scholarly journals Estimates of radiance reflected towards the zenith at the surface of the sea

Ocean Science ◽  
2010 ◽  
Vol 6 (4) ◽  
pp. 861-876 ◽  
Author(s):  
E. Aas
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spectral Range ◽  
Solar Zenith Angle ◽  
The Other ◽  
Sea Surface ◽  
Wind Speeds ◽  
Irradiance Data ◽  
Sky Radiance ◽  
Relative Errors

Abstract. Remote sensing of water colour by ship-mounted sensors represents an important tool for the validation of satellite products and the monitoring of water quality. The recorded radiance from the sea has to be corrected for the surface-reflected radiance from sun and sky in order to obtain the water-leaving radiance. Here the simple case of radiance reflected towards the zenith is studied. A set of observed sky radiance and solar irradiance data from Oslo has been used together with a Gaussian slope distribution for the sea surface in order to estimate the reflected radiance. The spectral range studied is 405–650 nm, the solar zenith angles are in the range 37°–76°, and the wind speeds are up to 10 m s−1. The analysis of the results show that the reflected radiance has to be separated into three contributions: sky radiance and sun rays reflected at the foam-free surface and irradiance reflected by whitecaps and foam. It is then demonstrated that by using four input values, namely the downward irradiance, the sky radiance from the zenith, the solar zenith angle and the wind speed, it is possible to obtain by simple expressions estimates of the reflected radiance that only differ from the former calculated values by relative errors of less than 5%. The analysis also indicates that for the spectral range studied neither the water-leaving radiance nor the surface-reflected radiance can be disregarded relative to the other one in the Case 2 waters of the Oslofjord-Skagerrak area. The results form a first step towards the study of reflected radiance in viewing angles differing from the nadir direction.

scholarly journals Estimates of radiance reflected towards the zenith at the surface of the sea

2010 ◽  
Vol 7 (3) ◽  
pp. 1059-1102 ◽  
Author(s):  
E. Aas
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Spectral Range ◽  
Solar Zenith Angle ◽  
The Other ◽  
Sea Surface ◽  
Wind Speeds ◽  
Irradiance Data ◽  
Sky Radiance ◽  
Relative Errors

Abstract. Remote sensing of water colour by ship-mounted sensors represents an important tool for the validation of satellite products and the monitoring of water quality. The recorded radiance from the sea has to be corrected for the surface-reflected radiance from sun and sky in order to obtain the water-leaving radiance. Here the simple case of radiance reflected towards the zenith is studied. A set of observed sky radiance and solar irradiance data from Oslo has been used together with a Gaussian slope distribution for the sea surface in order to estimate the reflected radiance. The spectral range studied is 405–650 nm, the solar zenith angles are in the range 37°–76°, and the wind speeds are up to 10 m s−1. The analysis of the results show that the reflected radiance has to be separated into three contributions: sky radiance and sun rays reflected at the foam-free surface and irradiance reflected by whitecaps and foam. It is then demonstrated that by using four input values, namely the downward irradiance, the sky radiance from the zenith, the solar zenith angle and the wind speed, it is possible to obtain by simple expressions estimates of the reflected radiance that only differ from the former calculated values by relative errors of 4% or less. The analysis also indicates that for the spectral range studied neither the water-leaving radiance nor the surface-reflected radiance can be disregarded relative to the other one in the Case 2 waters of the Oslofjord-Skagerrak area. The results form a first step towards the study of reflected radiance in viewing angles differing from the nadir direction.

scholarly journals Does Social Distancing Have an Effect on Water Quality? : An Evidence from Chlorophyll-a Level in the Water of Populated Southeast Asian Coasts

2020 ◽  
Author(s):  
Andrio Adwibowo
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Anthropogenic Activities ◽  
Remote Sensing Data ◽  
Sea Surface ◽  
Nutrient Contents ◽  
Social Distancing

