| 光谱学与光谱分析 |
|
|
|
|
|
| Estimation of Forest Canopy Chlorophyll Content Based on PROSPECT and SAIL Models |
| YANG Xi-guang, FAN Wen-yi*, YU Ying |
| Northeast Forestry University, Harbin 150040, China |
|
|
|
|
Abstract The forest canopy chlorophyll content directly reflects the health and stress of forest. The accurate estimation of the forest canopy chlorophyll content is a significant foundation for researching forest ecosystem cycle models. In the present paper, the inversion of the forest canopy chlorophyll content was based on PROSPECT and SAIL models from the physical mechanism angle. First, leaf spectrum and canopy spectrum were simulated by PROSPECT and SAIL models respectively. And leaf chlorophyll content look-up-table was established for leaf chlorophyll content retrieval. Then leaf chlorophyll content was converted into canopy chlorophyll content by Leaf Area Index (LAI). Finally, canopy chlorophyll content was estimated from Hyperion image. The results indicated that the main effect bands of chlorophyll content were 400-900 nm, the simulation of leaf and canopy spectrum by PROSPECT and SAIL models fit better with the measured spectrum with 7.06% and 16.49% relative error respectively, the RMSE of LAI inversion was 0.542 6 and the forest canopy chlorophyll content was estimated better by PROSPECT and SAIL models with precision=77.02%.
|
|
Received: 2009-12-12
Accepted: 2010-03-16
|
|
|
|
Corresponding Authors:
FAN Wen-yi
E-mail: fanwy@163.com
|
|
[1] Gitelson A A, Merzlyak M N, Lichtenthaler H K. Journal of Plant Physiology, 1996, 148: 501.
[2] Carter G A. International Journal of Remote Sensing, 1994, 15: 697.
[3] Sampson P H, Zarco-Tejada P J, Mohammed G H, et al. Forest Science, 2003, 49(3): 381.
[4] Datt B. Journal of Plant Physiology, 1999, 154: 30.
[5] Datt B. Remote Sensing of Environment, 1998, 66: 111.
[6] Penuelas J, Baret F B, Filella I. Photosynthetica, 1995, 31: 221.
[7] Blackburn G A. Remote sensing of Environment, 1998, 66: 273.
[8] Haboudane D, Miller J R, Tremblay N, et al. Remote Sensing of Environment, 2002, 81: 416.
[9] Zhang Y Q, Chen J M, Miller J R, et al. Remote Sensing of Environment, 2008, 112(7): 3234.
[10] Renzullo L J, Blanchield A L, Guillermin R, et al. International Journal of Remote Sensing, 2006, 27(4): 817.
[11] Zarco-Tejada P J, Miller J R, Noland T L, et al. IEEE Transactions on Geosciences and Remote Sensing, 2001, 39(7): 1491.
[12] Zarco-Tejada P J, Miller J R, Harron H, et al. Remote Sensing of Environment, 2004, 89: 189.
[13] Chen J M, Cihlar J. Remote Sensing of Environment, 1996, 55: 153.
[14] Pearlman J, Carman S, Segal C, et al. In Proceedings of IGARSS, 2001, 17: 3036.
[15] Liang S L, Fang H L, Thorp L, et al. Transactions on Geoscience and Remote Sensing, 2003, 41(1): 1260.
[16] Datt B, McViear T R, VanNiel T G, et al. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1246.
[17] Gondenough D G, Andrew D, Niemann K O, et al. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1321.
[18] NIU Zheng, WANG Chang-yao, et al(牛 铮，王长耀，等). Remote Sensing and Applications for Carbon Cycle(碳循环遥感基础与应用). Beijing：Science Press(北京：科学出版社), 2008. 66.
[19] Verhoef W. Ph.D. thesis. National Aerospace Lab., Amsterdam, The Netherlands, 1998.
[20] Jacquemoud S, Baret F. Remote Sensing of Environment, 1990, 34: 75.
|
| [1] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [2] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
| [3] |
WANG Jing-yong1, XIE Sa-sa2, 3, GAI Jing-yao1*, WANG Zi-ting2, 3*. Hyperspectral Prediction Model of Chlorophyll Content in Sugarcane Leaves Under Stress of Mosaic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2885-2893. |
| [4] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
| [5] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
| [6] |
KONG Bo1, YU Huan2*, SONG Wu-jie2, 3, HOU Yu-ting2, XIANG Qing2. Hyperspectral Characteristics and Quantitative Remote Sensing Inversion of Gravel Grain Size in the North Tibetan Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2381-2390. |
| [7] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
| [8] |
WANG Hui-min1, 2, YU Lei1, XU Kai-lei1, 2, JIANG Xiao-guang1, 2, WAN Yu-qing1, 2*. Estimation of Salt Content of Saline Soil in Arid Areas Based on GF-5 Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2278-2286. |
| [9] |
CAO Yang1, 2, LI Yan-hong1, 2*. Study on the Effects of NO2 Pollution Under COVID-19 Epidemic
Prevention and Control in Urumqi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1981-1987. |
| [10] |
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. |
| [11] |
WANG Yan-cang1, 4, LI Xiao-fang2, LI Li-jie5, LI Nan1, 4*, JIANG Qian-nan1, 4, GU Xiao-he3, YANG Xiu-feng1, 4LIN Jia-lu1, 4. Quantitative Inversion of Chlorophyll Content in Stem and Branch of
Pitaya Based on Discrete Wavelet Differential Transform Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 549-556. |
| [12] |
LI Xiao-kai, YU Hai-ye, YU Yue, WANG Hong-jian, ZHANG Lei, ZHANG Xin, SUI Yuan-yuan*. Inversion Model of Clorophyll Content in Rice Based on a Bonic
Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 93-99. |
| [13] |
XU Long-xin1, 2, 3, 4, SUN Yong-hua2, 3, 4*, WU Wen-huan1, ZOU Kai2, 3, 4, HE Shi-jun2, 3, 4, ZHAO Yuan-ming2, 3, 4, YE Miao2, 3, 4, ZHANG Xiao-han2, 3, 4. Research on Classification of Construction Waste Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3927-3934. |
| [14] |
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. |
| [15] |
HU Xin-yu1, 2, XU Zhang-hua1, 2, 3, 5, 6*, HUANG Xu-ying1, 2, 8, ZHANG Yi-wei1, 2, CHEN Qiu-xia7, WANG Lin1, 2, LIU Hui4, LIU Zhi-cai1, 2. Relationship Between Chlorophyll and Leaf Spectral Characteristics and Their Changes Under the Stress of Phyllostachys Praecox[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2726-2739. |
|
|
|
|
