光谱学与光谱分析 |
|
|
|
|
|
Estimation of Regional Leaf Area Index by Remote Sensing Inversion of PROSAIL Canopy Spectral Model |
LI Shu-min1, LI Hong1*, SUN Dan-feng2, ZHOU Lian-di1 |
1. Institute of Agricultural Integrated Development,Beijing Academy of Agricultural and Forestry Sciences,Beijing 100097,China 2. Resources and Environment College,China Agricultural University,Beijing 100193,China |
|
|
Abstract The present paper selected Qingyundian town and Weishanzhuang town in DaXing District, and Gaolingying town in Shunyi District as test areas, using MODIS data and ASTER data in different scales. The feasibility of winter wheat LAI inversion by PROSAIL physical model, especially the stability of remote sensing data in different scales, was discussed, and the results from experience model inversion were compared with that from statistical methods. The values of all samples LAI inversion from experience model are close in a region, which means experience model is a reflection of general growing trend, ignoring spatial heterogeneity of the regional leaf area index. But the value of LAI inversion from physical model can be truer in reflecting spatial heterogeneity of the regional leaf area index. The value of LAI inversion from physical model is more real, compared with experience model. With the method of linear weighing, the scale conversion was accomplished, and the LAI inversion results from different remote sensing scale data were compared, and were found similar. The result shows that in the process of large-scale regional LAI inversion, physical model inversion is more valid.
|
Received: 2008-10-16
Accepted: 2009-01-20
|
|
Corresponding Authors:
LI Hong
E-mail: lihsdf@sina.com
|
|
[1] YAO Yan-juan, CHEN Liang-fu, LIU Qin-huo, et al(姚延娟, 陈良富, 柳钦火, 等). Journal of Remote Sensing(遥感学报), 2006, 10(6): 869. [2] LI Kai-li, JIANG Jian-jun, MAO Rong-zheng, et al(李开丽, 蒋建军, 茅荣正, 等). Acta Ecologica Sinica(生态学报), 2005, 25(6): 1491. [3] WANG Xi-qun, MA Lü-yi, JIA Zhong-kui, et al(王希群, 马履一, 贾忠奎, 等). Chinese Journal of Ecology(生态学杂志), 2005, 24(5): 537. [4] WU Bing-fang, ZENG Yuan, HUANG Jin-liang(吴炳方, 曾 源, 黄进良). Advances in Earth Science(地球科学进展), 2004, 19(4): 585. [5] FANG Xiu-qin, ZHANG Wan-chang(方秀琴, 张万昌). Remote Sensing for Land & Resources(国土资源遥感), 2003,(3): 58. [6] Qi J,Moran M S,Cabot F,et al. Remote Sensing of Environment, 1995, 54(1): 71. [7] CHEN Jian, NI Shao-xiang, LI Yun-mei, et al(陈 健, 倪绍祥, 李云梅, 等). Remote Sensing for Land & Resources(国土资源遥感), 2005, (2): 20. [8] Jesuswc,Valefxr,Coelhorr. Agronomy Journal, 2001, 93(5): 989. [9] SJacquemoud,et al. Remote Sensing of Environment, 1995, 53(3): 163. [10] Privette J L,Emery W J,Schimel D S. Remote Sensing of Environment, 1996, 58(2): 187. [11] RUAN Wei-li, NIU Zheng(阮伟利, 牛 铮). Journal of the Graduate School of the Chinese Academy of Sciences(中国科学院研究生院学报), 2004, 21(1): 78. [12] SHI Run-he, ZHUANG Da-fang, NIU Zheng, et al(施润和, 庄大方, 牛 铮, 等). Chinese Journal of Ecology(生态学杂志), 2006, (5): 1. [13] CHEN Xin-fang, AN Shu-qing, CHEN Jing-ming, et al(陈新芳, 安树青, 陈镜明, 等). Chinese Journal of Ecology(生态学杂志), 2005, 24(9): 1074. [14] WANG Lu, LIN Qi-zhong, JIA Dong, et al(王 璐, 蔺启忠, 贾 东, 等). Environmental Science(环境科学), 2007, 28:1822. [15] TANG Shi-hao, ZHU Qi-jiang, SUN Rui(唐世浩, 朱启疆, 孙 睿). Progress in Natural Science(自然科学进展), 2006, 16(3): 331. |
[1] |
LI He1, WANG Yu2, FAN Kai2, MAO Yi-lin2, DING Shi-bo3, SONG Da-peng3, WANG Meng-qi3, DING Zhao-tang1*. Evaluation of Freezing Injury Degree of Tea Plant Based on Deep
Learning, Wavelet Transform and Visible Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 234-240. |
[2] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
[3] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[4] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[5] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
[6] |
ZHU Yu-chen1, 2, WANG Yan-cang3, 4, 5, LI Xiao-fang6, LIU Xing-yu3, GU Xiao-he4*, ZHAO Qi-chao3, 4, 5. Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2902-2909. |
[7] |
LI Chun-qiang1, 2, GAO Yong-gang1, 2, XU Han-qiu1, 2*. Cross Comparison Between Landsat New Land Surface Temperature
Product and the Corresponding MODIS Product[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 940-948. |
[8] |
ZHANG Hai-yang, ZHANG Yao*, TIAN Ze-zhong, WU Jiang-mei, LI Min-zan, LIU Kai-di. Extraction of Planting Structure of Winter Wheat Using GBDT and Google Earth Engine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 597-607. |
[9] |
LI Yun-xia1, MA Jun-cheng2, LIU Hong-jie3, ZHANG Ling-xian1*. Tillering Number Estimation of Winter Wheat Based on Visible
Spectrogram and Lightweight Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 273-279. |
[10] |
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. |
[11] |
YANG Xin1, 2, YUAN Zi-ran1, 2, YE Yin1, 2*, WANG Dao-zhong1, 2, HUA Ke-ke1, 2, GUO Zhi-bin1, 2. Winter Wheat Total Nitrogen Content Estimation Based on UAV
Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3269-3274. |
[12] |
WANG Ge1, YU Qiang1*, Yang Di2, NIU Teng1, LONG Qian-qian1. Retrieval of Dust Retention Distribution in Beijing Urban Green Space Based on Spectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2572-2578. |
[13] |
FENG Tian-shi1, 2, 3, PANG Zhi-guo1, 2, 3*, JIANG Wei1, 2, 3. Remote Sensing Retrieval of Chlorophyll-a Concentration in Lake Chaohu Based on Zhuhai-1 Hyperspectral Satellite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2642-2648. |
[14] |
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. |
[15] |
ZHAO Ai-ping1, MA Jun-cheng1, WU Yong-feng1*, HU Xin2, REN De-chao2, LI Chong-rui1. Predicting Yield Reduction Rates of Frost-Damaged Winter Wheat After Jointing Using Sentinel-2 Broad-Waveband Spectral Indices[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2225-2232. |
|
|
|
|