NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study

Yosef Z. Yacobi, Wesley J. Moses, Semion Kaganovsky, Benayahu Sulimani, Bryan C. Leavitt, Anatoly A. Gitelson

Research output: Contribution to journalArticle

80 Citations (Scopus)

Abstract

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21mgm-3 during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.

Original languageEnglish (US)
Pages (from-to)2428-2436
Number of pages9
JournalWater Research
Volume45
Issue number7
DOIs
StatePublished - Mar 2011

Fingerprint

Chlorophyll
Lakes
coastal water
reflectance
chlorophyll a
lake
Parameterization
Water
parameterization
Calibration
calibration
MERIS
inland water
Mean square error
water
near infrared
Infrared radiation
wavelength
Wavelength

Keywords

  • Hyperspectral
  • MERIS
  • Near-infra-red
  • Remote sensing

ASJC Scopus subject areas

  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution

Cite this

NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters : Lake Kinneret case study. / Yacobi, Yosef Z.; Moses, Wesley J.; Kaganovsky, Semion; Sulimani, Benayahu; Leavitt, Bryan C.; Gitelson, Anatoly A.

In: Water Research, Vol. 45, No. 7, 03.2011, p. 2428-2436.

Research output: Contribution to journalArticle

Yacobi, Yosef Z. ; Moses, Wesley J. ; Kaganovsky, Semion ; Sulimani, Benayahu ; Leavitt, Bryan C. ; Gitelson, Anatoly A. / NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters : Lake Kinneret case study. In: Water Research. 2011 ; Vol. 45, No. 7. pp. 2428-2436.
@article{81adfbff4eaa46c58132671274d58fc5,
title = "NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study",
abstract = "A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21mgm-3 during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.",
keywords = "Hyperspectral, MERIS, Near-infra-red, Remote sensing",
author = "Yacobi, {Yosef Z.} and Moses, {Wesley J.} and Semion Kaganovsky and Benayahu Sulimani and Leavitt, {Bryan C.} and Gitelson, {Anatoly A.}",
year = "2011",
month = "3",
doi = "10.1016/j.watres.2011.02.002",
language = "English (US)",
volume = "45",
pages = "2428--2436",
journal = "Water Research",
issn = "0043-1354",
publisher = "Elsevier Limited",
number = "7",

}

TY - JOUR

T1 - NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters

T2 - Lake Kinneret case study

AU - Yacobi, Yosef Z.

AU - Moses, Wesley J.

AU - Kaganovsky, Semion

AU - Sulimani, Benayahu

AU - Leavitt, Bryan C.

AU - Gitelson, Anatoly A.

PY - 2011/3

Y1 - 2011/3

N2 - A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21mgm-3 during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.

AB - A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chl-a concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21mgm-3 during four field campaigns. A two-band model without re-parameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes.

KW - Hyperspectral

KW - MERIS

KW - Near-infra-red

KW - Remote sensing

UR - http://www.scopus.com/inward/record.url?scp=79952533386&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952533386&partnerID=8YFLogxK

U2 - 10.1016/j.watres.2011.02.002

DO - 10.1016/j.watres.2011.02.002

M3 - Article

C2 - 21376361

AN - SCOPUS:79952533386

VL - 45

SP - 2428

EP - 2436

JO - Water Research

JF - Water Research

SN - 0043-1354

IS - 7

ER -