FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313530106> ?p ?o ?g. }

• W4313530106 endingPage "113426" @default.
• W4313530106 startingPage "113426" @default.
• W4313530106 abstract "Aerosol optical properties are among the most fundamental parameters in atmospheric environmental studies. Satellite aerosols retrievals that are based on deep learning or machine learning approach have been widely discussed in remote sensing studies, but the flexible random forest (RF) model has not received much attention in the retrieval of geostationary satellite, like Himawari-8. Thus, the Himawari-8 aerosol retrieval achieved by RF model requires further investigation and optimization. Based on the radiative transfer equation, this study proposed a RF model driven by a differential operator, which quantifies a simple linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. The spectral information of aerosols is achieved by independent TOA reflectance comparison between images rather than one result from multiple band synthesis. The method allows simple feature inputs and shows weak dependence on auxiliary data. It also achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The model performance was evaluated using a series of comprehensive temporal and spatial validation analyses. A sample-based tenfold cross-validation (10-CV) shows that the new method can simultaneously improve the estimation of aerosol properties, with considerably high correlation coefficients (R2) of 0.85 for AODs at the 0.50 μm wavelengths, a mean absolute error (MAE) of 0.08, a root mean square error (RMSE) of 0.13 and >70% of the samples fell within the AOD expected error (EE). The high accuracy of the spectral AOD retrievals also exhibits good performance on AE calculations, with at least 2/3 of the samples falling within the EE. The site based 10-CV also evaluates the spatial predictions on AODs at the 0.50 μm wavelength, with R2 of 0.67, MAE of 0.12 and RMSE of 0.18. It also has outperformed the Himawari operational aerosol products and appeared to be comparable to other popular machine learning models with better AE retrievals in some typical regions. Two typical regional pollution cases also highlight the advantages of the new aerosol monitoring approach. The 5 km resolution aerosol retrievals exhibit good spatial coverage and performance when describing the regional pollution levels and types. The proposed method improves the performance of RF in retrieving aerosol properties from geostationary satellites and also offers a new prospective for aerosol remote sensing using machine learning approaches." @default.
• W4313530106 created "2023-01-06" @default.
• W4313530106 creator A5031584858 @default.
• W4313530106 creator A5061624232 @default.
• W4313530106 creator A5069656143 @default.
• W4313530106 date "2023-03-01" @default.
• W4313530106 modified "2023-10-09" @default.
• W4313530106 title "The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images" @default.
• W4313530106 cites W1533261678 @default.
• W4313530106 cites W1968340782 @default.
• W4313530106 cites W1977518971 @default.
• W4313530106 cites W1977959274 @default.
• W4313530106 cites W1979660112 @default.
• W4313530106 cites W1981719480 @default.
• W4313530106 cites W1987337512 @default.
• W4313530106 cites W1989944407 @default.
• W4313530106 cites W1991146308 @default.
• W4313530106 cites W1993065427 @default.
• W4313530106 cites W1995123924 @default.
• W4313530106 cites W2007791627 @default.
• W4313530106 cites W2020994040 @default.
• W4313530106 cites W2030221894 @default.
• W4313530106 cites W2069977802 @default.
• W4313530106 cites W2083944525 @default.
• W4313530106 cites W2089433206 @default.
• W4313530106 cites W2095598361 @default.
• W4313530106 cites W2102553184 @default.
• W4313530106 cites W2103977502 @default.
• W4313530106 cites W2117131127 @default.
• W4313530106 cites W2119744638 @default.
• W4313530106 cites W2125763679 @default.
• W4313530106 cites W2144801285 @default.
• W4313530106 cites W2147867287 @default.
• W4313530106 cites W2165405661 @default.
• W4313530106 cites W2167604263 @default.
• W4313530106 cites W2169258810 @default.
• W4313530106 cites W2169501951 @default.
• W4313530106 cites W2297827415 @default.
• W4313530106 cites W2399455941 @default.
• W4313530106 cites W2417623769 @default.
• W4313530106 cites W2620207452 @default.
• W4313530106 cites W2620300958 @default.
• W4313530106 cites W2704099376 @default.
• W4313530106 cites W2803058927 @default.
• W4313530106 cites W2804076223 @default.
• W4313530106 cites W2865430977 @default.
• W4313530106 cites W2890236410 @default.
• W4313530106 cites W2900901958 @default.
• W4313530106 cites W2909938372 @default.
• W4313530106 cites W2911964244 @default.
• W4313530106 cites W2943638903 @default.
• W4313530106 cites W2953978338 @default.
• W4313530106 cites W2994306432 @default.
• W4313530106 cites W2996334933 @default.
• W4313530106 cites W2998121241 @default.
• W4313530106 cites W3015616758 @default.
• W4313530106 cites W3046736293 @default.
• W4313530106 cites W3089090706 @default.
• W4313530106 cites W3111182779 @default.
• W4313530106 cites W3195871490 @default.
• W4313530106 cites W3215049759 @default.
• W4313530106 cites W4205154535 @default.
• W4313530106 cites W4212917383 @default.
• W4313530106 cites W4283264239 @default.
• W4313530106 doi "https://doi.org/10.1016/j.rse.2022.113426" @default.
• W4313530106 hasPublicationYear "2023" @default.
• W4313530106 type Work @default.
• W4313530106 citedByCount "4" @default.
• W4313530106 countsByYear W43135301062023 @default.
• W4313530106 crossrefType "journal-article" @default.
• W4313530106 hasAuthorship W4313530106A5031584858 @default.
• W4313530106 hasAuthorship W4313530106A5061624232 @default.
• W4313530106 hasAuthorship W4313530106A5069656143 @default.
• W4313530106 hasConcept C105795698 @default.
• W4313530106 hasConcept C120665830 @default.
• W4313530106 hasConcept C121332964 @default.
• W4313530106 hasConcept C127313418 @default.
• W4313530106 hasConcept C1276947 @default.
• W4313530106 hasConcept C139945424 @default.
• W4313530106 hasConcept C153294291 @default.
• W4313530106 hasConcept C154945302 @default.
• W4313530106 hasConcept C16405173 @default.
• W4313530106 hasConcept C169258074 @default.
• W4313530106 hasConcept C19269812 @default.
• W4313530106 hasConcept C199390426 @default.
• W4313530106 hasConcept C2778329001 @default.
• W4313530106 hasConcept C2779345167 @default.
• W4313530106 hasConcept C33923547 @default.
• W4313530106 hasConcept C39432304 @default.
• W4313530106 hasConcept C41008148 @default.
• W4313530106 hasConcept C62649853 @default.
• W4313530106 hasConcept C74902906 @default.
• W4313530106 hasConceptScore W4313530106C105795698 @default.
• W4313530106 hasConceptScore W4313530106C120665830 @default.
• W4313530106 hasConceptScore W4313530106C121332964 @default.
• W4313530106 hasConceptScore W4313530106C127313418 @default.
• W4313530106 hasConceptScore W4313530106C1276947 @default.
• W4313530106 hasConceptScore W4313530106C139945424 @default.

• next