Remote sensing of tidal chlorophyll-a variations in estuaries

Catts, Glenn P.; Khorram, Siamak; Cloern, James E.; Knight, Allen W.; DeGloria, Stephen D.

doi:10.1080/01431168508948318

Cited by 23 publications

(6 citation statements)

References 10 publications

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“…The sample collection is complex as well (Yamada et al, 2004). Satellite remote sensing is commonly used with the capability of synoptic observation to track water quality utilizing a bio-optical model that describes the connectionbetween water and apparent optical properties components (Catts et al, 1985). The seasonal variations found in sea surface Chlorophyll-a and the surface thermal conditions are perceived in other biological conditions.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

Liu

Wei

Muthu³

et al. 2020

European Journal of Remote Sensing

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In the recent past, the Satellite authenticated synoptic instrument has been used to retrieve the water quality variables like chlorophyll, suspended materials and the pigmented dissolved organic matter. However, the use of chlorophyll phytoplankton endeavors acts as a proxy and strongly overestimates the contribution to the annual pelagic carbon flows from spring production. Further, Remote Sensing assisted Sparse Statistical Modelling (RSSSM)has been proposed to determine the chlorophyll-a concentration seasonal variations and spatial/temporal structure in the Hailing Bay. It provides high correlation information between the water surface environment and organic matter. Besides, it provides the highest possible correlation coefficient value and gives a more practical representation at a clear water reference site using a lab-scale simulation setup. Thus in considering the coastal system, the seasonal variation in chlorophyll ratios has been reviewed and outcomes has been analyzed using effective experimental validation at lab scale.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

Liu

Wei

Muthu³

et al. 2020

European Journal of Remote Sensing

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“…The semi-monthly variation is caused by the spring-neap tidal cycle (Bever and MacWilliams, 2013). The diurnal variation of SPM is largely due to the diurnal tide (Schoellhamer et al, 2008) as well as semi-diurnal tide (Stanev et al, 2007), while the diurnal variation of chlorophyll-a is related to the diurnal variations of light condition and tide (Catts et al, 1985;Powell et al, 1989;Cloern, 1996). Specifically, tidal advection (Conomos, 1979) moves chlorophyll-a patches (as well as SPM) back and forth and can potentially induce daily variation of local chlorophyll-a concentration observed at fixed stations.…”

Section: Influence Of Tidesmentioning

confidence: 99%

Light Regulation of Phytoplankton Growth in San Francisco Bay Studied Using a 3D Sediment Transport Model

et al. 2021

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In San Francisco Bay (SFB), light availability is largely determined by the concentration of suspended particulate matter (SPM) in the water column. SPM exhibits substantial variation with time, depth, and location. To study how SPM influences light and phytoplankton growth, we coupled a sediment transport model with a hydrodynamic model and a biogeochemical model. The coupled models were used to simulate conditions for the year of 2011 with a focus on northern SFB. For comparison, two simulations were conducted with ecosystem processes driven by SPM concentrations supplied by the sediment transport model and by applying a constant SPM concentration of 20 mg l–1. The sediment transport model successfully reproduced the general pattern of SPM variation in northern SFB, which improved the chlorophyll-a simulation resulting from the biogeochemical model, with vertically integrated primary productivity varying greatly, from 40 g[C] m–2 year–1 over shoals to 160 g[C] m–2 year–1 in the deep channel. Primary productivity in northern SFB is influenced by euphotic zone depth (Ze). Our results show that Ze in shallow water regions (<2 m) is mainly determined by water depth, while Ze in deep water regions is controlled by SPM concentration. As a result, Ze has low (high) values in shallow (deep) water regions. Large (small) differences in primary productivity exist between the two simulations in deep (shallow) water regions. Furthermore, we defined a new parameter Flight for “averaged light limitation” in the euphotic zone. The averaged chlorophyll-a concentration in the euphotic zone and Flight share a similar distribution such that both have high (low) values in shallow (deep) water regions. Our study demonstrates that light is a critical factor in regulating the phytoplankton growth in northern SFB, and a sediment transport model improves simulation of light availability in the water column.

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“…A range of pre-processing and quality assurance and control procedures were performed on the imagery, including converting all Landsat imagery to top-of-atmosphere reflectance (Chander et al, 2009), identifying missing regions of Landsat 1-3 MSS scenes, accounting for the failure of the scan-line corrector (SLC) in Landsat 7 ETM+ images (Scaramuzza et al, 2005;Chen et al, 2011;Catts et al, 1985), and masking of cloud shadows (Zhu and Woodcock, 2012;Irish, 2000;Craven et al, 2002). The location of tanks within the resulting images was determined using a shapefile of tank boundaries obtained from the Karnataka State Remote Sensing Application Centre (KSR-SAC, karnataka.gov.in/ksrsac), supplemented by 1970s topographic maps (surveyofindia.gov.in) for the beginning of the study period.…”

Section: Remote Sensing Analysismentioning

confidence: 99%

Author comments

Penny¹

2017

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The complexity and heterogeneity of human water use over large spatial areas and decadal timescales can impede the understanding of hydrological change, particularly in regions with sparse monitoring of the water cycle. In the Arkavathy watershed in southern India, surface water inflows to major reservoirs decreased over a 40-year period during which urbanization, groundwater depletion, modification of the river network, and changes in agricultural practices also occurred. These multiple, interacting drivers combined with limited hydrological monitoring make attribution of the causes of diminishing water resources in the watershed challenging and impede effective policy responses. To mitigate these challenges, we developed a novel, spatially distributed dataset to understand hydrological change by characterizing the residual trends in surface water extent that remain after controlling for precipitation variations and comparing the trends with historical land use maps to assess human drivers of change. Using an automated classification approach with subpixel unmixing, we classified water extent in nearly 1700 man-made lakes, or tanks, in Landsat images from 1973 to 2010. The classification results compared well with a reference dataset of water extent of tanks (R 2 = 0.95). We modeled the water extent of 42 clusters of tanks in a multiple regression on simple hydrological covariates (including precipitation) and time. Inter-annual variability in precipitation accounted for 63 % of the predicted variability in water extent. However, precipitation did not exhibit statistically significant trends in any part of the watershed. After controlling for precipitation variability, we found statistically sig-nificant temporal trends in water extent, both positive and negative, in 13 of the clusters. Based on a water balance argument, we inferred that these trends likely reflect a nonstationary relationship between precipitation and watershed runoff. Independently of precipitation, water extent increased in a region downstream of Bangalore, likely due to increased urban effluents, and declined in the northern portion of the Arkavathy. Comparison of the drying trends with land use indicated that they were most strongly associated with irrigated agriculture, sourced almost exclusively by groundwater. This suggests that groundwater abstraction was a major driver of hydrological change in this watershed. Disaggregating the watershed-scale hydrological response via remote sensing of surface water bodies over multiple decades yielded a spatially resolved characterization of hydrological change in an otherwise poorly monitored watershed. This approach presents an opportunity to understand hydrological change in heavily managed watersheds where surface water bodies integrate upstream runoff and can be delineated using satellite imagery.

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Remote sensing of tidal chlorophyll-a variations in estuaries

Cited by 23 publications

References 10 publications

RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

RETRACTED ARTICLE: Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling

Light Regulation of Phytoplankton Growth in San Francisco Bay Studied Using a 3D Sediment Transport Model

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