000			02486 am a22002533u 4500
042			_adc
100	1	0	_aHajeb, Mohammad _eauthor _92141
700	1	0	_aHamzeh, Saeid _eauthor _92142
700	1	0	_aAlavipanah, Seyed Kazem _eauthor _92143
700	1	0	_aNeissi, Lamya _eauthor _92144
700	1	0	_aVerrelst, Jochem _eauthor
245	0	0	_aSimultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network
260			_c2023-02.
500			_a/pmc/articles/PMC7614048/
500			_a/pubmed/36644684
520			_aQuantifying biophysical and biochemical vegetation variables is of great importance in precision agriculture. Here, the ability of artificial neural networks (ANNs) to generate multiple outputs is exploited to simultaneously retrieve Leaf area index (LAI), leaf sheath moisture (LSM), leaf chlorophyll content (LCC), and leaf nitrogen concentration (LNC) of sugarcane from Sentinel-2 spectra. We apply a type of ANNs, Bayesian Regularized ANN (BRANN), which incorporates the Bayes' theorem into a regularization scheme to tackle the overfitting problem of ANN and improve its generalizability. Quantitatively assessing the result accuracy indicated RMSE values of 0.48 (m(2)/m(2)) for LAI, 2.36 (% wb) for LSM, 5.85 (μg/cm(2)) for LCC, and 0.23 (%) for LNC, applying simultaneous retrieval. It was demonstrated that simultaneous retrievals of the variables outperformed the individual retrievals. The superiority of the proposed BRANN over a conventional ANN trained with the Levenberg-Marquardt algorithm was confirmed through statistical comparison of their results. The model was applied over the entire Sentinel-2 images to map the considered variables. The maps were probed to qualitatively evaluate the model performance. The results indicated that the retrievals reasonably represent spatial and temporal variations of the variables. Generally, this study demonstrated that the BRANN simultaneous retrieval model can provide faster and more accurate retrievals than those obtained from conventional ANNs and individual retrievals.
540			_a
540			_ahttps://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nInt J Appl Earth Obs Geoinf
856	4	1	_uhttp://dx.doi.org/10.1016/j.jag.2022.103168 _zConnect to this object online.
999			_c2180 _d2180