Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (3) : 54-57     DOI: 10.6046/gtzyyg.2010.03.12
Technology and Methodology |
Equivalent Leave Water Reflectance Based on In Situ Spectral Measurements In the Taihu Lake
HAN Xiu-zhen 1, ZHENG Wei 1, LIU Xiang 2
1.National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China; 2.Beijing Oriental TITAN Technology Co., Ltd, Beijing 100083, China
Download: PDF(758 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Leave water reflectance is an important parameter in the study of water optical characteristics. To better interpret the effect of cyanophytes contamination on water optical characteristics, the authors conducted in situ measurement of spectral reflectance and water sampling in the Taihu Lake on 10 and 11, November 2008. Remarkable effects were observed in leave water reflectance of the cyanobacterial water, leading to an obvious absorption peak in the red region and an increase in the near-infrared region. Equivalent leave water reflectance based on FY-3A and MODIS band settings was derived by using the spectral response functions. Furthermore, the authors used the Ration Index (RI) model for the estimation of chlorophyll-a on 12, November 2008, and observed high determination coefficients R2=0.72, which were further used to map the chlorophyll-a distribution. The results obtained will be helpful to the further evaluation of optical characteristics and water quality.

Keywords Image mosaic      Internal Geometric Accuracy      Geometric distortion     
: 

 

 
  TP 79

 
Issue Date: 20 September 2010
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
CAO Tong
HU Wei-Min
Cite this article:   
CAO Tong,HU Wei-Min. Equivalent Leave Water Reflectance Based on In Situ Spectral Measurements In the Taihu Lake[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(3): 54-57.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.03.12     OR     https://www.gtzyyg.com/EN/Y2010/V22/I3/54

[1]马荣华,孔维娟,段洪涛,等. 基于MODIS影像估测太湖蓝藻暴发期藻蓝素含量[J]. 中国环境科学, 2009,29(3):254-260.

[2]廖程浩,刘雪华. MODIS数据水体识别指数的识别效果比较分析[J]. 国土资源遥感, 2008(4):22-26.

[3]李建国,孙晓明,康慧,等. 曹妃甸近海Ⅱ类水体光谱反射率与悬浮泥沙浓度相关性研究[J]. 国土资源遥感, 2009(3):54-58.

[4]李云亮,张运. 基于TM 影像的太湖夏季悬浮物和叶绿a浓度反演[J]. 遥感信息, 2008(6):22-27.

[5]孔维娟,马荣华,段洪涛,等. 太湖秋冬季蓝藻水华MODIS卫星遥感监测[J]. 遥感信息, 2009(4):80-84.

[6]祝令亚,王世新,周艺,等. 应用MODIS影像估测太湖水体悬浮物浓度[J]. 水科学进展, 2007, 18(3):444-450.

[7]宋瑜,宋晓东,郭照冰,等. 利用MERIS 产品数据反演太湖叶绿素a 浓度研究[J]. 遥感信息, 2009(4):19-24.

[8]孙德勇,李云梅,王桥,等. 基于实测高光谱的太湖水体悬浮物浓度遥感估算研究[J]. 红外与毫米波学报, 2009, 28(2):124-128.

[9]Moses W J, Gitelson A A, Beranikov S, et al. Satellite Estimation of Chlorophyll-a Concentration Using the Red and NIR Bands of MERIS-the Azov Sea Case Study [J]. IEEE Geoscience and Remote Sensing Letters,2009,2026657.

[10]Gitelson A A, Dallomo G, Moses W, et al. A Simple Semi-analytical Model for Remote Estimation of Chlorophyll-a in Turbid Waters:Validation [J]. Remote Sensing of Environment, 2008, 112:3582-3593.

[11]Dallolmo G, Gitelson A A. Effect of Bio-optical Parameter Variability and Uncertainties in Reflectance Measurements on the Remote Estimation of Chlorophyll-a Concentration in Turbid Productive Waters:Modeling Results [J].Applied Optics,2006,45(15):3577-3592.

[12]Dallolmo G, Gitelson A A, Runaquist C. Towards a Unified Approach for Remote Estimation of Chlorophyll-a in Both Terrestrial Vegetation and Turbid Productive Waters [J]. Geophysical Research Letttter, 2003, 30 (18):1938.

[1] LI Penglong, DING Yi, HU Yan, LUO Ding, DUAN Songjiang, SHU Wenqiang. A method for rapid UAV images mosaicking based on GPU parallel computing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 57-63.
[2] BU Kun, WANG Zhiliang, WANG Juanle, ZHANG Shuwen, YANG Jiuchun, Yu Lingxue. Implementation of plane split model in remote sensing image mosaicking[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 225-230.
[3] CHENG Hong, ZHENG Yue, SUN Wenbang. Mosaic algorithm for remote sensing images based on minimum gradient point in local area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(1): 31-36.
[4] NI Zhong-Yun, HE Zheng-Wei, WU Hua, LIU Ting-Ting. A Preliminary Discussion on Typical Problems in the Remote Sensing Project of Tibetan Mineral Resources Potential Evaluation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 97-101.
[5] ZHANG Deng-Rong, ZHANG Han-Kui, YU Le, CHEN Qian. Multi Remote Sensing Image Mosaic Based on Valid Area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(1): 39-43.
[6] HUANG Shi-Cun, LI Xing-Chao, LU Yi-Lin, DU Quan-Liang. THE MOSAIC AND MAPPING OF CHINA DIGITAL IMAGES BASED ON CBERS-02 DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2008, 20(3): 42-44.
[7] CAO Tong, HU Wei-Min . STUDY ON THE EARTH CORRECTION OF TM AND THE MOSAIC METHOD[J]. REMOTE SENSING FOR LAND & RESOURCES, 2001, 13(3): 36-40.
[8] Wang Xiaohong, Zhang Ruijiang, Tian Shu Fang. THE DATA QUALITY ANALYSIS OF THE CBERS-1 SATELLITE IN ZHONGDIAN AREA, YUNNAN PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 2000, 12(2): 18-23,34.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech