|
|
|
|
|
|
Threshold Calibration of Key Parameters of Withered Grass Based on PROSAIL Model in Qinghai-Tibet Plateau |
LIANG Hao1, XU Wei-xin1*, DUAN Xu-hui1, ZHANG Juan2, DAI Na1, XIAO Qiang-zhi1, WANG Qi-yu1 |
1. School of Resources and Environment, Chengdu University of Information Technology, Chengdu 610225, China
2. Qinghai Institute of Meteorological Science, Qinghai Meteorological Bureau, Xining 810001,China
|
|
|
Abstract Grassland, as an important part of the ecosystem in the Qinghai-Tibet Plateau, plays an ecological indicator role. However, during the non-growing season, it generally didn’t monitor or observe alpine grass in winter. It could be a great gap to develop the methods of grassland monitoring and its application in winter. PROSAIL, a physical radiation model, can quantitatively describe the relationship between various vegetation parameters and canopy reflectance spectra. In this study, the latest version of the PROSAIL-D model and ground observed data were applied to explore the thresholds of critical range for 10 parameters of withered grass affected by reflectance spectrum. Based on the reflectance spectrums and the corresponding character’s parameters of withered grass that were obtained in the field, 15 000 possible withered grass spectrums were simulated by the PROSAIL model. Compared to the difference of reflectance spectra between withered grass and green grass observed in winter and summer respectively, it is found that a clear difference displayed on visible and near-infrared bands and with a significant linear in 400~1 300 nm spectral range for withered grass in winter in Qinghai-Tibet Plateau. On that basis, we proposed a method to distinguish the withered and green grass using the difference between red and green spectral reflections. It can be considered as a withered grass spectrum while the difference is greater than 0. Furthermore, a dataset of potential withered grass spectrum was established by two-steps identification from 15 000 possible spectrums based on the methods described above. The potential withered grass spectrums are correlated closely to the observed spectrums with a whole range of 400~2 500 nm, and the R2 of all the simulated spectrum lines was between 0.904 and 0.994. By EFAST method and global sensitivity analysis, the brown pigment, carotenoid, anthocyanin, leaf structure and hot spot were identified as non-sensitive factors that respond to the withered grass spectrum. Finally, PROSAIL model was run again in OFAT (One Factor at a Time) with 99% confidence interval as the criterion and cosine distance as the evaluation function. The parameter threshold intervals of the sensitive factors of withered grass are estimated as: leaf area index of 0.2~0.89, chlorophyll content in 0~1.29 μg·cm-2, the average leaf angle between 11°~90°, equivalent water thickness from 0.000 1 to 0.005 cm, dry matter content within 0.008~0.05 g·cm-2.The results provide some important parameters and further understanding of grass characteristics in winter, and it will strongly promote the application in remote sensing monitoring.
|
Received: 2021-04-27
Accepted: 2021-07-12
|
|
Corresponding Authors:
XU Wei-xin
E-mail: weixin.xu@cuit.edu.cn
|
|
[1] English Stephen, Lean Peter, Geer Alan. Journal of Quantitative Spectroscopy and Radiative Transfer, 2020, 251: 107044.
[2] de Sá Nuno César, Baratchi Mitra, Hauser Leon T, et al. Remote Sensing, 2021, 13(4): 648.
[3] Zhang Yangyang, Yang Jian, Du Lin. Sensors, 2021, 21(5): 1869.
[4] Berger Katja, Atzberger Clement, Danner Martin, et al. Remote Sensing, 2018, 10(1): 85.
[5] LIANG Shun-lin,BAI Rui,CHEN Xiao-na,et al(梁顺林,白 瑞,陈晓娜,等). Journal of Remote Sensing(遥感学报),2020,24(6):618.
[6] Sinha S K, Padalia H, Dasgupta A, et al. International Journal of Applied Earth Observation and Geoinformation, 2020, 86: 102027.
[7] Danner Martin, Berger Katja, Wocher Matthias, et al. Remote Sensing, 2019, 11(10): 1150.
[8] Mohamad M A. J. Forestry Res., 2018, 29(5): 1395.
[9] Féret J B, Gitelson A A, Noble S D, et al. Remote Sensing of Environment, 2017, 193: 204.
[10] Sun Bo, Pan Wuyang, Wang Zili, et al. Microelectronics Reliability, 2015, 55(9-10): 1384.
[11] HAO Gai-rui,LI Jia-ke,LI Huai-en,et al(郝改瑞, 李家科, 李怀恩,等). Journal of Hydroelectric Engineering(水力发电学报),2018,37(12):54.
[12] Gao Lei, Bryan Brett A, Liu Jian, et al. J. Clean Prod., 2017, 162: 1009.
[13] George Kuruvachan K, Kumar C Santhosh, Sivadas Sunil, et al. Pattern Recognition Letters, 2018, 112: 285.
[14] Mavrotas George, Makryvelios Evangelos. European Journal of Operational Research, 2021, 291(2): 794.
