|
|
|
|
|
|
Hyperspectral Estimation of Tea Leaves Water Content Under the Influence of Dust Retention |
JIANG Jing1, 2, ZHAO Zi-wei1, 2, CAI Chang1, 2, ZHANG Jin-song3, CHENG Zhi-qing1, 2* |
1. College of Resources and Environment, Anhui Agricultural University, Hefei 230036, China
2. Hefei Agricultural Environment Science Observation and Experiment Station, Ministry of Agriculture, Hefei 230036, China
3. Chinese Academy of Forestry, Beijing 100091, China |
|
|
Abstract In order to reduce the influence of dust retention on the extraction of effective spectral information of tea leaf and to establish a more robust water content estimation model of tea leaf by spectrum. We took “Shu Chazao” as the research object and collected samples of tea leaves by random sampling. Then the hyperspectral information, leaf water content and dust retention rate of leaves were measured. The correlation coefficient method was used to extract feature information. Newly-built vegetation indexes were constructed by the normalization calculation method and ratio calculation method, The relative variability analysis was used to screen the candidate indexes that reduce the impact of dust retention on the accuracy of the leaf water content estimation model. By comparing the response relationship between newly-built vegetation indexes and existing water indexes under the different conditions of dust retention, the optimal vegetation index estimation model of tea leaf water content which less affected by dust retention, was selected. Finally, the high-precision estimation models of the tea leaf water content with the optimal vegetation index were established and verified. The results show that, dust leaves’ spectral reflectance is higher than clean leaves in 711~1 378 nm bands. The correlation between the water content of the tea leaves and vegetation index is affected by dust retention, but its correlation direction is not. Dust retention also makes the accuracy value of tea leaf water content estimation model decreased. The newly-built ratio index (RVI(1 298, 1 325)) with 1 298 and 1 325 nm as the center band is least affected by leaf dust retention under complex environmental conditions. Therefore, it is the optimal vegetation index, and the hyperspectral estimation model of tea leaf water content constructed by RVI(1 298, 1 325) has higher estimation accuracy, better sensitivity and stability (y=0.245x-0.241, R2=0.854, RMSE=0.001). In conclusion, this study provides a basis for the refined water management of tea trees and provides new ideas that high-precision models of water content estimation is constructed by hyperspectral information under complex environmental conditions.
|
Received: 2020-10-26
Accepted: 2021-03-12
|
|
Corresponding Authors:
CHENG Zhi-qing
E-mail: chengzhiqing1@126.com
|
|
[1] Verrelst J, Rivera J P, Frank V, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 260.
[2] Fang M H, Ju W M, Zhan W F. Remote Sensing of Environment, 2017, 196: 13.
[3] Feng Z, Zhou G S. Remote Sensing of Environment, 2015, 7(11): 2239.
[4] HU Zhen-zhu, PAN Cun-de, PAN Xin, et al(胡珍珠, 潘存德, 潘 鑫, 等). Scientia Silvae Sinicae(林业科学), 2016, 52(12): 39.
[5] PAN Qing-mei, ZHANG Jin-song, MENG Ping, et al(潘庆梅, 张劲松, 孟 平, 等). Journal of Northeast Forestry University(东北林业大学学报), 2019, 47(7): 68.
[6] CHEN Zhi-fang, SONG Ni, WANG Jing-lei, et al(陈智芳, 宋 妮, 王景雷, 等). Scientia Agricultura Sinica(中国农业科学), 2017, 50(5): 871.
[7] XU Qing, MA Yi, JIANG Qi, et al(徐 庆, 马 驿, 蒋 琦, 等). Remote Sensing Information(遥感信息), 2018, 33(5): 1.
[8] SUN Teng-teng, LIN Wen-peng, LI Ying, et al(孙腾腾, 林文鹏, 李 莹, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(8): 2539.
[9] LI Wei-tao, WU Jian, CHEN Tai-sheng, et al(李伟涛, 吴 见, 陈泰生, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(2): 180.
[10] GAO Chuan-you(高传友). Research of Soiland Water Conservation(水土保持研究), 2016, 23(1): 187.
[11] Lin W P, Li Y, Du S Q, et al. Ecological Indicators, 2019, 104(9): 41.
[12] CHENG Zhi-qing, ZHANG Jin-song, ZHENG Ning(程志庆, 张劲松, 郑 宁). Chinese Patent(中国专利):ZL201410310957.6, 2017.
[13] Katherine M H, Christopher M, Jin W. Remote Sensing of Environment, 2019, 231: 111. |
[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] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
FENG Hai-kuan1, 2, TAO Hui-lin1, ZHAO Yu1, YANG Fu-qin3, FAN Yi-guang1, YANG Gui-jun1*. Estimation of Chlorophyll Content in Winter Wheat Based on UAV Hyperspectral[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3575-3580. |
[12] |
WANG Xiao-xuan1, LU Xiao-ping1*, MENG Qing-yan2, 3, LI Guo-qing4, WANG Jun4, ZHANG Lin-lin2, 3, YANG Ze-nan1. Inversion of Leaf Area Index Based on GF-6 WFV Spectral Vegetation
Index Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2278-2283. |
[13] |
WANG Xiao-xuan1, LU Xiao-ping1*, LI Guo-qing2, WANG Jun2, YANG Zen-an1, ZHOU Yu-shi1, FENG Zhi-li1. Combining the Red Edge-Near Infrared Vegetation Indexes of DEM to
Extract Urban Vegetation Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2284-2289. |
[14] |
WANG Jin1, 2, CHEN Shu-tao1, 2*, DING Si-cheng1, 2, YAO Xue-wen1, 2, ZHANG Miao-miao1, 2, HU Zheng-hua2. Relationships Between the Leaf Respiration of Soybean and Vegetation
Indexes and Leaf Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1607-1613. |
[15] |
JI Tong1, 2, WANG Bo1, 2, YANG Jun-ying1, 2, LI Qiang1, 2, HE Guo-xing1, 2, PAN Dong-rong3, LIU Xiao-ni1, 2*. Spectral Characteristic Analysis and Spectral Identification of Desert Plants in Yanchi, Ningxia[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 678-685. |
|
|
|
|