FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4361006854> ?p ?o ?g. }

• W4361006854 endingPage "983" @default.
• W4361006854 startingPage "983" @default.
• W4361006854 abstract "The aboveground dry biomass (AGDB) of winter wheat can reflect the growth and development of winter wheat. The rapid monitoring of AGDB by using hyperspectral technology is of great significance for obtaining the growth and development status of winter wheat in real time and promoting yield increase. This study analyzed the changes of AGDB based on a winter wheat irrigation experiment. At the same time, the AGDB and canopy hyperspectral reflectance of winter wheat were obtained. The effect of spectral preprocessing algorithms such as reciprocal logarithm (Lg), multiple scattering correction (MSC), standardized normal variate (SNV), first derivative (FD), and second derivative (SD); sample division methods such as the concentration gradient method (CG), the Kennard–Stone method (KS), and the sample subset partition based on the joint X–Y distances method (SPXY); sample division ratios such as 1:1 (Ratio1), 3:2 (Ratio2), 2:1 (Ratio3), 5:2 (Ratio4), and 3:1 (Ratio5); dimension reduction algorithms such as uninformative variable elimination (UVE); and modeling algorithms such as partial least-squares regression (PLSR), stepwise multiple linear regression (SMLR), artificial neural network (ANN), and support vector machine (SVM) on the hyperspectral monitoring model of winter wheat AGDB was studied. The results showed that irrigation can improve the AGDB and canopy spectral reflectance of winter wheat. The spectral preprocessing algorithm can change the original spectral curve and improve the correlation between the original spectrum and the AGDB of winter wheat and screen out the bands of 1400 nm, 1479 nm, 1083 nm, 741 nm, 797 nm, and 486 nm, which have a high correlation with AGDB. The calibration sets and validation sets divided by different sample division methods and sample division ratios have different data-distribution characteristics. The UVE method can obviously eliminate some bands in the full-spectrum band. SVM is the best modeling algorithm. According to the universality of data, the better sample division method, sample division ratio, and modeling algorithm are SPXY, Ratio4, and SVM, respectively. Combined with the original spectrum and by using UVE to screen bands, a model with stable performance and high accuracy can be obtained. According to the particularity of data, the best model in this study is FD-CG-Ratio4-Full-SVM, for which the R2c, RMSEc, R2v, RMSEv, and RPD are 0.9487, 0.1663 kg·m−2, 0.7335, 0.3600 kg·m−2, and 1.9226, respectively, which can realize hyperspectral monitoring of winter wheat AGDB. This study can provide a reference for the rational irrigation of winter wheat in the field and provide a theoretical basis for monitoring the AGDB of winter wheat by using hyperspectral remote sensing technology." @default.
• W4361006854 created "2023-03-30" @default.
• W4361006854 creator A5002181207 @default.
• W4361006854 creator A5024507253 @default.
• W4361006854 creator A5030521320 @default.
• W4361006854 creator A5035854819 @default.
• W4361006854 creator A5048237052 @default.
• W4361006854 creator A5055838753 @default.
• W4361006854 creator A5056243202 @default.
• W4361006854 creator A5060269527 @default.
• W4361006854 creator A5072548035 @default.
• W4361006854 creator A5073249308 @default.
• W4361006854 creator A5079017501 @default.
• W4361006854 date "2023-03-26" @default.
• W4361006854 modified "2023-10-05" @default.
• W4361006854 title "Evaluation of Hyperspectral Monitoring Model for Aboveground Dry Biomass of Winter Wheat by Using Multiple Factors" @default.
• W4361006854 cites W1965106709 @default.
• W4361006854 cites W1974128299 @default.
• W4361006854 cites W1979853865 @default.
• W4361006854 cites W1982216854 @default.
• W4361006854 cites W1989700757 @default.
• W4361006854 cites W1997943029 @default.
• W4361006854 cites W2002610435 @default.
• W4361006854 cites W2012358846 @default.
• W4361006854 cites W2021664362 @default.
• W4361006854 cites W2022715909 @default.
• W4361006854 cites W2025003676 @default.
