between the vegetation index and corn canopy chlorophyll content, and the CIgreen (560, 705) (Green Chlorophyll Index) improved 0.516 that is the highest. The index calculating between the visible light band and the first red edge band (705 nm), the near infrared band with the second red edge band (740 nm), both of which established the regression model with the field survey of corn canopy chlorophyll content, and promoted the best fitting precision. The MTCI (MERIS Terrestrial Chlorophyll Index) has the highest fitting precision in which the R2 is 0.803, RMSE is 3.185, RE is 4.819%. It is shown that adding the red edge band will improve the fitting precision and it is suitable for crop growth monitoring.
|
Received: 2018-03-28
Accepted: 2018-08-05
|
|
Corresponding Authors:
SUN Zhong-ping
E-mail: sunnybnu114@163.com
|
|
[1] Peng Y, Nguy-Robertson A, Arkebauer T, et al. Remote Sensing, 2017, 9(3): 226.
[2] Chemura A, Mutanga O, Odindi J. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, (99): 1.
[3] JIANG Hai-ling, YANG Hang, CHEN Xiao-ping, et al(姜海玲, 杨 杭, 陈小平). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(4): 975.
[4] DONG Heng, MENG Qing-ye, WANG Jin-liang, et al(董 恒, 孟庆野, 王金梁, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2012, 31(4): 336.
[5] Jesús D, Jochem V, Luis A, et al. Sensors, 2011, 11(7): 7063.
[6] Schlemmer M, Gitelson A, Schepers J, et al. International Journal of Applied Earth Observation &Geoinformation, 2013, 25(1): 47.
[7] ZHENG Yang, WU Bing-fang, ZHANG Miao(郑 阳, 吴炳方, 张 淼). Journal of Remote Sensing(遥感学报), 2017, 21(2): 318.
[8] WANG Xiao-xing, CHANG Qing-rui, LIU Meng-yun, et al(王晓星, 常庆瑞, 刘梦云, 等). Journal of Northwest A & F University·Nat. Sci. Ed(西北农林科技大学学报·自然科学版), 2016, 44(2): 48.
[9] DING Yong-jun, ZHANG Jing-jing, LI Xiu-hua, et al(丁永军, 张晶晶, 李修华,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(3): 292.
[10] Gitelson A A, Keydan G P, Merzlyak M N. Geophysical Research Letters, 2006, 33(11): 431.
[11] Dash J, Curran P J. Evaluation of the MERIS Terrestrial Chlorophyll Index (MTCT), Geoscience and Remote Sensing Symposium, IGARSS’04, 2004.
[12] Dash J, Curran P J. The Meris Terrestrial Chlorophyll Index, International Journal of Remote Sensing, 2004, 25(23): 5403.
[13] Carmona F, Rivas R, Fonnegra D C. European Journal of Remote Sensing, 2015, 48: 319.
[14] Barnes E M, Clarke T R, Richards S E, et al. Coincident Detection of Crop Water Stress, Nitrogen Status and Canopy Density Using Ground-based Multispectral Data. International Conference on Precision Agriculture and Other Resource Management,2015.
[15] Penuelas J, Baret F, Filella I. Photosynthetica, 1995, 31(2): 221. |
[1] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[2] |
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718. |
[3] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
[4] |
ZHENG Shu-yuan1, 2, HAI Yan1, 2, HE Meng-qi1, 2, WANG Jian-xiong1, 2. Construction of Vegetation Index in Visible Light Band of GF-6 Image With Higher Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3509-3517. |
[5] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[6] |
LI Yang1, LI Xiao-qi1, YANG Jia-ying1, SUN Li-juan2, CHEN Yuan-yuan1, YU Le1, WU Jing-zhu1*. Visualisation of Starch Distribution in Corn Seeds Based on Terahertz Time-Domain Spectral Reflection Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2722-2728. |
[7] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
[8] |
YANG Dong-feng1, HU Jun2*. Accurate Identification of Maize Varieties Based on Feature Fusion of Near Infrared Spectrum and Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2588-2595. |
[9] |
LIU Zhao1, 2, LI Hua-peng1, CHEN Hui1, 2, ZHANG Shu-qing1*. Maize Yield Forecasting and Associated Optimum Lead Time Research Based on Temporal Remote Sensing Data and Different Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2627-2637. |
[10] |
MA Bao-dong, YANG Xiang-ru, JIANG Zi-wei, CHE De-fu. Influence and Quantitative Analysis of Coal Dust Retention on Reflectance Spectra and Vegetation Index of Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1947-1952. |
[11] |
REN Hong-rui1, 2, ZHANG Yue-qi2, HE Qi-jin3, LI Rong-ping1, ZHOU Guang-sheng4, 5*. Extraction of Pddy Rice Planting Area Based on Multi-Temporal FY-3 MERSI Remote Sensing Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1606-1611. |
[12] |
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, BIAN Ming-bo1, 3, ZHAO Yu1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5. Estimation of Nitrogen Content in Potato Plants Based on Spectral Spatial Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1532-1540. |
[13] |
MENG Hao-ran1, 2, LI Cun-jun1, 3*, ZHENG Xiang-yu1, 2, GONG Yu-sheng2, LIU Yu1, 3, PAN Yu-chun1, 3. Research on Extraction of Camellia Oleifera by Integrating Spectral, Texture and Time Sequence Remote Sensing Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1589-1597. |
[14] |
WANG Shao-yan1, CHEN Zhi-fei2, LUO Yang1, JIAN Chun-xia1, ZHOU Jun-jie3, JIN Yuan1, XU Pei-dan3, LEI Si-yue3, XU Bing-cheng1, 4*. Study on Nutrient Content of Bothriochloa Ischaemum Community in the Loess Hilly-Gully Region Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1612-1621. |
[15] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
|
|
|
|