[15] Banu Asfana, Ali Mohammad Yeakub, Rahman Mohamed Abdul, et al. The International Journal of Advanced Manufacturing Technology, 2020, 106(9): 4247.
|
[1] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[2] |
WANG Xin-hui1, 2, GONG Cai-lan1, 2*, HU Yong1, 2, LI Lan1, 2, HE Zhi-jie1, 2. Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1880-1885. |
[3] |
SU Wei1,2, WU Jia-yu1,2, WANG Xin-sheng1,2, XIE Zi-xuan1,2, ZHANG Ying1,2, TAO Wan-cheng1,2, JIN Tian1,2. Retrieving Corn Canopy Leaf Area Index Based on Sentinel-2 Image and PROSAIL Model Parameter Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1891-1897. |
[4] |
LI Shuo1, LI Chun-lian1, CHEN Song-chao3, 4, XU Dong-yun2, SHI Zhou2*. Removing the Effects of Water From Visible-Near Infrared Spectra in Soil Profiles for the Estimation of Organic Carbon[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1234-1239. |
[5] |
ZHAO Heng-qian1,2, ZHANG Wen-bo2, ZHU Xiao-xin2, BI Yin-li2*, LI Yao3, ZHAO Xue-sheng2, JIN Qian4. Analysis on Susceptibility of Vegetation Canopy Spectra in Coal Mining Area to Land Reclamation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1858-1863. |
[6] |
YANG Wei-shan1,2,3, LI Meng4*, SUN Xiao-lei2, HU Hua-ling4, HUANG Li-juan2. Fluorescence Spectral Characteristics of Dissolved Organic Matter in Meadow Soils in Qinghai under Different Altitudes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1477-1482. |
[7] |
FANG Xue-jing1,2,3, XIONG Wei1,3*, SHI Hai-liang1,3, LUO Hai-yan1,3, CHEN Di-hu1,3. Forward Model and Sensitivity Analysis for Limb-Scattered Radiation of Mesospheric OH Radicals Emission in Ultraviolet Band[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3278-3285. |
[8] |
SHAN Chang-gong1, LIU Cheng2*, WANG Wei3, SUN You-wen3, LIU Wen-qing3, TIAN Yuan3, YANG Wei3. Analysis of Sensitivity of the Parameters on Carbon Dioxide Retrieval Using High-Resolution Solar Absorption Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(07): 1997-2003. |
[9] |
WANG Zhi-wei1,2,3, WU Xiao-dong1, YUE Guang-yang1, ZHAO Lin1*, WANG Qian1, NAN Zhuo-tong1, QIN Yu1, WU Tong-hua1, SHI Jian-zong1, ZOU De-fu1,2. Spatial and Temporal Variations in Spectrum-Derived Vegetation Growth Trend in Qinghai-Tibetan Plateau from 1982 to 2014 [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(02): 471-477. |
[10] |
ZHANG Peng, HONG Yan-ji*, SHEN Shuang-yan, DING Xiao-yu, MADi . Study on Chemical Kinetic Effect of Dielectric Barrier Discharge Plasma [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(03): 706-710. |
[11] |
ZHENG Xing-ming1, 2, DING Yan-ling1, ZHAO Kai1, 2*, JIANG Tao1, LI Xiao-feng1, 2, ZHANG Shi-yi1, LI Yang-yang1, WU Li-li1, SUN Jian3, REN Jian-hua1, ZHANG Xuan-xuan4 . Estimation of Vegetation Water Content from Landsat 8 OLI Data [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(12): 3385-3390. |
[12] |
SONG Xiao-ning1, MA Jian-wei1, LI Xiao-tao2, LENG Pei1, ZHOU Fang-cheng1, LI Shuang1 . Estimation of Vegetation Canopy Water Content Using Hyperion Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(10): 2833-2837. |
[13] |
FENG Ming-chun, XU Liang*, GAO Min-guang, JIAO Yang, WEI Xiu-li, JIN Ling, CHENG Si-yang, LI Xiang-xian, FENG Shu-xiang. Optical Properties Research of Bacillus Subtilis Spores by Fourier Transform Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(12): 3193-3196. |
[14] |
JIAO Quan-jun1, ZHANG Bing1*, LIU Liang-yun1, HE Yong-tao2, HU Yong1 . Analysis on Vegetations Spectral Characteristics along the Altitudinal Gradients in South-Facing Slope of Dangxiong Valley [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(10): 2810-2814. |
[15] |
ZHAO Shao-hua1,2, QIN Qi-ming2, ZHANG Feng1, WANG Qiao1, YAO Yun-jun3, YOU Lin2,JIANG Hong-bo2, CUI Rong-bo2, YAO Yian-juan1 . Research on Using a Mono-Window Algorithm for Land Surface Temperature Retrieval from Chinese Satellite for Environment and Natural Disaster Monitoring(HJ-1B) Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(06): 1552-1556. |
|
|
|
|