Estimation of Vegetation Canopy Water Content Using Hyperion Hyperspectral Data
SONG Xiao-ning1, MA Jian-wei1, LI Xiao-tao2, LENG Pei1, ZHOU Fang-cheng1, LI Shuang1
1. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 2. Remote Sensing Technology Application Center, China Institute of Water Resources and Hydropower Research, Beijing 100048, China
Abstract：Vegetation canopy water content (VCWC) has widespread utility in agriculture, ecology and hydrology. Based on the PROSAIL model, a novel model for quantitative inversion of vegetation canopy water content using Hyperion hyperspectral data was explored. Firstly, characteristics of vegetation canopy reflection were investigated with the PROSAIL radiative transfer model, and it was showed that the first derivative at the right slope (980～1 070 nm) of the 970 nm water absorption feature (D980～1 070) was closely related to VCWC, and determination coefficient reached to 0.96. Then, bands 983, 993, 1 003, 1 013, 1 023, 1 033, 1 043, 1 053 and 1 063 nm of Hyperion data were selected to calculate D980～1 070, and VCWC was estimated using the proposed method. Finally, the retrieval result was verified using field measured data in Yingke oasis of the Heihe basin. It indicated that the mean relative error was 12.5%, RMSE was within 0.1 kg·m-2 and the proposed model was practical and reliable. This study provides a more efficient way for obtaining VCWC of large area.
Key words：Hyperion;PROSAIL model;First derivative;Vegetation canopy water content
宋小宁1，马建威1，李小涛2，冷 佩1，周芳成1，李 爽1 . 基于Hyperion高光谱数据的植被冠层含水量反演 [J]. 光谱学与光谱分析, 2013, 33(10): 2833-2837.
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. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(10): 2833-2837.
[1] Battrick B, Estec, ESA, et al. The Changing Earth. Noordwijk: ESA Publications Division, 2006. [2] Ghulam A, Li Z L, Qin Q M, et al. Agricultural and Forrest Meteorology, 2008, 148: 1679. [3] Jackson T J, Chen D Y, Cosh M, et al. Remote Sensing of Environment, 2004, 92: 475. [4] Si Y L, Schlerf M, Zurita-Milla R, et al. Remote Sensing of Environment, 2012, 121: 415. [5] Jacquemoud S, Verhoef W, Baret F, et al. Remote Sensing of Environment, 2009, 113: 56. [6] Clevers J G P W, Kooistra L, Schaepman M E. International Journal of Applied Earth Observation and Geoinformation, 2008, 10(3): 388. [7] Clevers J G P W, Kooistra L, Schaepman M E. International Journal of Applied Earth Observation and Geoinformation, 2010, 12(2): 119. [8] Cheng Y B, Zarco-Tejada P J, Riano D, et al. Remote Sensing of Environment, 2006, 105: 354. [9] Li X, Li X, Li Z,et al. Journal of Geophysical Research, 2009, 114(D22103): 1415. [10] Jacquemoud S, Baret F. Remote Sensing of Environment, 1990, 34(2): 75. [11] Verhoef W. Remote Sensing of Environment, 1984, 16(2): 125. [12] Kim d. Sail [EB/OL]. [2012-10-1].http://ars.usda.gov/services/software/download.htm?softwareid=12&modecode=12-65-06-00. [13] Fourty T, Baret F, Jacquemoud S, et al. Remote Sensing of Environment, 1996, 56(2): 104.
