Long-term PM2.5 pollution over China: Identification of PM2.5 pollution hotspots and source contributions

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 893, P. 164871 - 164871

Published: June 16, 2023

Language: Английский

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Spatiotemporal changes in aerosols over Bangladesh using 18 years of MODIS and reanalysis data

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 315, P. 115097 - 115097

Published: April 30, 2022

Language: Английский

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Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA)

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2023, Volume and Issue: 16, P. 4947 - 4962

Published: Jan. 1, 2023

Aerosol satellite retrieval can provide detailed aerosol information on a large scale, which becomes one of the main ways global research. However, rapid and accrue by is challenging, typically requiring radiation transfer models (RTMs) surface reflectance (SR). An algorithm (SEMARA) combining simplified robust estimation obtain local high-precision optical depth (AOD) without RTMs SR datasets, while method cannot perform large-scale long-term retrieval. Hereby, machine learning (ML) based fully connected neural network (FCNN) SEMARA was proposed. The new optimizes traditional sample construction ML achieve at larger spatial temporal scale. Moderate resolution imaging spectroradiometer data were applied to AOD four typical regions globally. retrievals validated using robotic measurements in comparison MOD04_3K SEMARA. accuracy validation indicators method, root-mean-square error (RMSE) 0.109, mean absolute (MAE) 0.072, Pearson correlation coefficient (R) 0.8983, approximately 79.69% fell within expected (EE), performed better than (RMSE = 0.1972 MAE 0.1403, R 0.7692 Within EE 55.24%) 0.2465 0.1106, 0.0.5968 72.85%) all study regions, reflect variation with continuity coverage.

Language: Английский

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Investigating the applicability of a simple iterative approach for aerosol optical depth (AOD) retrieval over diverse land surface types from Landsat 8 and Sentinel 2 using visible and near-infrared (VNIR) spectral bands

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 314, P. 120082 - 120082

Published: Sept. 14, 2023

Language: Английский

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GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 310, P. 119965 - 119965

Published: July 19, 2023

The Geostationary Ocean Color Imager II (GOCI-II) deployed on board GEO-KOMPSAT 2B (GK2B) satellite can observe the earth with high frequency, which is of great significance to dynamic monitoring regional aerosol optical depth (AOD). However, lack short-wave infrared bands makes classical methods based radiative transfer analysis difficult achieve retrieval aerosols. data-driven learning from massive samples high-accuracy aerosols local, and global scale. required for method are obtain due short operation time GK2B insufficient ground-based measurements. Aiming solve such a problem, Simplified Robust Surface Reflectance Estimation Aerosol Retrieval Algorithm (SEMARA) were aggregated be applied in study. SEMARA using observation retrieve local AOD reliability accuracy, used construct high-quality high-quantity training higher efficiency compared traditional sample methods. Four methods: Fully Connected Neural Network (FCNN), Random Forest (RF), Support Vector Machine (SVM) eXtreme Gradient Boosting (XGBoost), chosen over South Korea 2021 2022 GOCI-II. results validated measurements Robotic (AENRONET) sites show that FCNN has accuracy stability, followed by, RF XGBoost methods, while SVM significantly overestimate spatial distribution AOD.

Language: Английский

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Evaluation of surface reflectance retrieval over diverse surface types using SREM algorithm in varied aerosol conditions for coarse to medium resolution data from multiple spaceborne sensors

International Journal of Remote Sensing, Journal Year: 2023, Volume and Issue: 44(11), P. 3358 - 3384

Published: June 3, 2023

ABSTRACTSurface reflectance (SR) is one of the most important components for deriving different biophysical parameters from remotely sensed data. For accurate retrieval SR, it essential to accurately estimate degree atmospheric alteration target signals reaching at-sensor. Simplified and robust SR estimation method (SREM) such approximate that could demonstrate top-of-atmosphere (ToA) signals, without using any precalculated LUTs or aerosol information. The present study attempts assess accuracy SREM algorithm over varied Indian land surfaces data Landsat 8, Sentinel 2, AWiFS LISS III. results indicate while performs quite well 2 surface types, performance good 8 as except water in coastal band. retrievals also show high correlation III but estimated values are much lower than level due smaller ToA values. Furthermore, load does impact adversely, least impactful datasets particularly water. Additionally, underestimated at all spectral bands, slightly green SWIR band case 8. Overall, findings will need be modified adapt retrievals, can, nevertheless, readily used a simplified quick region 2.KEYWORDS: SREMsurface estimationLandsat 8Sentinel 2LISS IIIAWiFS AcknowledgementsThe authors thankful NRSC IIRS providing access datasets. We USGS, ESA Copernicus, MODIS teams study.Disclosure statementNo potential conflict interest was reported by author(s).Data Availability statementLISS can ordered National Remote Sensing Centre (NRSC), ISRO's Bhoonidhi portal (https://bhoonidhi.nrsc.gov.in/bhoonidhi/index.html). freely downloaded USGS Earthexplorer (https://earthexplorer.usgs.gov/). Copernicus Open Access Hub (https://scihub.copernicus.eu/dhus/#/home). available https://ladsweb.modaps.eosdis.nasa.gov/).

Language: Английский

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A retrieval method for land surface temperatures based on UAV broadband thermal infrared images via the three-dimensional look-up table

Building and Environment, Journal Year: 2022, Volume and Issue: 226, P. 109793 - 109793

Published: Nov. 10, 2022

Language: Английский

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Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(5)

Published: April 25, 2024

Language: Английский

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Performance of MODIS Deep Blue Collection 6.1 Aerosol Optical Depth Products Over Indonesia: Spatiotemporal Variations and Aerosol Types

Advances in Meteorology, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 12

Published: June 28, 2022

This study aims to evaluate the performance of long-term Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection 6.1 (C6.1) in determining spatiotemporal variation aerosol optical depth (AOD) and types over Indonesia. For this purpose, monthly MODIS DB AOD datasets are directly compared with Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud-screened quality-assured) measurements at 8 sites throughout The results indicate that retrievals AERONET have a high correlation Sumatra Island (i.e., Kototabang (r = 0.88) Jambi 0.9)) Kalimantan Palangkaraya 0.89) Pontianak 0.92)). However, correlations low Bandung, Palu, Sorong. In general, tends overestimate all by 16 61% can detect extreme fire events Islands quite well. Indonesia mostly consist clean continental, followed biomass burning/urban industrial mixed aerosols. Palu Sorong had highest continental contribution (90%), while Bandung burning/urban-industrial atmospheric composition (93.7%). aerosols, was found Pontianak, proportion 48.4%. Spatially, annual mean western part is higher than eastern part. Seasonally, observed during period September–November, which associated emergence events.

Language: Английский

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Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

Atmospheric Research, Journal Year: 2023, Volume and Issue: 298, P. 107141 - 107141

Published: Dec. 1, 2023

Language: Английский

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