Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands

Abstract

"Algorithms for remote estimation of chlorophyll-a in coastal and inland waters using red and near infrared bands" (2010 Abstract: Remote sensing algorithms that use red and NIR bands for the estimation of chlorophyll-a concentration [Chl] can be more effective in inland and coastal waters than algorithms that use blue and green bands. We tested such two-band and three-band red-NIR algorithms using comprehensive synthetic data sets of reflectance spectra and inherent optical properties related to various water parameters and a very consistent in situ data set from several lakes in Nebraska, USA. The two-band algorithms tested with MERIS bands were R rs (708)/R rs (665) and R rs (753)/R rs (665). The three-band algorithm with MERIS bands was in the form R3 = [R rs 1 (665)  R rs 1 (708)] × R rs (753). It is shown that the relationships of both R rs (708)/R rs (665) and R3 with [Chl] do not depend much on the absorption by CDOM and non-algal particles, or the backscattering properties of water constituents, and can be defined in terms of water absorption coefficients at the respective bands as well as the phytoplankton specific absorption coefficient at 665 nm. The relationship of the latter with [Chl] was established for [Chl] > 1 mg/m 3 and then further used to develop algorithms which showed a very good match with field data and should not require regional tuning. 6, 28-36 (1985). (2010) 1161-1166 (1979). 12. A. Vasilkov, and O. Kopelevich, -Reasons for the appearance of the maximum near 700 nm in the radiance spectrum emitted by the ocean layer,‖ Oceanology (Mosc.) 22, 697-701 (1982). 13. Published in Optics Express

Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8

“…The inferring methods make use of site-sensor-specific empirical/semi-empirical algorithms (e.g. Gitelson et al, 2008;Gilerson et al, 2010;Gurlin et al, 2011;Kudela et al, 2015), or spectral inversion schemes of physically based bio-optical models (e.g. Brando & Dekker, 2003;Giardino et al, 2007;Heege et al, 2014;Gege, 2014).…”

Estimation of chlorophyll-a concentration in turbid productive waters using airborne hyperspectral data

“…Previous studies have shown that the two-band NIR-red model (Eq. (2)) with l 1 and l 2 set to match the MERIS spectral channels at 665 nm and 708 nm, respectively, gives accurate results for data simulated by radiative transfer equations (Gilerson et al, 2009(Gilerson et al, , 2010 as well as data acquired by field spectrometers (Gitelson et al, 2008) and spaceborne sensors Moses et al, 2009a, b). Considering the aforementioned limitation on l 3 and choosing AISA spectral channels that were located the closest to the MERIS channels for l 1 and l 2 , the three-band and twoband NIR-red models for AISA data were formulated as, Three-band AISA NIR-red Model:…”

“…Previous experimental (Gitelson et al, 2008) and modeling (Dall'Olmo and Gitelson, 2006;Gilerson et al, 2010) results have shown that the NIR-red models are not very sensitive to variations in the biogeochemical characteristics of waters besides chl-a concentration. Thus, the variations in the slope and intercept across the multiple dates ( Fig.…”

“…These models have been developed and validated using data acquired by field spectrometers (Gitelson, 1992;Gitelson et al, 2008Gitelson et al, , 2009Gurlin et al, 2011), satellites Moses et al, 2009b) and data simulated using the radiative transfer model, Hydrolight (Gilerson et al, 2010). The optimal spectral ranges for the location of these wavelengths were found to be, l 1 ¼ 660e670 nm, l 2 ¼ 700e730 nm, and l 3 ¼ 740e760 nm Gitelson et al, 2008).…”

“…The seasonal variations could be due to the inability of the algorithms in their specific parameterized forms to account for seasonal changes in the biogeochemical characteristics of water or due to variations in the atmospheric effects on the images collected in different seasons, which were not accurately removed. However, the three-band and two-band NIRred models (Equations (1) and (2)) have been previously shown to yield accurate estimates of chl-a concentration irrespective of seasonal changes in the biogeochemical properties of water (e.g., Gitelson et al, 2008;Moses et al, 2009b;Gilerson et al, 2010;Gurlin et al, 2011;, and it is of interest to investigate their sensitivity to variations in atmospheric effects. In this study, we have applied these NIR-red models (Equations (1) and (2)) to multi-temporal airborne hyperspectral reflectance data w a t e r r e s e a r c h 4 6 ( 2 0 1 2 ) 9 9 3 e1 0 0 4 acquired by the AISA-Eagle hyperspectral sensor over several inland water bodies in the Midwestern United States and investigated the performances of the models by addressing the following questions: …”