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Optical remote sensing of marine, coastal, and inland waters
Published in P. Dakin John, G. W. Brown Robert, Handbook of Optoelectronics, 2017
Starting with SeaWiFS in 1997, we now have a long-time series of chlorophyll data, which makes it possible to understand seasonal and interannual variability in the primary productivity of the global ocean. Ocean color sensors continue to monitor key ecosystems such as the major upwelling regions off the coast of Peru, West Africa, and South Africa/Namibia, which support some of the world’s richest fisheries. In the North Atlantic, numerous studies have tied the spawning of fish and other marine species to the onset of the spring plankton bloom. In high latitude waters, where access is difficult and expensive, satellite measurements of ocean color have shown how massive plankton blooms support a rich abundance of life. The blooms often start near the ice edge and expand into ice-free water. Variations in the recruitment to commercially important fish stocks have been linked to phytoplankton phenology (timing of the seasonal cycle) as observed by ocean color satellites.
Satellite Imaging and Sensing
Published in John G. Webster, Halit Eren, Measurement, Instrumentation, and Sensors Handbook, 2017
The launched (October 1997) Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which is a part of MTPE, provides quantitative data on global ocean biooptical properties to the earth science community. SeaWiFS is a follow-on sensor to the Coastal Zone Color Scanner (CZCS), which ceased operations in 1986. See Figure 79.3 for a channel description of these two sensors; notice that all channels are concentrated in the (0.4, 0.7 μm) interval of the electromagnetic spectrum.
Sensitivity analysis of vertical mixing schemes in a regional domain using modular Ocean model
Published in ISH Journal of Hydraulic Engineering, 2023
Mousumi Sarkar, Siddhesh Tirodkar, Rajesh Chauhan, Srinivas L. Vellala, Sridhar Balasubramanian, Manasa R. Behera
where, is the latent heat lost by the ocean due to evaporation, latent heat of vaporization for fresh water, , and is the mass flux of fresh water leaving the ocean due to evaporation, which is provided as input. Chlorophyll-a concentration from NASA SeaWiFS (Sea-viewing Wide Field-of-view Sensor) is used in short-wave penetration scheme that uses the parameterization originally developed by Morel and Antoine (1994). Rainfall rate is collected from Tropical Rainfall Measuring Mission (TRMM) (Huffman and Pendergrass 2019). Daily climatology of all of these air-sea fluxes, and weekly climatology for chlorophyll are calculated using the corresponding datasets, from Jan, 2000 to Dec, 2008, the same time period used in calculating climatological wind forcing. Daily climatological sensible heat flux is taken from Japanese 25-year ReAnalysis (JRA-25) (Onogi et al. 2007). Table 1 includes the details about the forcing used for the study. In case of the spin up runs for the first 10 years, the model is forced with data after calculating the climatology. After spin up, the model is forced with all the forcing data for another 3 years (i.e. from Jan 2000 to Dec 2002). In this paper, the seasons are considered as DJF (Dec-Jan-Feb), MAM (Mar-Apr-May), JJAS (Jun-Jul-Aug-Sep), and ON (Oct-Nov) based on the direction of wind flow. BoB faces north-easterly and south-westerly winds in DJF during NEM and in JJAS during SWM, respectively.
Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives
Published in Critical Reviews in Environmental Science and Technology, 2020
Xiaoli Wei, Ni-Bin Chang, Kaixu Bai, Wei Gao
The SeaWiFS is aboard the GeoEye's OrbView-2 (a.k.a. SeaStar) satellite operated between September 1997 and December 2010. The primary mission of SeaWiFS was to derive ocean color parameters (McClain, Feldman, & Hooker, 2004). The reflectance of ocean occupies a small part in the TOA reflectance and is hard to separate from the atmosphere, making the SeaWiFS more accurate and stable in calibration during operation (Gordon & Wang, 1994; Eplee et al., 2012). The SeaWiFS AOD retrieval algorithm is also based on the DB algorithm over land (Hsu, Christina, et al., 2004, Hsu et al., 2006). Wang et al. (2001) corrected the sun glint contamination of the SeaWiFS AOD retrieval algorithm and Ahmad et al. (2010) improved the aerosol model. Sayer et al. (2012b) compared SeaWiFS data with AERONET data over water. Further information about the dataset can be downloaded from http://disc.gsfc.nasa.gov/dust/. Hsu et al. (2012) evaluated the AOD dataset over land from 1997 to 2010 and found that the US and Europe have a decreasing trend while China and India are experiencing an increasing trend. Compared with AERONET data, the correlation coefficient is 0.86 and 72% of data fall in the EE of 0.05 + 0.20τ over ocean (Sayer et al., 2012).
Monitoring water color anomaly of lakes based on an integrated method using Landsat-8 OLI images
Published in International Journal of Digital Earth, 2022
Xiaoqin Yang, Ruqing Tong, Li Ma, Jian Li, Siqi Wang, Liqiao Tian
Remote sensing is an effective means of environmental monitoring over larger areas with more continuous spatial and more frequent temporal observations than other methods (Chen and Feng 2018). At present, many satellite sensors, such as the Medium Resolution Imaging Spectrometer (MERIS) (Woerd and Wernand 2015), the Moderate Resolution Imaging Spectroradiometer (MODIS) (Wang et al. 2018), the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (Guan et al. 2020), and Landsat-8 (Urbanski et al. 2016), have been used in monitoring the marine and coastal water environment. These spectrometers are also designed specifically to detect the physical and chemical composition of water, such as the turbidity (Zhou et al. 2021; Sun et al. 2021), the Secchi disk depth (SDD) (Wang et al. 2020; Yin et al. 2021), the chlorophyll-a (Cao et al. 2020, 2022), and other colored components. Researchers have recorded the water color from satellite images and used this to develop the water color grade Forel-Ule Index (FUI) through water-leaving reflectance based on the Commission on Illumination (CIE) colorimetry system that is not sensitive to atmospheric correction accuracy (Chen, Huang, and Tang 2020; Van der Woerd and Robert Wernand 2018; Wang et al. 2019; Wernand, van der Woerd, and Gieskes 2013). Studies have illustrated that the FUI or hue angle can be calculated from satellite reflectance according to the band settings of different spectrometers, including MODIS, MERIS, Sentinel-3, and Landsat-8, for its strong stability and the ability to transform between different sensors (Lehmann et al. 2018; Van der Woerd and Robert Wernand 2018). These early studies indicate that the remotely sensed water color parameters have great potential to monitor the water environment changes and characterize water quality over a long-time scale.