FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4323644291> ?p ?o ?g. }

• W4323644291 endingPage "16" @default.
• W4323644291 startingPage "1" @default.
• W4323644291 abstract "Mainstream satellite cloud masking algorithms are prone to mis-masking in haze-polluted areas, which may cause errors in aerosol radiative effect calculations and attribution of surface solar radiance changes, thereby distinguishing between clouds and haze is critical to obtaining accurate land and atmospheric data products. Existing cloud and haze mask algorithms based on the threshold method may require us to spend a lot of manpower to perform multiple threshold tests, in addition the obtained thresholds are only applicable to particular sensors, which limits the generality of the threshold-based cloud and haze mask algorithms. In this study, a new cloud and haze mask algorithm based on a combination of radiative transfer simulations and machine learning (SCHM) is proposed and applied to MODIS images. When we simulated the apparent reflectance of the first seven visible and near-infrared channels of MODIS, the CALIOP and AERONET data verification results showed that the SCHM algorithm achieved 85.16% and 90.08% hit rates for cloud and haze recognition, respectively. When we added three thermal infrared channels (20, 31, and 35 bands) for simulation, the cloud and haze hit rates were improved to approximately 85.72% and 90.62%, respectively. This indicates that the SCHM algorithm can improve the accuracy of detection results by improving the radiative transfer simulation parameters. Compared with existing threshold-based methods, the SCHM algorithm has the advantages of simple logic, convenient modification, and flexible configuration." @default.
• W4323644291 created "2023-03-10" @default.
• W4323644291 creator A5035518479 @default.
• W4323644291 creator A5049894593 @default.
• W4323644291 creator A5063557429 @default.
• W4323644291 creator A5083980544 @default.
• W4323644291 date "2023-01-01" @default.
• W4323644291 modified "2023-10-16" @default.
• W4323644291 title "A New Cloud and Haze Mask Algorithm From Radiative Transfer Simulations Coupled With Machine Learning" @default.
• W4323644291 cites W1940238987 @default.
• W4323644291 cites W1941212858 @default.
• W4323644291 cites W1966068706 @default.
• W4323644291 cites W1969535121 @default.
• W4323644291 cites W1970283663 @default.
• W4323644291 cites W1986875129 @default.
• W4323644291 cites W2002553792 @default.
• W4323644291 cites W2003571388 @default.
• W4323644291 cites W2010638763 @default.
• W4323644291 cites W2013344607 @default.
• W4323644291 cites W2019811237 @default.
• W4323644291 cites W2022707507 @default.
• W4323644291 cites W2022979816 @default.
• W4323644291 cites W2023307338 @default.
• W4323644291 cites W2035570184 @default.
• W4323644291 cites W2040496435 @default.
• W4323644291 cites W2049637169 @default.
• W4323644291 cites W2053811834 @default.
• W4323644291 cites W2055138256 @default.
• W4323644291 cites W2055552276 @default.
• W4323644291 cites W2063800109 @default.
• W4323644291 cites W2066698326 @default.
• W4323644291 cites W2070259289 @default.
• W4323644291 cites W2083854930 @default.
• W4323644291 cites W2089642777 @default.
• W4323644291 cites W2097693355 @default.
• W4323644291 cites W2098138574 @default.
• W4323644291 cites W2102489438 @default.
• W4323644291 cites W2103775154 @default.
• W4323644291 cites W2103977502 @default.
• W4323644291 cites W2105058711 @default.
• W4323644291 cites W2106515163 @default.
• W4323644291 cites W2109927385 @default.
• W4323644291 cites W2109979560 @default.
• W4323644291 cites W2112229871 @default.
• W4323644291 cites W2114572425 @default.
• W4323644291 cites W2122111042 @default.
• W4323644291 cites W2142827986 @default.
• W4323644291 cites W2145265567 @default.
• W4323644291 cites W2150021548 @default.
• W4323644291 cites W2153872357 @default.
• W4323644291 cites W2154953100 @default.
• W4323644291 cites W2164882196 @default.
• W4323644291 cites W2166604768 @default.
• W4323644291 cites W2167929905 @default.
• W4323644291 cites W2169258810 @default.
• W4323644291 cites W2173209715 @default.
• W4323644291 cites W2180732040 @default.
• W4323644291 cites W2188098015 @default.
• W4323644291 cites W2511192020 @default.
• W4323644291 cites W2574388714 @default.
• W4323644291 cites W2579955828 @default.
• W4323644291 cites W2594035764 @default.
• W4323644291 cites W2739812755 @default.
• W4323644291 cites W2763686595 @default.
• W4323644291 cites W2771508394 @default.
• W4323644291 cites W2783756924 @default.
• W4323644291 cites W2789790541 @default.
• W4323644291 cites W2791126284 @default.
• W4323644291 cites W2801005346 @default.
• W4323644291 cites W2890236410 @default.
• W4323644291 cites W2899079540 @default.
• W4323644291 cites W2905112502 @default.
• W4323644291 cites W2921499885 @default.
• W4323644291 cites W2929660114 @default.
• W4323644291 cites W2991428754 @default.
• W4323644291 cites W2992697992 @default.
• W4323644291 cites W2993580201 @default.
• W4323644291 cites W2996137214 @default.
• W4323644291 cites W3107727261 @default.
• W4323644291 cites W3159141041 @default.
• W4323644291 doi "https://doi.org/10.1109/tgrs.2023.3252264" @default.
• W4323644291 hasPublicationYear "2023" @default.
• W4323644291 type Work @default.
• W4323644291 citedByCount "0" @default.
• W4323644291 crossrefType "journal-article" @default.
• W4323644291 hasAuthorship W4323644291A5035518479 @default.
• W4323644291 hasAuthorship W4323644291A5049894593 @default.
• W4323644291 hasAuthorship W4323644291A5063557429 @default.
• W4323644291 hasAuthorship W4323644291A5083980544 @default.
• W4323644291 hasConcept C111919701 @default.
• W4323644291 hasConcept C11413529 @default.
• W4323644291 hasConcept C120665830 @default.
• W4323644291 hasConcept C121332964 @default.
• W4323644291 hasConcept C127313418 @default.
• W4323644291 hasConcept C142362112 @default.
• W4323644291 hasConcept C153294291 @default.
• W4323644291 hasConcept C153349607 @default.
• W4323644291 hasConcept C199390426 @default.

• next