Turbidity in Apalachicola Bay, Florida from Landsat 5 TM and Field Data: Seasonal Patterns and Response to Extreme Events

Abstract

Synoptic monitoring of estuaries, some of the most bio-diverse and productive environments on Earth, is essential to study small-scale water dynamics and its role on spatiotemporal variation in water quality important to indigenous marine species and surrounding human settlements. We present a detailed study of turbidity, an optical index of water quality, in Apalachicola Bay, Florida (USA) using historical in situ measurements and Landsat 5 TM data archive acquired from 2004 to 2011. Data mining techniques such as time-series decomposition, principal component analysis, and classification tree-based models were utilized to decipher time-series for examining variations in physical forcings, and their effects on diurnal and seasonal variability in turbidity in Apalachicola Bay. Statistical analysis showed that the bay is highly dynamic in nature, both diurnally and seasonally, and its water quality (e.g., turbidity) is largely driven by interactions of different physical forcings such as river discharge, wind speed, tides, and precipitation. River discharge and wind speed are the most influential forcings on the eastern side of river mouth, whereas all physical forcings were relatively important to the western side close to the major inlet, the West Pass. A bootstrap-optimized and atmospheric-corrected single-band empirical relationship (Turbidity (NTU) = 6568.23 × (Reflectance (Band 3)) 1.95 ; R 2 = 0.77 ± 0.06, range = 0.50-0.91, N = 50) is proposed with seasonal thresholds for its application in various seasons. The validation of this relationship yielded R 2 = 0.70 ± 0.15 (range = −0.96-0.97; N = 38; RMSE = 7.78 ± 2.59 NTU; Bias (%) = −8.70 ± 11.48). Complex interactions of physical forcings and their effects on water dynamics have been discussed in detail using Landsat 5 TM-based turbidity maps during major events between 2004 and 2011. Promising results of the single-band turbidity algorithm with Landsat 8 OLI imagery suggest its potential for long-term monitoring of water turbidity in a shallow water estuary such as Apalachicola Bay.

Water Quality Drivers in 11 Gulf of Mexico Estuaries

“…Joshi et al [39], for example, found that turbidity in Apalachicola Bay, Florida, (located approximately 300 km from Mobile Bay to the west and Tampa Bay to the southeast) is largely driven by a combination of river discharge, wind speed, tides and precipitation, and that the interactions of these physical forcings affect different sections of the bay in dynamic ways. We found that Mobile Bay turbidity is driven by a combination of river discharge, wind speed, water level and precipitation, and that the three Florida estuaries (TB, CH, SB) are driven by wind speed and discharge.…”

“…In general, we assumed that MODIS Band 1 observations have minimal contributions from light reflected from the sea bottom in estuarine waters deeper than about 2.8 m due to the strong absorption of red light by water [21]. This approach has been used several times in the past, with mixed success, in different estuaries and coastal waters around the world [6,13,14,21,[33][34][35][36][37][38][39]. Other bio-optical algorithms that utilize blue, green, or yellow bands to estimate parameters such as chlorophyll-a concentration are usually contaminated by reflectance from the ocean bottom in shallow areas and don't provide accurate estimates.…”

An estuarine-tuned quasi-analytical algorithm (QAA-V): assessment and application to satellite estimates of SPM in Galveston Bay following Hurricane Harvey

“…In recent years, both empirical and semi-analytical models have been frequently used to link satellite observations and in-water properties, such as IOPs, vertical diffuse attenuation coefficients (K d ), suspended particulate matter (SPM) concentrations, CDOM, pigment concentrations, phytoplankton cell counts and cell size, and particle size (D'Sa et al, 2003(D'Sa et al, , 2007Pan et al, 2010;Chen et al, 2013;Brewin et al, 2015;Joshi et al, 2017b). Empirical relationships are mathematical formulations (e.g., simple or multiple regressions) that directly link water-leaving measurements to the parameter of interest in surface waters.…”

Water Optics and Water Colour Remote Sensing

“…Turbidity climatology over an eight-year period (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) in Apalachicola Bay was investigated based on remote sensing estimation from Landsat 5 TM and Landsat 8 OLI imagery using a single-band empirical relationship of band 3 [22]. Deng et al [23] tested the applicability of the Vertically Generalised Production Model (VGPM) to estimate phytoplankton primary production by comparing the model-derived and the in situ results, investigated the long-term temporal-spatial variations in primary production using MODIS data, and further discussed the potential affecting factors in Lake Taihu.…”