An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia
نویسندگان
چکیده
We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering period 2001–2020. focus on Deep Blue (DB) and Multi-Angle Implementation Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time space. further employ speciated AOD estimates Copernicus Monitoring Service (CAMS) reanalyses help diagnose aerosol types hence sources. Considering as a whole, monthly mean AODs show similar temporal behaviour, with well-defined seasonal peak Austral summer. However, excepting periods intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, average, 50%. Decomposing into maps, patterns behaviour distinct differences, larger dynamic range AOD, markedly (?AOD?0.1) northern southeastern regions during winter This is counter-balanced by typically smaller across Australian interior. Site level comparisons all available 2 data Robotic Network (AERONET) sites operational study that tends marginally outperform terms correlation (RMAIAC = 0.71, RDB 0.65) root-mean-square error (RMSEMAIAC 0.065, RMSEDB 0.072). To probe this further, we classify according predominant surface type within 25 km radius. analysis shows MAIAC’s advantage retained for R but RMSE. For (Bare, comprising just 1.2% Australia) performance both algorithms relatively poor, 0.403, 0.332).
منابع مشابه
Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia
This study evaluates the new Aqua MODIS Dark Target (DT) Collection 6 (C6) Aerosol Optical Depth (AOD) (MYD04_3K) retrieval algorithm at 3 km resolution over Asian countries that have recently experienced severe and increasing air pollution. Retrievals showed generally low accuracy compared with the AErosol RObotic NETwork (AERONET), with only 55% of retrievals within the expected error (EE). T...
متن کاملMODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water
We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coas...
متن کاملValidation of MODIS aerosol optical depth retrieval over land
[1] Aerosol optical depths (ta) are derived operationally for the first time from the MODIS (Moderate Resolution Imaging Spectroradiometer) measurements over vegetated and partially vegetated land at 0.47 and 0.66 mm wavelengths. The extensive validation made during July – September 2000 encompasses 315 co-located ta in space and time derived by MODIS and AERONET (Aerosol Robotic Network) from ...
متن کاملAn over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals
MODIS Collection 5 retrieved aerosol optical depth (AOD) over land (MOD04/MYD04) was evaluated using 4 years of matching AERONET observations, to assess its suitability for aerosol data assimilation in numerical weather prediction models. Examination of errors revealed important sources of variation in random errors (e.g., atmospheric path length, scattering angle “hot spot”), and systematic bi...
متن کاملEvaluation of the MODIS aerosol optical depth retrieval over different ecosystems in China during EAST-AIRE
The accuracy of the Moderate Resolution Imaging Spectroradiometer’s (MODIS) aerosol products is still uncertain in China, due to a lack of validation by long-term and large-scale ground-based observations. In this paper, the MODIS aerosol optical depth (AOD) product is evaluated using Chinese Sun Hazemeter Network (CSHNET) data as ground truths over different ecological regions in China during ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14112664