Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or develope...
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Remote sensing of suspended particulate matter, SPM, from space has long been used to assess its spatio-temporal variability in various coastal areas. The associated algorithms were generally site specific or developed over a relatively narrow range of concentration, which make them inappropriate for global applications (or at least over broad SPM range). In the frame of the GlobCoast project, a large in situ data set of SPM and remote sensing reflectance, R-rs(lambda), has been built gathering together measurements from various coastal areas around Europe, French Guiana, North Canada, Vietnam, and China. This data set covers various contrasting coastal environments diversely affected by different biogeochemical and physical processes such as sediment resuspension, phytoplankton bloom events, and rivers discharges (Amazon, Mekong, Yellow river, MacKenzie, etc.). The SPM concentration spans about four orders of magnitude, from 0.15 to 2626 g center dot m(-3). Different empirical and semi-analytical approaches developed to assess SPM from R-rs(lambda) were tested over this in situ data set. As none of them provides satisfactory results over the whole SPM range, a generic semi-analytical approach has been developed. This algorithm is based on two standard semi-analytical equations calibrated for low-to-medium and highly turbid waters, respectively. A mixing law has also been developed for intermediate environments. Sources of uncertainties in SPM retrieval such as the bio-optical variability, atmospheric correction errors, and spectral bandwidth have been evaluated. The coefficients involved in these different algorithms have been calculated for ocean color (SeaWiFS, MODIS-A/T, MERIS/OLCI, VIIRS) and high spatial resolution (LandSat8-OLI, and Sentinel2-MSI) sensors. The performance of the proposed algorithm varies only slightly from one sensor to another demonstrating the great potential applicability of the proposed approach over global and contrasting coastal waters.
Secchi disk depth (Z(sd)) is an essential environmental factor for studying ecosystem dynamics and biogeochemical processes in aquatic environments. Monitoring the long-term changes in water transparency is critical t...
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Secchi disk depth (Z(sd)) is an essential environmental factor for studying ecosystem dynamics and biogeochemical processes in aquatic environments. Monitoring the long-term changes in water transparency is critical to predict the cascading impacts of climate change on marine ecosystems. We investigated the seasonal and interannual dynamics of Z(sd) in the eastern coast of Peninsula Malaysia (ECPM) and the Straits of Malacca (SoM) using a 21-year time series of MODIS ocean color data. To enable the reliable assessment of Z(sd) and its long-term variability, the performance of existing and regional algorithms was investigated using in-situ optical measurements collected during different monsoon seasons and in various environmental conditions. Our validation results showed that the existing Z(sd) algorithms performed adequately, but exhibited large errors, especially at relatively high Z(sd) values. On the other hand, the regional empirical algorithm based on a direct relationship between remote sensing reflectance and Z(sd) showed significant improvements by reducing the overall bias observed in existing Z(sd) schemes. The results indicated that the monthly climatological Z(sd) over the period 2000-2020 showed distinct patterns in different seasons. The ECPM waters had deeper Z(sd) than SoM waters. Maximum transparency usually occurred during the southwest and spring inter-monsoon and minimum transparency occurred during the northeast monsoon. Long-term seasonal evolution of Z(sd) reveals that widespread and persistent anomalies dominated the ECPM and SoM regions. Interannual trends indicate notable and complex changes in Z(sd) that were probably driven by variability in the ocean-atmosphere dynamics of Nino-Southern Oscillation (ENSO) and local environmental conditions. This study highlights the extensive analysis of Z(sd) status and its spatio-temporal pattern from space, which can significantly benefit long-term ocean monitoring efforts in the ECPM and SoM regions
The propagation of downwelling irradiance at wavelength l from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, up down arrow(K) over bar (d)(lambda). There are two standard meth...
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The propagation of downwelling irradiance at wavelength l from surface to a depth (z) in the ocean is governed by the diffuse attenuation coefficient, up down arrow(K) over bar (d)(lambda). There are two standard methods for the derivation of ($) over bar (d)( lambda) in remote sensing, which both are based on empirical relationships involving the blue-to-green ratio of ocean color. Recently, a semianalytical method to derive beta(K) over bar (d)( lambda) from reflectance has also been developed. In this study, using (K) over bar (d)(490) and (K) over bar (d)( 443) as examples, we compare the (K) over bar (d)(lambda) values derived from the three methods using data collected in three different regions that cover oceanic and coastal waters, with (K) over bar (d)(490) ranging from similar to0.04 to 4.0 m(-1). The derived values are compared with the data calculated from in situ measurements of the vertical profiles of downwelling irradiance. The comparisons show that the two standard methods produced satisfactory estimates of (K) over bar (d)(lambda) in oceanic waters where attenuation is relatively low but resulted in significant errors in coastal waters. The newly developed semianalytical method appears to have no such limitation as it performed well for both oceanic and coastal waters. For all data in this study the average of absolute percentage difference between the in situ measured and the semianalytically derived (K) over bar (d) is similar to 14% for lambda = 490 nm and similar to 11% for lambda = 443 nm.
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