{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:23:27Z","timestamp":1785813807273,"version":"3.56.0"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-16-CONV-0004."],"award-info":[{"award-number":["ANR-16-CONV-0004."]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N\u2013PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.<\/jats:p>","DOI":"10.3390\/rs14010216","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"216","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture"],"prefix":"10.3390","volume":"14","author":[{"given":"Eva","family":"Lopez-Fornieles","sequence":"first","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"},{"name":"ChemHouse Research Group, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guilhem","family":"Brunel","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Rancon","sequence":"additional","affiliation":[{"name":"CNRS, IMS, UMR No. 5218, Groupe Signal et Image, Universit\u00e9 de Bordeaux, 33405 Talence, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Belal","family":"Gaci","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"},{"name":"ChemHouse Research Group, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"Metz","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"},{"name":"ChemHouse Research Group, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas","family":"Devaux","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, LISAH, University Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Taylor","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Tisseyre","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2123-5266","authenticated-orcid":false,"given":"Jean-Michel","family":"Roger","sequence":"additional","affiliation":[{"name":"INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, France"},{"name":"ChemHouse Research Group, 34000 Montpellier, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.isprsjprs.2018.05.020","article-title":"A Novel Spectrum Enhancement Technique for Multi-Temporal, Multi-Spectral Data Using Spatial-Temporal Filtering","volume":"142","author":"Albanwan","year":"2018","journal-title":"ISPRS J. 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