Full-coverage daily 1-km MAIAC Aerosol Optical Depth (AOD) data in China, 2000-2020
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Aerosols significantly affect climate change, and ambient aerosols are related to various adverse health outcomes. Satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent worldwide local-scale gradients of aerosol loading. Although the MAIAC AOD product has assisted in examining the spatiotemporal pattern of atmospheric aerosols in China, accurate assessment of long-term aerosol loading countrywide at high spatiotemporal resolution is still challenging due to its non-random missingness. We aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverag' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["AOD","MAIAC","Full coverage","China"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Aerosols significantly affect climate change, and ambient aerosols are related to various adverse health outcomes. Satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent worldwide local-scale gradients of aerosol loading. Although the MAIAC AOD product has assisted in examining the spatiotemporal pattern of atmospheric aerosols in China, accurate assessment of long-term aerosol loading countrywide at high spatiotemporal resolution is still challenging due to its non-random missingness. We aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverag' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Aerosols significantly affect climate change, and ambient aerosols are related to various adverse health outcomes. Satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent worldwide local-scale gradients of aerosol loading. Although the MAIAC AOD product has assisted in examining the spatiotemporal pattern of atmospheric aerosols in China, accurate assessment of long-term aerosol loading countrywide at high spatiotemporal resolution is still challenging due to its non-random missingness. We aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverag
AOD; MAIAC; Full coverage; China