scholarly journals Showcasing the potential of wastewater-based epidemiology to track pharmaceuticals consumption in cities: Comparison against prescription data collected at fine spatial resolution

2021 ◽  
Vol 150 ◽  
pp. 106404
Author(s):  
M. Escolà Casas ◽  
N.S. Schröter ◽  
I. Zammit ◽  
M. Castaño-Trias ◽  
S. Rodriguez-Mozaz ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Prescription Data ◽  
Fine Spatial Resolution

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

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

scholarly journals Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Victor A. Alegana ◽  
Peter M. Atkinson ◽  
Christopher Lourenço ◽  
Nick W. Ruktanonchai ◽  
Claudio Bosco ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Population Distribution ◽  
Malaria Incidence ◽  
World Health Organisation ◽  
World Health ◽  
Fine Spatial Resolution ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
And Control

Abstract The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.

Downscaling sub-daily Land Surface Temperature time series for monitoring heat in urban environments

2020 ◽  
Author(s):  
Nikos Alexandris ◽  
Matteo Piccardo ◽  
Vasileios Syrris ◽  
Alessandro Cescatti ◽  
Gregory Duveiller
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Environments ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fine Spatial Resolution

<p>The frequency of extreme heat related events is rising. This places the ever growing number of urban dwellers at higher risk. Quantifying these phenomena is important for the development and monitoring of climate change adaptation and mitigation policies. In this context, earth observations offer increasing opportunities to assess these phenomena with an unprecedented level of accuracy and spatial reach. Satellite thermal imaging systems acquire Land Surface Temperature (LST) which is fundamental to run models that study for example hotspots and heatwaves in urban environments.</p><p>Current instruments include TIRS on board Landsat 8 and MODIS on board of Terra satellites. These provide LST products on a monthly basis at 100m and twice per day at 1km respectively. Other sensors on board geostationary satellites, such as MSG and GOES-R, produce sub-hourly thermal images. For example the SEVIRI instrument onboard MSG, captures images every 15 minutes. However, this is done at an even coarser spatial resolution, which is 3 to 5 km in the case of SEVIRI. Nevertheless, none of the existing systems can capture LST synchronously with fine spatial resolution at a high temporal frequency, which is a prerequisite for monitoring heat stress in urban environments.</p><p>Combining LST time series of high temporal resolution (i.e. sub-daily MODIS- or SEVIRI-derived data) with products of fine spatial resolution (i.e. Landsat 8 products), and potentially other related variables (i.e. reflectance, spectral indices, land cover information, terrain parameters and local climatic variables), facilitates the downscaling of LST estimations. Nonetheless, considering the complexity of how distinct surfaces within a city heat-up differently during the course of a day, such a downscaling is meaningful for practically synchronous observations (e.g. Landsat-8 and MODIS Terra’s morning observations).</p><p>The recently launched ECOSTRESS mission provides multiple times in a day high spatial resolution thermal imagery at 70m. Albeit, recording the same locations on Earth every few days at varying times. We explore the associations between ECOSTRESS and Landsat-8 thermal data, based on the incoming radiation load and distinct surface properties characterised from other datasets. In our approach, first we upscale ECOSTRESS data to simulate Landsat-8 images at moments that coincide the acquisition times of other sensors products. In a second step, using the simulated Landsat-8 images, we downscale LST products acquired at later times, such as MODIS Aqua (ca. 13:30) or even the hourly MSG data. This composite downscaling procedure enables an enhanced LST estimation that opens the way for better diagnostics of the heat stress in urban landscapes.</p><p>In this study we discuss in detail the concepts of our approach and present preliminary results produced with the JEODPP, JRC's high throughput computing platform.</p>

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