Modeling of the atmospheric effects and its application to the remote sensing of ocean color

1983 ◽  
Vol 22 (23) ◽  
pp. 3751 ◽  
Author(s):  
P. Y. Deschamps ◽  
M. Herman ◽  
D. Tanre
2018 ◽  
Vol 8 (12) ◽  
pp. 2684 ◽  
Author(s):  
Michael Twardowski ◽  
Alberto Tonizzo

An analytical radiative transfer (RT) model for remote sensing reflectance that includes the bidirectional reflectance distribution function (BRDF) is described. The model, called ZTT (Zaneveld-Twardowski-Tonizzo), is based on the restatement of the RT equation by Zaneveld (1995) in terms of light field shape factors. Besides remote sensing geometry considerations (solar zenith angle, viewing angle, and relative azimuth), the inputs are Inherent Optical Properties (IOPs) absorption a and backscattering bb coefficients, the shape of the particulate volume scattering function (VSF) in the backward direction, and the particulate backscattering ratio. Model performance (absolute error) is equivalent to full RT simulations for available high quality validation data sets, indicating almost all residual errors are inherent to the data sets themselves, i.e., from the measurements of IOPs and radiometry used as model input and in match up assessments, respectively. Best performance was observed when a constant backward phase function shape based on the findings of Sullivan and Twardowski (2009) was assumed in the model. Critically, using a constant phase function in the backward direction eliminates a key unknown, providing a path toward inversion to solve for a and bb. Performance degraded when using other phase function shapes. With available data sets, the model shows stronger performance than current state-of-the-art look-up table (LUT) based BRDF models used to normalize reflectance data, formulated on simpler first order RT approximations between rrs and bb/a or bb/(a + bb) (Morel et al., 2002; Lee et al., 2011). Stronger performance of ZTT relative to LUT-based models is attributed to using a more representative phase function shape, as well as the additional degrees of freedom achieved with several physically meaningful terms in the model. Since the model is fully described with analytical expressions, errors for terms can be individually assessed, and refinements can be readily made without carrying out the gamut of full RT computations required for LUT-based models. The ZTT model is invertible to solve for a and bb from remote sensing reflectance, and inversion approaches are being pursued in ongoing work. The focus here is with development and testing of the in-water forward model, but current ocean color remote sensing approaches to cope with an air-sea interface and atmospheric effects would appear to be transferable. In summary, this new analytical model shows good potential for future ocean color inversion with low bias, well-constrained uncertainties (including the VSF), and explicit terms that can be readily tuned. Emphasis is put on application to the future NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission.


Author(s):  
Mariusz E. Grotte ◽  
Roger Birkeland ◽  
Evelyn Honore-Livermore ◽  
Sivert Bakken ◽  
Joseph L. Garrett ◽  
...  

2021 ◽  
Vol 13 (15) ◽  
pp. 3000
Author(s):  
Georg Zitzlsberger ◽  
Michal Podhorányi ◽  
Václav Svatoň ◽  
Milan Lazecký ◽  
Jan Martinovič

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.


2021 ◽  
Vol 257 ◽  
pp. 112356
Author(s):  
Karlis Mikelsons ◽  
Menghua Wang ◽  
Xiao-Long Wang ◽  
Lide Jiang

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.


2014 ◽  
Vol 53 (15) ◽  
pp. 3301 ◽  
Author(s):  
Zhongping Lee ◽  
Shaoling Shang ◽  
Chuanmin Hu ◽  
Giuseppe Zibordi

2021 ◽  
Author(s):  
Musab Mbideen ◽  
Balázs Székely

<p>Remote Sensing (RS) and Geographic Information System (GIS) instruments have spread rapidly in recent years to manage natural resources and monitor environmental changes. Remote sensing has a vast range of applications; one of them is lakes monitoring. The Dead Sea (DS) is subjected to very strong evaporation processes, leading to a remarkable shrinkage of its water level. The DS is being dried out due to a negative balance in its hydrological cycle during the last five decades. This research aims to study the spatial changes in the DS throughout the previous 48 years. Change detection technique has been performed to detect this change over the research period (1972-2020). 73 Landsat imageries have been used from four digital sensors; Landsat 1-5 MSS C1 Level-1, Landsat 4-5 TM C1 Level-1, Land sat 7 ETM+ C1  Level-1, and Landsat 8 OLI-TIRS C1 Level. After following certain selection criteria , the number of studied images decreased. Furthermore, the Digital Surface Model of the Space Shuttle Radar Topography Mission and a bathymetric map of the Dead Sea were used. The collected satellite imageries were pre-processed and normalized using ENVI 5.3 software by converting the Digital Number (DN) to spectral radiance, the spectral radiance was converted to apparent reflectance, atmospheric effects were removed, and finally, the black gaps were removed. It was important to distinguish between the DS lake and the surrounding area in order to have accurate results, this was done by performing classification techniques. The digital terrain model of the DS was used in ArcGIS (3D) to reconstruct the elevation of the shore lines. This model generated equations to detect the water level, surface area, and water volume of the DS. The results were compared to the bathymetric data as well. The research shows that the DS water level declined 65 m (1.35 m/a) in the studied period. The surface area and the water volume declined by 363.56 km<sup>2 </sup>(7.57 km<sup>2</sup>/a) and 53.56 km<sup>3</sup> (1.11 km<sup>3</sup>/a), respectively. The research also concluded that due to the bathymetry of the DS, the direction of this shrinkage is from the south to the north. We hypothesize that anthropogenic effects have contributed in the shrinkage of the DS more than the climate. The use of the DS water by both Israel and Jordan for industrial purposes is the main factor impacting the DS, another factor is the diversion of the Jordan and Yarmouk rivers. Our results also allow to give a prediction for the near future of the DS: the water level is expected to reach –445 m in 2050, while the surface area and the water volume is expected to be 455 km<sup>2</sup> and 142 km<sup>3</sup>, respectively. </p>


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