Literature DB >> 18853267

Estimation of chlorophyll-a concentration using field spectral data: a case study in inland Case-II waters, North China.

JingPing Xu1, Fang Li, Bai Zhang, KaiShan Song, ZongMing Wang, DianWei Liu, GuangXin Zhang.   

Abstract

In the remote sensing of chlorophyll-a (Chla) in inland Case-II waters, the assumption that the optical parameter of Chla specific absorption coefficient a*ph remains constant usually restrains application of many models. In this paper, we presented a newly developed model [Rrs(-1)(lambda1) - Rrs(-1)(lambda2)] x Rrs(lambda3) x a*ph(-1)(lambda1) which was improved on a previous three-band model to isolate interferences from a*ph. In terms of the importance of water optical properties in the model development, spectral and absorption characteristics were analyzed for Shitoukoumen Reservoir and Songhua Lake in Northeast China, as typical examples of inland Case-II waters. Both waters showed overwhelming absorption sum of tripton and chromophoric dissolved organic matter (CDOM) owing to their relatively low Chla contents (1.53 to 19.35 microgl(-1)). According to the optical characteristics of waters studied, optimal positions for lambda (1), lambda (2) and lambda (3) were spectrally tuned to be at 664, 684 and 705 nm, respectively. The model allowed accurate Chla estimation with a determination coefficient (R (2)) close to 0.98 and a root mean square error (RMSE) of 0.87 microgl(-1). Comparison of different models further showed the stability of the improved model, implying its potential use in water color remote sensing. Although the findings underline the rationale behind the improved model, an extensive database containing data in different water conditions and water types is required to generalize its application.

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Year:  2008        PMID: 18853267     DOI: 10.1007/s10661-008-0568-z

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  6 in total

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2.  Effect of bio-optical parameter variability on the remote estimation of chlorophyll-a concentration in turbid productive waters: experimental results.

Authors:  Giorgio Dall'Olmo; Anatoly A Gitelson
Journal:  Appl Opt       Date:  2005-01-20       Impact factor: 1.980

3.  Estimation of the remote-sensing reflectance from above-surface measurements.

Authors:  C D Mobley
Journal:  Appl Opt       Date:  1999-12-20       Impact factor: 1.980

4.  Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean.

Authors:  H R Gordon; O B Brown; M M Jacobs
Journal:  Appl Opt       Date:  1975-02-01       Impact factor: 1.980

5.  Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data.

Authors:  Yunpeng Wang; Hao Xia; Jiamo Fu; Guoying Sheng
Journal:  Sci Total Environ       Date:  2004-07-26       Impact factor: 7.963

6.  Assessment of chlorophyll-a concentration and trophic state for Lake Chagan using Landsat TM and field spectral data.

Authors:  Hongtao Duan; Yuanzhi Zhang; Bai Zhang; Kaishan Song; Zongming Wang
Journal:  Environ Monit Assess       Date:  2006-10-21       Impact factor: 3.307

  6 in total
  3 in total

1.  Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources.

Authors:  Kaishan Song; Lin Li; Zongming Wang; Dianwei Liu; Bai Zhang; Jingping Xu; Jia Du; Linhai Li; Shuai Li; Yuandong Wang
Journal:  Environ Monit Assess       Date:  2011-04-28       Impact factor: 2.513

2.  Comparison of different semi-empirical algorithms to estimate chlorophyll-a concentration in inland lake water.

Authors:  Hongtao Duan; Ronghua Ma; Jingping Xu; Yuanzhi Zhang; Bai Zhang
Journal:  Environ Monit Assess       Date:  2009-11-12       Impact factor: 2.513

3.  Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

Authors:  Salem Ibrahim Salem; Hiroto Higa; Hyungjun Kim; Hiroshi Kobayashi; Kazuo Oki; Taikan Oki
Journal:  Sensors (Basel)       Date:  2017-07-31       Impact factor: 3.576

  3 in total

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