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.
Key words:Dust retention; Hyperspectral model; Vegetation index; Water content of tea leaf
[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.