scholarly journals Introducing a nested multimedia fate and transport model for organic contaminants (NEM)

2021 ◽  
Author(s):  
Knut Breivik ◽  
Sabine Eckhardt ◽  
Michael S. McLachlan ◽  
Frank Wania
Keyword(s):  
Spatial Resolution ◽  
Computational Efficiency ◽  
Organic Contaminants ◽  
Transport Model ◽  
Fate And Transport ◽  
Fine Spatial Resolution ◽  
Global Modelling

Nesting allows a new global modelling tool to combine computational efficiency with the fine spatial resolution required for many applications.

scholarly journals Simulation of Pesticide Behavior in a Paddy Block by a Pesticide Fate and Transport Model

10.5109/16138 ◽  
2009 ◽  
Vol 54 (2) ◽  
pp. 505-512
Author(s):  
Thai Khanh Phong ◽  
Kazuaki Hiramatsu ◽  
Son Hong Vu ◽  
Satoru Ishihara ◽  
Hirozumi Watanabe
Keyword(s):  
Transport Model ◽  
Fate And Transport

The NASA-TROPOMI Aerosol Algorithm: Evaluation of first results

2021 ◽  
Author(s):  
Omar Torres ◽  
Hiren Jethva ◽  
Changwoo Ahn ◽  
Glen Jaross ◽  
Diego Loyola
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Spatial Resolution ◽  
Single Scattering ◽  
Initial Evaluation ◽  
Continental Scale ◽  
Related Information ◽  
Fine Spatial Resolution ◽  
Direct Measurements ◽  
First Results

<p>The NASA-TROPOMI aerosol algorithm (TropOMAER), is an adaptation of the currently operational OMI near-UV (OMAERUV & OMACA) inversion schemes, that take advantage of TROPOMI’s unprecedented fine spatial resolution at UV wavelengths, and the availability of ancillary aerosol-related information to derive aerosol loading in cloud-free and above-cloud aerosols scenes. In this presentation we will introduce the NASA TROPOMI aerosol algorithm and discuss initial evaluation results of retrieved aerosol optical depth (AOD) and single scattering albedo (SSA) by direct comparison to AERONET AOD direct measurements and SSA inversions. We will also demonstrate TropOMAER retrieval capabilities in the context of recent continental scale aerosol events.</p>

scholarly journals A Simple Spatio–Temporal Data Fusion Method Based on Linear Regression Coefficient Compensation

2020 ◽  
Vol 12 (23) ◽  
pp. 3900
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Gennadii Donchyts ◽  
Arjen Haag ◽  
Albrecht Weerts
Keyword(s):  
Linear Regression ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Fusion Method ◽  
Temporal Data ◽  
Resolution Image ◽  
Coarse Resolution ◽  
Fine Spatial Resolution ◽  
Spatio Temporal

High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is difficult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and efficient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in different areas and using different sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.

Road map extraction by multiple detectors in fine spatial resolution SAR data1

2003 ◽  
Vol 29 (4) ◽  
pp. 481-490 ◽  
Author(s):  
Fabio Dell'Acqua ◽  
Paolo Gamba ◽  
Gianni Lisini
Keyword(s):  
Spatial Resolution ◽  
Road Map ◽  
Fine Spatial Resolution ◽  
Multiple Detectors

scholarly journals Generalizing Source Geometry of Site Contamination by Simulating and Analyzing Analytical Solution of Three-Dimensional Solute Transport Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Xingwei Wang ◽  
Jiajun Chen ◽  
Hao Wang ◽  
Jianfei Liu
Keyword(s):  
Solute Transport ◽  
Contaminant Transport ◽  
Transport Model ◽  
Three Dimensional ◽  
Fate And Transport ◽  
Source Area ◽  
Equation Model ◽  
Plume Migration ◽  
Advective Diffusion ◽  
Source Geometry

Due to the uneven distribution of pollutions and blur edge of pollutant area, there will exist uncertainty of source term shape in advective-diffusion equation model of contaminant transport. How to generalize those irregular source terms and deal with those uncertainties is very critical but rarely studied in previous research. In this study, the fate and transport of contaminant from rectangular and elliptic source geometry were simulated based on a three-dimensional analytical solute transport model, and the source geometry generalization guideline was developed by comparing the migration of contaminant. The result indicated that the variation of source area size had no effect on pollution plume migration when the plume migrated as far as five times of source side length. The migration of pollution plume became slower with the increase of aquifer thickness. The contaminant concentration was decreasing with scale factor rising, and the differences among various scale factors became smaller with the distance to field increasing.

Distributed water erosion modelling at fine spatial resolution across Denmark

Geomorphology ◽  
2019 ◽  
Vol 342 ◽  
pp. 150-162 ◽  
Author(s):  
Nils Onnen ◽  
Goswin Heckrath ◽  
Antoine Stevens ◽  
Preben Olsen ◽  
Mette B. Greve ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Water Erosion ◽  
Fine Spatial Resolution ◽  
Erosion Modelling

scholarly journals Calibrating R-LINE model results with observational data to develop annual mobile source air pollutant fields at fine spatial resolution: Application in Atlanta

2016 ◽  
Vol 147 ◽  
pp. 446-457 ◽  
Author(s):  
Xinxin Zhai ◽  
Armistead G. Russell ◽  
Poornima Sampath ◽  
James A. Mulholland ◽  
Byeong-Uk Kim ◽  
...  
Keyword(s):  
Observational Data ◽  
Spatial Resolution ◽  
Air Pollutant ◽  
Mobile Source ◽  
Line Model ◽  
Fine Spatial Resolution
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