The COVID 19 related social distancing is hypothesized can affect the environmental quality including the air and water quality. Correspondingly, this study aims to study how the reduction of activities of people living near the rivers and the coastal areas due to social distancing may decrease the discharges of materials and nutrients to the water body. The chlorophyll-a was used as bio indicators of nutrient contents related to the anthropogenic activities in the coast. The study was conducted in the Jakarta coast considering that this coast was surrounded by populated cities with total population equal to 16 million people. The chlorophyll-a was measured in mg/m3 and monitored using remote sensing data from January to April 2020 representing the period before and after the implementation of social distancing. The determinant environmental factor measured was sea surface temperature (0C). The study considered that there were reductions of levels and areas of chlorophyll-a in the coast. The chlorophyll-a levels were reduced from January to April (p<0.05). The chlorophyll-a levels for January, February, March, and April were 7.36 mg/m3 (95%CI: 6.34-8.37), 7.90 mg/m3 (95%CI: 7.32-8.47), 6.52 mg/m3 (95%CI: 5.37-7.66), and 4.21 mg/m3 (95%CI: 3.34-5.07) respectively. However, the differences of chlorophyll-a were not influenced by the sea surface temperature factor (p>0.05). Based on remote sensing data in January and February, the sizes of coastal areas with chlorophyll-a levels >7.00 mg/m3 were larger than areas observed in March and April. Contrarily, the coastal area sizes with low chlorophyll-a levels <5.00 mg/m3 were increasing in April. To conclude the dynamic of anthropogenic activities in coastal setting is responsible and associated with the water quality and nutrient contents as indicated by chlorophyll-a levels.

scholarly journals Validation of an Improved Statistical Theory for Sea Surface Whitecap Coverage Using Satellite Remote Sensing Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3306 ◽  
Author(s):  
Haili Wang ◽  
Yongzeng Yang ◽  
Changming Dong ◽  
Tianyun Su ◽  
Baonan Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Statistical Theory ◽  
Satellite Remote Sensing ◽  
Wave Spectrum ◽  
Remote Sensing Data ◽  
The Other ◽  
Sea Surface ◽  
Westerly Wind ◽  
Whitecap Coverage ◽  
Spectrum Width

The whitecap coverage at the sea surface is affected by the ratio of kinetic energy to potential energy, θ, the wave spectrum width parameter, ρ, and other factors. This paper validates an improved statistical theory for surface whitecap coverage. Based on the theoretical analysis, we find that the whitecap coverage is more sensitive to ρ than to θ, and the improved statistical theory for surface whitecap coverage is suitable in regions of rough winds and waves. The satellite-derived whitecap coverage data in the westerly wind zone is used to validate the improved theory. The comparison between the results from theory and observations displays a better performance from the improved theory relative to the other methods tested.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

Modeling and risk analysis of bromate formation from ozonation of bromide-containing waters

1996 ◽  
Vol 34 (7-8) ◽  
pp. 79-85 ◽  
Author(s):  
Rengao Song ◽  
Roger Minear ◽  
Paul Westerhoff ◽  
Gary Amy
Keyword(s):  
Water Quality ◽  
Risk Analysis ◽  
Ammonia Concentration ◽  
Quality Characteristics ◽  
The Other ◽  
High Ph ◽  
Treatment Processes ◽  
Ozone Dose ◽  
Other Hand

Empirical bromate formation models were developed from batch ozonation data to simulate the effects of important water quality characteristics and treatment processes on bromate formation. Bromate formation was favored at high pH, bromide concentration, alkalinity, and ozone dose. On the other hand, increasing DOC and ammonia concentration decreased bromate formation. Validation of the bromate models demonstrated that the models accurately simulated bromate formation. Risk analysis of bromate formation was performed on 5 utilities in which ozone was used, and it was concluded that under typical ozonation conditions, the associated risk related to bromate formation could be as high as 10−3.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

scholarly journals Limnology of Merbok estuary, Kedah, Malaysia

2015 ◽  
Vol 41 (1) ◽  
pp. 13-19
Author(s):  
Kaniz Fatema ◽  
Wan Maznah Wan Omar ◽  
Mansor Mat Isa
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Suspended Solids ◽  
Total Suspended Solids ◽  
Quality Parameters ◽  
The Other ◽  
Wet Season ◽  
Hand Temperature ◽  
Do Concentration ◽  
Merbok Estuary

Water quality in three different stations of Merbok estuary was investigated limnologically from October, 2010 to September, 2011. Water temperature, transparency and total suspended solids (TSS) varied from 27.45 - 30.450C, 7.5 - 120 cm and 10 -140 mg/l, respectively. Dissolved Oxygen (DO) concentration ranged from 1.22-10.8 mg/l, while salinity ranged from 3.5-35.00 ppt. pH and conductivity ranged from 6.35 - 8.25 and 40 - 380 ?S/cm, respectively. Kruskal Wallis H test shows that water quality parameters were significantly different among the sampling months and stations (p<0.05). This study revealed that DO, salinity, conductivity and transparency were higher in wet season and TSS was higher in dry season. On the other hand, temperature and pH did not follow any seasonal trends.Bangladesh J. Zool. 41(1): 13-19, 2013

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