000			03905 am a22003253u 4500
042			_adc
100	1	0	_aTagliabue, Giulia _eauthor _91306
700	1	0	_aBoschetti, Mirco _eauthor _91307
700	1	0	_aBramati, Gabriele _eauthor _91308
700	1	0	_aCandiani, Gabriele _eauthor _91309
700	1	0	_aColombo, Roberto _eauthor _91310
700	1	0	_aNutini, Francesco _eauthor _91311
700	1	0	_aPompilio, Loredana _eauthor _91312
700	1	0	_aRivera-Caicedo, Juan Pablo _eauthor _91313
700	1	0	_aRossi, Marta _eauthor _91314
700	1	0	_aRossini, Micol _eauthor _91315
700	1	0	_aVerrelst, Jochem _eauthor
700	1	0	_aPanigada, Cinzia _eauthor _91317
245	0	0	_aHybrid retrieval of crop traits from multi-temporal PRISMA hyperspectral imagery
260			_c2022-05.
500			_a/pmc/articles/PMC7613384/
500			_a/pubmed/36093126
520			_aThe recently launched and upcoming hyperspectral satellite missions, featuring contiguous visible-to-shortwave infrared spectral information, are opening unprecedented opportunities for the retrieval of a broad set of vegetation traits with enhanced accuracy through novel retrieval schemes. In this framework, we exploited hyperspectral data cubes collected by the new-generation PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency to develop and test a hybrid retrieval workflow for crop trait mapping. Crop traits were mapped over an agricultural area in north-east Italy (Jolanda di Savoia, FE) using PRISMA images collected during the 2020 and 2021 vegetative seasons. Leaf chlorophyll content, leaf nitrogen content, leaf water content and the corresponding canopy level traits scaled through leaf area index were estimated using a hybrid retrieval scheme based on PROSAIL-PRO radiative transfer simulations coupled with a Gaussian processes regression algorithm. Active learning algorithms were used to optimise the initial set of simulated data by extracting only the most informative samples. The accuracy of the proposed retrieval scheme was evaluated against a broad ground dataset collected in 2020 in correspondence of three PRISMA overpasses. The results obtained were positive for all the investigated variables. At the leaf level, the highest accuracy was obtained for leaf nitrogen content (LNC: r(2)=0.87, nRMSE=7.5%), while slightly worse results were achieved for leaf chlorophyll content (LCC: r(2)=0.67, nRMSE=11.7%) and leaf water content (LWC: r(2)=0.63, nRMSE=17.1%). At the canopy level, a significantly higher accuracy was observed for nitrogen content (CNC: r(2)=0.92, nRMSE=5.5%) and chlorophyll content (CCC: r(2)=0.82, nRMSE=10.2%), whereas comparable results were obtained for water content (CWC: r(2)=0.61, nRMSE=16%). The developed models were additionally tested against an independent dataset collected in 2021 to evaluate their robustness and exportability. The results obtained (i. e., LCC: r(2)=0.62, nRMSE=27.9%; LNC: r(2)=0.35, nRMSE=28.4%; LWC: r(2)=0.74, nRMSE=20.4%; LAI: r(2)=0.84, nRMSE=14.5%; CCC: r(2)=0.79, nRMSE=18.5%; CNC: r(2)=0.62, nRMSE=23.7%; CWC: r(2)=0.92, nRMSE=16.6%) evidence the transferability of the hybrid approach optimised through active learning for most of the investigated traits. The developed models were then used to map the spatial and temporal variability of the crop traits from the PRISMA images. The high accuracy and consistency of the results demonstrates the potential of spaceborne imaging spectroscopy for crop monitoring, paving the path towards routine retrievals of multiple crop traits over large areas that could drive more effective and sustainable agricultural practices worldwide.
540			_a
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nISPRS J Photogramm Remote Sens
856	4	1	_uhttp://dx.doi.org/10.1016/j.isprsjprs.2022.03.014 _zConnect to this object online.
999			_c1640 _d1640