Hyperspectral model for transparency of Chagan lake in semi-arid environment of Songnen plain
-
摘要: 通过实测查干湖高光谱数据,建立透明度(Secchi Disk Depth,SDD)单波段估测模型、比值估测模型以及神经网络高光谱估测模型,并以确定性系数R2以及剩余残差RMSE为指标进行了验证.通过对单波段估测模型和比值估测模型进行比较发现,单波段模型估测结果与比值模型相差无几,而水体透明度经对数处理有利于模型精度提高,但是神经网络模型是三者中最优的.查干湖透明度高光谱定量估测模型的建立,有利于今后利用遥感影像,对查干湖水体透明度进行全面估测,对于研究和监测查干湖水体水质状况有重要意义.Abstract: The transparency,which can demonstrate the limpid or muddy degree of lake water also be used to evaluate water eutrophic state,is one of the water body visible degree indicators.The traditional transparency of surface water is observed with Secchi Disk.It is time-consuming and strenuous,and only represents some local information. The remote sensing technology, which can cover large area simultaneously and periodically,may deal with this kind of limitation effectively. In this paper,77 spectral reflectance data of different water sites in Chagan lake were collected during 6 field works,and the water transparency data were acquired simultaneously. the linear regression constructed with every single band of reflectance and derivative against the water transparency data;the band ratio model with the reflectance of 780 nm and 654 nm also established.Finally,ANN-BP model is established with diagnostic band reflectance and derivative as the input vector.The result shows that water trans-parency has an intimate relation with water reflectance,and correlation coefficient is about 0.5 in blue and green spectral region, it is about 0.6 in red spectral region;while it obtains the maximum value in short wave of near infrared region. As for derivative reflectance,it also obtains higher value in near infrared spectral region.The regression model established with single band gets the similar performance as that with band ratio as regression dependent variable. By comparison,the ANN-BP model performed best with determination coefficient(R2)of 0.98. It indicates that hyperspectral remote sensing models established for estimation Chagan lake water transparency can obtain the comparatively accurate result. The construction of hyperspectral models in Chagan lake will to help estimate the water body transparency with satellite images,and also provide a method for remote sensing monitoring of other inner water body with similar water status as that in Chagan lake.
-
Keywords:
- Semi-arid region /
- inland lake /
- Secchi Disk Depth /
- Hyperspectral /
- Chagan lake
-
-
[1] 韩震,陈西庆,恽才兴.海洋高光谱遥感研究进展[J].海洋科学,2003,27(1):22-25. [2] 杨富亿.查干湖的综合开发与利用[J].资源开发与市场,1998,14(6):247-249. [3] 张运林,秦伯强,陈伟民.湖泊光学研究动态及其应用[J].水科学进展,2003,14(5):653-659. [4] Doxarana D,Froidefonda J M,Lavender S,et al.Spectral signature of highly turbid waters:Application with SPOT data to quantify suspended particulate matter concentrations[J].Remote Sensing of Environment,2002,81:149-161. [5] Kallio K,Kutser T,Hannonen T,et al.Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons[J].The Science of the Total Environment,2001,268:59-77. [6] Koponen S,Pulliainena J,Kallio K,et al.Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J].Remote Sensing of Environment,2002,79:51-59. [7] Harma P,Vepsalainena J,Hannonena T,et al.Detection of water quality using simulated satellite data and semi-empirical algorithms in Finland[J].The Science of the Total Environment,2001,268:107-121. [8] Kallio K,Kutser T,Hannonen T.Retrieval of water quality from airborne imaging spectrometry of various lake types in different seasons[J].The Science of the Total Environment,2001,268:59-77. [9] 张霄宇,林以安,唐仁友,等.遥感技术在河口颗粒态总磷分布及扩散研究中的应用初探[J].海洋学报,2005,25(1):51-56. [10] 吴敏,王学军.应用MODIS遥感数据监测巢湖水质[J].湖泊科学,2005,17(2):110-113. [11] Keiner L E.A neural networks model for estimation sea surface chlorophyll and sediments from Thematic Mapper Imagery.Remote Sensing of Environment,1998,66:153-165. [12] Danson F M,Rowland C S.Training a neural network with a canopy reflectance model to estimate crop leaf area index[J].International Journal of Remote Sensing,2003,24(23):4891-4905. [13] Zhang Y,Pulliainen S,Koponen,et al.Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data[J].Remote Sens Environ,2002,81:327-336.
计量
- 文章访问数: 221
- HTML全文浏览量: 24
- PDF下载量: 437
下载:
