Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture
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...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/1/216 |
| _version_ | 1797497728299696128 |
|---|---|
| author | Eva Lopez-Fornieles Guilhem Brunel Florian Rancon Belal Gaci Maxime Metz Nicolas Devaux James Taylor Bruno Tisseyre Jean-Michel Roger |
| author_facet | Eva Lopez-Fornieles Guilhem Brunel Florian Rancon Belal Gaci Maxime Metz Nicolas Devaux James Taylor Bruno Tisseyre Jean-Michel Roger |
| author_sort | Eva Lopez-Fornieles |
| collection | DOAJ |
| description | 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 <i>R</i><sup>2</sup> 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–PLS 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. |
| first_indexed | 2024-03-10T03:23:24Z |
| format | Article |
| id | doaj.art-b52e30baf5404fc09a41cf92af76eb57 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:23:24Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b52e30baf5404fc09a41cf92af76eb572023-11-23T12:15:00ZengMDPI AGRemote Sensing2072-42922022-01-0114121610.3390/rs14010216Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in AgricultureEva Lopez-Fornieles0Guilhem Brunel1Florian Rancon2Belal Gaci3Maxime Metz4Nicolas Devaux5James Taylor6Bruno Tisseyre7Jean-Michel Roger8INRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceCNRS, IMS, UMR No. 5218, Groupe Signal et Image, Université de Bordeaux, 33405 Talence, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, LISAH, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceINRAE, Institut Agro, ITAP, University Montpellier, 34000 Montpellier, FranceRecent 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 <i>R</i><sup>2</sup> 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–PLS 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.https://www.mdpi.com/2072-4292/14/1/216unfold methodschemometricsSentinel-2multispectral remote sensing |
| spellingShingle | Eva Lopez-Fornieles Guilhem Brunel Florian Rancon Belal Gaci Maxime Metz Nicolas Devaux James Taylor Bruno Tisseyre Jean-Michel Roger Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture Remote Sensing unfold methods chemometrics Sentinel-2 multispectral remote sensing |
| title | Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture |
| title_full | Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture |
| title_fullStr | Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture |
| title_full_unstemmed | Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture |
| title_short | Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture |
| title_sort | potential of multiway pls n pls regression method to analyse time series of multispectral images a case study in agriculture |
| topic | unfold methods chemometrics Sentinel-2 multispectral remote sensing |
| url | https://www.mdpi.com/2072-4292/14/1/216 |
| work_keys_str_mv | AT evalopezfornieles potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT guilhembrunel potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT florianrancon potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT belalgaci potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT maximemetz potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT nicolasdevaux potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT jamestaylor potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT brunotisseyre potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture AT jeanmichelroger potentialofmultiwayplsnplsregressionmethodtoanalysetimeseriesofmultispectralimagesacasestudyinagriculture |