Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean DOI
Pei Xin, Leiku Yang,

Weiqian Ji

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: unknown, P. 120949 - 120949

Published: Nov. 1, 2024

Language: Английский

Continuity between NASA MODIS Collection 6.1 and VIIRS Collection 2 land products DOI Creative Commons
Miguel O. Román,

Chris Justice,

Ian Paynter

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 302, P. 113963 - 113963

Published: Jan. 3, 2024

This paper provides a review and summary status of the research underway by NASA Terra Aqua Suomi-NPP Land Discipline Team to provide continuity global land data products from Moderate resolution Imaging Spectroradiometer (MODIS) Visible Infrared Radiometer Suite (VIIRS). The two MODIS instruments on Earth Observing System (morning overpass) (afternoon platforms have provided more than twenty years data. peer-reviewed generated are now being transitioned production using VIIRS inputs, with intention providing dynamic for observations. As part that process, undergoing intercomparison evaluation. These results where available show promising levels agreement accuracy in all cases. also offers options establishing products.

Language: Английский

Citations

42

Validation and diurnal variation evaluation of MERRA-2 multiple aerosol properties on a global scale DOI
Xin Su, Yuhang Huang, Lunche Wang

et al.

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 311, P. 120019 - 120019

Published: Aug. 10, 2023

Language: Английский

Citations

18

Global evaluation of Fengyun-3 MERSI dark target aerosol retrievals over land DOI Creative Commons
Leiku Yang,

Weiqian Ji,

Pei Xin

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1), P. 1 - 24

Published: April 25, 2024

The Medium Resolution Spectral Image (MERSI) is a MODIS-like sensor aboard Fengyun-3 satellite. first version of MERSI aerosol algorithm has been developed based on MODIS dark target (DT) algorithm, with modified models for estimating surface reflectance and an adjusted inland water masking method to release haze aerosols. This study applies DT the global observations from upgraded (MERSI-II) Fengyun-3D. And then, Aerosol Optical Depth (AOD) results year 2019–2020 are validated against Robotic Network (AERONET) data. In addition, analyses spatial distribution error characteristics MERSI-II retrievals presented. overall validation demonstrates that perform well globally, correlation coefficient 0.877 67.1% matchups within Expected Error envelope ± (0.05 + 0.2τ), which close statistic metrics products. AODs exhibit similar trends dependence. Moreover, two revealed in retrieval performance at site regional scales, as analysis monthly averages. These findings indicate success ported algorithm.

Language: Английский

Citations

5

Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS DOI
Lunche Wang, Xin Su, Yi Wang

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 214, P. 261 - 281

Published: July 1, 2024

Language: Английский

Citations

5

Research on the distribution and influencing factors of fine mode aerosol optical depth (AODf) in China DOI
Haifeng Xu, Jinji Ma,

Wenhui Luo

et al.

Atmospheric Environment, Journal Year: 2024, Volume and Issue: 334, P. 120721 - 120721

Published: Oct. 1, 2024

Language: Английский

Citations

4

Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments DOI Creative Commons
Ludovico Di Antonio, Claudia Di Biagio, Gilles Forêt

et al.

Atmospheric chemistry and physics, Journal Year: 2023, Volume and Issue: 23(19), P. 12455 - 12475

Published: Oct. 6, 2023

Abstract. The aerosol optical depth (AOD) is a derived measurement useful to investigate the load and its distribution at different spatio-temporal scales. In this work we use long-term (2000–2021) MAIAC (Multi-Angle Implementation of Atmospheric Correction) retrievals with 1 km resolution climatological AOD variability trends scales in Europe: continental (30–60∘ N, 20∘ W–40∘ E), regional (100 × 100 km2) an urban–local scale (3 3 km2). climatology shows highest values during summer (JJA) lowest winter (DJF) seasons. Regional are investigated for 21 cities Europe, including capitals large urban agglomerations. Analyses show average (550 nm) between 0.06 0.16 while also displaying strong north–south gradient. This gradient corresponds similar one European background, higher being located over Po Valley, Mediterranean Basin eastern Europe. Average enhancements local respect 57 %, 55 39 % 32 found metropolitan centers such as Barcelona, Lisbon, Paris Athens, respectively, suggesting non-negligible enhancement burden through emissions. Negative deviations observed other cities, Amsterdam (−17 %) Brussels (−6 %), indicating background signal heterogeneous spatial that conceals signal. Finally, negative statistically significant entire continent observed. A stronger decrease rate occurs most under investigation.

Language: Английский

Citations

10

A High-Resolution Aerosol Retrieval Algorithm Via Deep Learning DOI
Bing Tu, Chengxin Hu, Bo Liu

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0

Enhancing global aerosol retrieval from satellite data via deep learning with mutual information estimation DOI
Xiaohu Sun, Yong Xue, Lin Sun

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104534 - 104534

Published: April 14, 2025

Language: Английский

Citations

0

Aerosol induced changes in solar radiation spectrum and PV power production DOI
Jesús Polo

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 415 - 434

Published: Jan. 1, 2025

Language: Английский

Citations

0

Evaluation and analysis of long-term MODIS MAIAC aerosol products in China DOI

Huang Ge,

Xin Su, Lunche Wang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174983 - 174983

Published: July 22, 2024

Language: Английский

Citations

3