Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation

Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram...

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Main Authors: Christian Massimiliano Baldin, Vittorio Marco Casella
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Geosciences
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Online Access:https://www.mdpi.com/2076-3263/15/5/184
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author Christian Massimiliano Baldin
Vittorio Marco Casella
author_facet Christian Massimiliano Baldin
Vittorio Marco Casella
author_sort Christian Massimiliano Baldin
collection DOAJ
description Prior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram matching to calibrate PlanetScope indices to achieve the same values as Sentinel-2 (useful also for proxy). Based on these findings, the authors examined the effectiveness of linear regression in aligning individual bands prior to computing indices to determine if the bands are shifted differently. The research was conducted on five important bands: Red, Green, Blue, NIR, and RedEdge. These bands allow for the computation of well-known vegetation indices like NDVI and NDRE, and soil indices like Iron Oxide Ratio and Coloration Index. Previous research showed that linear regression is not sufficient by itself to align indices in the worst-case scenario. However, this paper demonstrates its efficiency in achieving accurate band alignment. This finding highlights the importance of considering specific scaling requirements for bands obtained from different satellite sensors, such as PlanetScope and Sentinel-2. Contemporary images acquired by the two sensors during May and July demonstrated different behaviors in their bands; however, linear regression can align the datasets even during the problematic month of May.
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spelling doaj-art-a2236451d9d3474fb8037f7f51cd21752025-08-20T03:48:01ZengMDPI AGGeosciences2076-32632025-05-0115518410.3390/geosciences15050184Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index DerivationChristian Massimiliano Baldin0Vittorio Marco Casella1Dipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, 27100 Pavia, ItalyDipartimento di Ingegneria Civile e Architettura, Università degli Studi di Pavia, 27100 Pavia, ItalyPrior research has shown that for specific periods, vegetation indices from PlanetScope and Sentinel-2 (used as a reference) must be aligned to benefit from the experience of Sentinel-2 and utilize techniques such as data fusion. Even during the worst-case scenario, it is possible through histogram matching to calibrate PlanetScope indices to achieve the same values as Sentinel-2 (useful also for proxy). Based on these findings, the authors examined the effectiveness of linear regression in aligning individual bands prior to computing indices to determine if the bands are shifted differently. The research was conducted on five important bands: Red, Green, Blue, NIR, and RedEdge. These bands allow for the computation of well-known vegetation indices like NDVI and NDRE, and soil indices like Iron Oxide Ratio and Coloration Index. Previous research showed that linear regression is not sufficient by itself to align indices in the worst-case scenario. However, this paper demonstrates its efficiency in achieving accurate band alignment. This finding highlights the importance of considering specific scaling requirements for bands obtained from different satellite sensors, such as PlanetScope and Sentinel-2. Contemporary images acquired by the two sensors during May and July demonstrated different behaviors in their bands; however, linear regression can align the datasets even during the problematic month of May.https://www.mdpi.com/2076-3263/15/5/184PlanetScopeSentinel-2remote sensingregressioncalibrationrice
spellingShingle Christian Massimiliano Baldin
Vittorio Marco Casella
Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
Geosciences
PlanetScope
Sentinel-2
remote sensing
regression
calibration
rice
title Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
title_full Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
title_fullStr Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
title_full_unstemmed Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
title_short Comparison of PlanetScope and Sentinel-2 Spectral Channels and Their Alignment via Linear Regression for Enhanced Index Derivation
title_sort comparison of planetscope and sentinel 2 spectral channels and their alignment via linear regression for enhanced index derivation
topic PlanetScope
Sentinel-2
remote sensing
regression
calibration
rice
url https://www.mdpi.com/2076-3263/15/5/184
work_keys_str_mv AT christianmassimilianobaldin comparisonofplanetscopeandsentinel2spectralchannelsandtheiralignmentvialinearregressionforenhancedindexderivation
AT vittoriomarcocasella comparisonofplanetscopeandsentinel2spectralchannelsandtheiralignmentvialinearregressionforenhancedindexderivation