Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306390980> ?p ?o ?g. }
- W4306390980 endingPage "925" @default.
- W4306390980 startingPage "911" @default.
- W4306390980 abstract "Abstract Machine learning combined with multisource remote sensing data to assess soil moisture content (SMC) has attracted considerable attention in SMC studies, but the retrieval results still remain uncertain. The purpose of this study is to combine multiple single machine learning models with integrated learning algorithms and propose an SMC retrieval method based on multiple differentiated models under a stacking integrated learning architecture. First, 19 factors, including: radar backscattering coefficient, vegetation index, and drought index, that affect SMC were extracted from SENTINEL‐1, LANDSAT, and terrain factors. Those with the highest importance scores were selected as retrieval factors using the Boruta algorithm combined with four single machine learning methods—classified regression tree, random forest, gradient boosting decision tree (GBDT), and extreme random tree. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and the generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that radar backscattering coefficient, temperature, vegetation drought index, land surface temperature, enhanced vegetation index, and solar local incident angle were the most important environmental variables for soil moisture retrieval. A comparison of the four machine learning methods in April and August showed that the GBDT model revealed the highest SMC retrieval accuracy, with root mean square error values of 1.87% and 1.64%, respectively. The stacking models were more accurate than the optimal single machine learning model, especially when using GBM. The multifactor integrated model constructed using spectral indices, radar backscatter coefficients, and topographic data exhibited high accuracy in soil surface moisture retrieval in an arid zone, providing a reference for land desertification studies and ecological environment management in the study region." @default.
- W4306390980 created "2022-10-17" @default.
- W4306390980 creator A5039591696 @default.
- W4306390980 creator A5040702505 @default.
- W4306390980 creator A5078250469 @default.
- W4306390980 creator A5090049720 @default.
- W4306390980 date "2022-11-21" @default.
- W4306390980 modified "2023-10-17" @default.
- W4306390980 title "Remote sensing‐based retrieval of soil moisture content using stacking ensemble learning models" @default.
- W4306390980 cites W1605688901 @default.
- W4306390980 cites W1843760463 @default.
- W4306390980 cites W1881981391 @default.
- W4306390980 cites W1983436430 @default.
- W4306390980 cites W1994086902 @default.
- W4306390980 cites W2003862898 @default.
- W4306390980 cites W2025042814 @default.
- W4306390980 cites W2026604749 @default.
- W4306390980 cites W2028979033 @default.
- W4306390980 cites W2039037836 @default.
- W4306390980 cites W2058232479 @default.
- W4306390980 cites W2061499926 @default.
- W4306390980 cites W2068371905 @default.
- W4306390980 cites W2084117724 @default.
- W4306390980 cites W2093465768 @default.
- W4306390980 cites W2098813253 @default.
- W4306390980 cites W2139682450 @default.
- W4306390980 cites W2145697390 @default.
- W4306390980 cites W2156665896 @default.
- W4306390980 cites W2171758610 @default.
- W4306390980 cites W2548895919 @default.
- W4306390980 cites W2566428602 @default.
- W4306390980 cites W2588003345 @default.
- W4306390980 cites W2598382903 @default.
- W4306390980 cites W2674441608 @default.
- W4306390980 cites W2796367884 @default.
- W4306390980 cites W2802521597 @default.
- W4306390980 cites W2804415560 @default.
- W4306390980 cites W2806779590 @default.
- W4306390980 cites W2885214314 @default.
- W4306390980 cites W2887303328 @default.
- W4306390980 cites W2901156873 @default.
- W4306390980 cites W2902112358 @default.
- W4306390980 cites W2918022889 @default.
- W4306390980 cites W2979490443 @default.
- W4306390980 cites W3000134814 @default.
- W4306390980 cites W3005829171 @default.
- W4306390980 cites W3011712375 @default.
- W4306390980 cites W3015083507 @default.
- W4306390980 cites W3038890942 @default.
- W4306390980 cites W3040530861 @default.
- W4306390980 cites W3048731834 @default.
- W4306390980 cites W3091920975 @default.
- W4306390980 cites W3092177585 @default.
- W4306390980 cites W3094122424 @default.
- W4306390980 cites W3095821188 @default.
- W4306390980 cites W3096665883 @default.
- W4306390980 cites W3105603053 @default.
- W4306390980 cites W3126169176 @default.
- W4306390980 cites W3133490427 @default.
- W4306390980 cites W3146023712 @default.
- W4306390980 cites W3150653528 @default.
- W4306390980 cites W3153837545 @default.
- W4306390980 cites W3156417958 @default.
- W4306390980 cites W3164643466 @default.
- W4306390980 cites W3178563107 @default.
- W4306390980 cites W3178764567 @default.
- W4306390980 cites W3192264912 @default.
- W4306390980 cites W3193904221 @default.
- W4306390980 cites W3196990047 @default.
- W4306390980 cites W3206527118 @default.
- W4306390980 cites W3217670614 @default.
- W4306390980 cites W4200100974 @default.
- W4306390980 cites W4206548648 @default.
- W4306390980 cites W4221099937 @default.
- W4306390980 cites W4224045009 @default.
- W4306390980 cites W4224228993 @default.
- W4306390980 cites W4228996734 @default.
- W4306390980 cites W4245245551 @default.
- W4306390980 cites W4284991999 @default.
- W4306390980 doi "https://doi.org/10.1002/ldr.4505" @default.
- W4306390980 hasPublicationYear "2022" @default.
- W4306390980 type Work @default.
- W4306390980 citedByCount "2" @default.
- W4306390980 countsByYear W43063909802023 @default.
- W4306390980 crossrefType "journal-article" @default.
- W4306390980 hasAuthorship W4306390980A5039591696 @default.
- W4306390980 hasAuthorship W4306390980A5040702505 @default.
- W4306390980 hasAuthorship W4306390980A5078250469 @default.
- W4306390980 hasAuthorship W4306390980A5090049720 @default.
- W4306390980 hasConcept C119857082 @default.
- W4306390980 hasConcept C127313418 @default.
- W4306390980 hasConcept C136764020 @default.
- W4306390980 hasConcept C154945302 @default.
- W4306390980 hasConcept C169258074 @default.
- W4306390980 hasConcept C187320778 @default.
- W4306390980 hasConcept C24939127 @default.
- W4306390980 hasConcept C37616216 @default.
- W4306390980 hasConcept C39432304 @default.