Sensitivity of Depth-Integrated Satellite Lidar to Subaqueous Scattering

A method is presented for estimating subaqueous integrated backscatter from the CALIOP lidar. The algorithm takes into account specular reflection of laser light, laser scattering by wind-generated foam as well as sun glint and solar scattering from the foam Analyses show that the estimated subaqueous integrated backscatter is most sensitive to the estimate of transmittance used in the atmospheric correction, and is very insensitive to the estimate of wind speed used. As a case study, CALIOP data over Tampa Bay were compared to MODIS 645 nm remote sensing reflectance, which previously has been shown to be nearly linearly related to turbidity. The results indicate good correlation on nearly all CALIOP clear-free dates during the period 2006 through 2007, particularly those with relatively high atmospheric transmittance. When data are composited over the entire period the correlation is reduced but still statistically significant, an indication of variability in the biogeochemical composition in the water. Overall, the favorable results show promise for the application of satellite lidar integrated backscatter in providing information about subsurface backscatter properties, which can be extracted using appropriate model

Performance of the MODIS FLH algorithm in estuarine waters: a multi-year (2003–2010) analysis from Tampa Bay, Florida (USA)

Although satellite technology promises great usefulness for the consistent monitoring of chlorophyll-α concentration in estuarine and coastal waters, the complex optical properties commonly found in these types of waters seriously challenge the application of this technology. Blue–green ratio algorithms are susceptible to interference from water constituents, different from phytoplankton, which dominate the remote-sensing signal. Alternatively, modelling and laboratory studies have not shown a decisive position on the use of near-infrared (NIR) algorithms based on the sun-induced chlorophyll fluorescence signal. In an analysis of a multi-year (2003–2010) in situ monitoring data set from Tampa Bay, Florida (USA), as a case, this study assesses the relationship between the fluorescence line height (FLH) product from the Moderate Resolution Imaging Spectrometer (MODIS) and chlorophyll-α

A multi-sensor approach to examining the distribution of total suspended matter (TSM) in the Albemarle-Pamlico Estuarine System, NC, USA

For many coastal waters, total suspended matter (TSM) plays a major role in key biological, chemical and geological processes. Effective mapping and monitoring technologies for TSM are therefore needed to support research investigations and environmental assessment and management efforts. Although several investigators have demonstrated that TSM or suspended sediments can be successfully mapped using MODIS 250 m data for relatively large water bodies, MODIS 250 m data is of more limited use for smaller estuaries and bays or aquatic systems with complex shoreline geometry. To adequately examine TSM in the Albemarle-Pamlico Estuarine System (APES) of North Carolina, the large-scale synoptic view of MODIS and the higher spatial resolution of other sensors are required. MODIS, Landsat 7 ETM+ and FORMOSAT-2 remote sensing instrument (RSI) data were collected on 8 November, 24 November and 10 December, 2010. Using TSM images (mg/L) derived from MODIS 250 m band 1 (620–670 nm) data, Landsat 7 ETM+ 30 m band 3 (630–690 nm) and FORMOSAT-2 RSI 8 m band 3 (630−690 nm) atmospherically corrected images were calibrated to TSM for select areas of the APES. There was a significant linear relationship between both Landsat 7 ETM+ (r2 = 0.87, n = 599, P < 0.001) and FORMOSAT-2 RSI (r2 = 0.95, n = 583, P < 0.001) reflectance images and MODIS-derived TSM concentrations, thus providing consistent estimates of TSM at 250, 30 and 8 m pixel resolutions. This multi-sensor approach will support a broad range of investigations on the water quality of the APES and help guide sampling schemes of future field campaigns

DEVELOPMENT OF REGIONAL TSS ALGORITHM OVER PENANG USING MODIS TERRA (250 M) SURFACE REFLECTANCE PRODUCT

Total suspended sediment (TSS) plays a significant role in the environment. Many researchers show that TSS has a high correlation with the red portion of the visible light spectrum. The correlation is highly dependent on geography of the study area. The aim of this study was to develop specific algorithms utilizing corrected MODIS Terra 250-m surface reflectance (Rrs) product (MOD09) to map TSS over the Penang coastal area. Field measurements of TSS were performed during two cruise trips that were conducted on 8 December 2008 and 29 January 2010 over the Penang coastal area. The relationship between TSS and the surface reflectance of MOD09 was analysed using regression analysis. The developed algorithm showed that Rrs are highly correlated with the in-situ TSS with R2 is 0.838. The result shows that the Rrs product could be used to estimate TSS over the Penang area

Potential Relationships Between Urban Development and the Trophic Status of Tampa Bay Tributaries and Lake Thonotosassa, Further the Potential Effect on Public Health

This slide presentation reviews the use of remote sensing to monitor the relationships between the urban development and water quality in Tampa Bay and the tributaries. It examines the changes in land cover/land use (LU/LC) and the affects that this change has on the water quality of Tampa Bay, Lake Thonotosassa and the tributaries, and that shows the ways that these changes can be estimated with remote sensing