• W4361006854 cites W2059740675 @default.
• W4361006854 cites W2066536516 @default.
• W4361006854 cites W2088067016 @default.
• W4361006854 cites W2122296748 @default.
• W4361006854 cites W2166650558 @default.
• W4361006854 cites W2792889281 @default.
• W4361006854 cites W2887282185 @default.
• W4361006854 cites W2895046594 @default.
• W4361006854 cites W2898280516 @default.
• W4361006854 cites W2904796016 @default.
• W4361006854 cites W2911620309 @default.
• W4361006854 cites W2948082146 @default.
• W4361006854 cites W2970686488 @default.
• W4361006854 cites W2979490443 @default.
• W4361006854 cites W2987174589 @default.
• W4361006854 cites W2992869585 @default.
• W4361006854 cites W3011346529 @default.
• W4361006854 cites W3036028893 @default.
• W4361006854 cites W3047276805 @default.
• W4361006854 cites W3099752826 @default.
• W4361006854 cites W3147649380 @default.
• W4361006854 cites W3172688603 @default.
• W4361006854 cites W4200594661 @default.
• W4361006854 cites W4229375059 @default.
• W4361006854 cites W4281739866 @default.
• W4361006854 cites W4282593578 @default.
• W4361006854 cites W4292745372 @default.
• W4361006854 cites W4306756069 @default.
• W4361006854 cites W4321250404 @default.
• W4361006854 doi "https://doi.org/10.3390/agronomy13040983" @default.
• W4361006854 hasPublicationYear "2023" @default.
• W4361006854 type Work @default.
• W4361006854 citedByCount "1" @default.
• W4361006854 countsByYear W43610068542023 @default.
• W4361006854 crossrefType "journal-article" @default.
• W4361006854 hasAuthorship W4361006854A5002181207 @default.
• W4361006854 hasAuthorship W4361006854A5024507253 @default.
• W4361006854 hasAuthorship W4361006854A5030521320 @default.
• W4361006854 hasAuthorship W4361006854A5035854819 @default.
• W4361006854 hasAuthorship W4361006854A5048237052 @default.
• W4361006854 hasAuthorship W4361006854A5055838753 @default.
• W4361006854 hasAuthorship W4361006854A5056243202 @default.
• W4361006854 hasAuthorship W4361006854A5060269527 @default.
• W4361006854 hasAuthorship W4361006854A5072548035 @default.
• W4361006854 hasAuthorship W4361006854A5073249308 @default.
• W4361006854 hasAuthorship W4361006854A5079017501 @default.
• W4361006854 hasBestOaLocation W43610068541 @default.
• W4361006854 hasConcept C101000010 @default.
• W4361006854 hasConcept C105795698 @default.
• W4361006854 hasConcept C115540264 @default.
• W4361006854 hasConcept C127313418 @default.
• W4361006854 hasConcept C159078339 @default.
• W4361006854 hasConcept C22354355 @default.
• W4361006854 hasConcept C3018661444 @default.
• W4361006854 hasConcept C33923547 @default.
• W4361006854 hasConcept C39432304 @default.
• W4361006854 hasConcept C48921125 @default.
• W4361006854 hasConcept C59822182 @default.
• W4361006854 hasConcept C62649853 @default.
• W4361006854 hasConcept C6557445 @default.
• W4361006854 hasConcept C86803240 @default.
• W4361006854 hasConceptScore W4361006854C101000010 @default.
• W4361006854 hasConceptScore W4361006854C105795698 @default.
• W4361006854 hasConceptScore W4361006854C115540264 @default.
• W4361006854 hasConceptScore W4361006854C127313418 @default.
• W4361006854 hasConceptScore W4361006854C159078339 @default.
• W4361006854 hasConceptScore W4361006854C22354355 @default.
• W4361006854 hasConceptScore W4361006854C3018661444 @default.
• W4361006854 hasConceptScore W4361006854C33923547 @default.
• W4361006854 hasConceptScore W4361006854C39432304 @default.
• W4361006854 hasConceptScore W4361006854C48921125 @default.

• next