Normalized difference chlorophyll index: A novel model for remote estimation of chlorophyll- a concentration in turbid productive waters

• Published in: Remote sensing of environment Vol. 117; pp. 394 - 406
• Main Authors: Mishra, Sachidananda, Mishra, Deepak R.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.02.2012; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; Mobile Bay; United States; Atlantic Ocean; North American Atlantic; Chesapeake Bay; North Atlantic; Gulf of Mexico; Alabama; Northwest Atlantic; Delaware Bay; Mississippi River; ASW, USA, Mississippi R. Delta; ANW, USA, Delaware Bay; ASW, Mexico Gulf; ASW, USA, Alabama, Mobile Bay; ANW, USA, Chesapeake Bay; Normalized difference chlorophyll index; Chlorophyll- a; Turbid productive waters; MEdium Resolution Imaging Spectrometer; Spectral algorithm; Remote sensing reflectance; algorithms; turbidity; Chlorophyll-a; Vegetation index; Bias; simulation; Research; Image; North America; Modeling; Variance; Result; Coastal water; estuaries; deltas; calibration; news; Validation; models; Estimation; Prediction; remote sensing; concentration; Chlorophyll a; Geographical division; Type; Quadratic function
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2011.10.016

We propose a normalized difference chlorophyll index (NDCI) to predict chlorophyll-a (chl- a) concentration from remote sensing data in estuarine and coastal turbid productive (case 2) waters. NDCI calibration and validation results derived from simulated and MEdium Resolution Imaging Spectrometer (MERIS) datasets show its potential application to widely varying water types and geographic regions. A quadratic function ( R 2 = 0.95, p < 0.0001) accurately explained the variance in the simulated data for a chl- a range of 1–60 mg m − 3 . Similarly a twofold calibration and validation of chl- a models using MERIS dataset, (chl- a range: 0.9–28.1 mg m − 3 ) yielded R 2 of 0.9, and RMSE of ~ 2 mg m − 3 respectively. NDCI was applied on images over the Chesapeake Bay and Delaware Bay, the Mobile Bay, and the Mississippi River delta region in the northern Gulf of Mexico. The newly developed algorithm was successful in predicting chl- a concentration with approximately 12% overall bias for all above study regions. Findings from this research imply that NDCI can be successfully used on MERIS images to quantitatively monitor chl- a in inland coastal and estuarine waters. In case of remote coastal waters with no ground truth data, NDCI can be used to detect algal bloom and qualitatively infer chl- a concentration ranges very similar to NDVI's application in terrestrial vegetation studies. ► We propose a new index to quantify chlorophyll- a in case 2 waters using MERIS data. ► Model calibration and validation was performed using simulated, field, and MERIS data. ► NDCI produced highest accuracy in quantifying chl- a in coastal waters. ► NDCI can qualitatively infer and map chl- a when/where in situ data is unavailable.