Utilizing neural networks for image downscaling and water quality monitoring

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods

Application of machine learning techniques to derive sea water turbidity from Sentinel-2 imagery

Earth Observation (EO) from satellites has the potential to provide comprehensive, rapid and inexpensive information about water bodies, integrating in situ measurements. Traditional methods to retrieve optically active water quality parameters from satellite data are based on semiempirical models relying on few bands, which often revealed to be site and season specific. The use of machine learning (ML) for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development and computing power. These models allow to exploit the wealth of spectral information through more flexible relationships and are less affected by atmospheric and other background factors. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of machine learning techniques. A dataset of 222 combination of turbidity measurements, collected in the North Tyrrhenian Sea – Italy from 2015 to 2021, and values of the 13 spectral bands in the pixel corresponding to the sample location was used. Two regression techniques were tested and compared: a Stepwise Linear Regression (SLR) and a Polynomial Kernel Regression. The two models show accurate and similar performance (R2 = 0.736, RMSE = 2.03 NTU, MAE = 1.39 NTU for the SLR and R2 = 0.725, RMSE = 2.07 NTU, MAE = 1.40 NTU for the Kernel). A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. The work shows that it is possible to reach a good accuracy in turbidity estimation from MSI TOA reflectance using ML models, fed by the whole spectrum of available bands, although the possible generation of errors related to atmospheric effect in turbidity estimates was not evaluated. Comparison between turbidity estimates obtained from the models with turbidity data from Copernicus CMEMS dataset named ‘Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’ produced consistent results. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the models to catch extreme events

Usando Teledetección para Identificar la Incidencia de Sedimentos del Canal del Dique en Sistemas Aquaticos Costeros

En este estudio de caso, se usó tecnología de teledetección para analizar la distribución espacial de plumas de sedimentos suspendidos del Canal del Dique, Colombia, en el Mar Caribe y cuerpos de agua costeros. Especialmente para distinguir si dichos sedimentos alcanzan las aguas del complejo coralino de Islas del Rosario. Del “Moderate Resolution Imaging Spectroradiometer (MODIS)”, se utilizó el producto de reflectancia de superficie (MOD09GQ) para estimar la reflectancia de la superficie del agua (RSA) como sustituto de la concentración de sedimentos suspendidos. Considerando el valor medio de RSA en el primer trimestre de cada año (el cual corresponde al trimestre más seco del año) se determinó la variación temporal interanual en las Islas del Rosario, en las dos bocas principales del Canal del Dique y en la boca principal del río de donde este se desprende, el Río Magdalena. Complementariamente, se usó teledetección para estimar las tendencias interanuales de precipitación en la cuenca hidrográfica del Río Magdalena y se analizó su posible relación con las tendencias de RSA. La precipitación se estimó usando el producto 3B43 V7 de la misión “Tropical Rainforest Meassuring Mission (TRMM)”. No se detectaron incrementos o decrementos en las tendencias interanuales de RSA en alguno de los sitios durante el periodo de estudio 2001-2014 (p> 0,05), pero se detectaron correlaciones significativas entre las tendencias interanuales en RSA en cada desembocadura de las cuenca hidrográfica (r = 0,57-0,90, p < 0,05) y entre éstas y la variación interanual de precipitación en la cuenca (r = 0,63-0,67, p < 0,05). Se detectaron mayores valores de RSA durante los meses de La Niña en comparación a los meses de El Niño. Con esta tecnología fue posible identificar una intersección espacial entre las plumas de sedimentos del Canal del Dique y el sistema coralino de Islas del Rosario

A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

The need for more effective environmental monitoring of the open and coastal ocean has recently led to notable advances in satellite ocean color technology and algorithm research. Satellite ocean color sensors' data are widely used for the detection, mapping and monitoring of phytoplankton blooms because earth observation provides a synoptic view of the ocean, both spatially and temporally. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Since the late 1970s, a wide variety of operational ocean color satellite sensors and algorithms have been developed. The comprehensive review presented in this article captures the details of the progress and discusses the advantages and limitations of the algorithms used with the multi-spectral ocean color sensors CZCS, SeaWiFS, MODIS and MERIS. Present challenges include overcoming the severe limitation of these algorithms in coastal waters and refining detection limits in various oceanic and coastal environments. To understand the spatio-temporal patterns of algal blooms and their triggering factors, it is essential to consider the possible effects of environmental parameters, such as water temperature, turbidity, solar radiation and bathymetry. Hence, this review will also discuss the use of statistical techniques and additional datasets derived from ecosystem models or other satellite sensors to characterize further the factors triggering or limiting the development of algal blooms in coastal and open ocean waters

Coastal turbidity derived from PROBA-V global vegetation satellite

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU