A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data

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dc.citation.issue10
dc.citation.volume15
dc.contributor.authorDehghan-Shoar MH
dc.contributor.authorPullanagari RR
dc.contributor.authorKereszturi G
dc.contributor.authorOrsi AA
dc.contributor.authorYule IJ
dc.contributor.authorHanly J
dc.contributor.editorBerger K
dc.contributor.editorCroft H
dc.contributor.editorLiu T
dc.contributor.editorLu B
dc.contributor.editorYin D
dc.date.accessioned2024-10-08T22:54:07Z
dc.date.available2024-10-08T22:54:07Z
dc.date.issued2023-05-09
dc.description.abstractThe increasing number of satellite missions provides vast opportunities for continuous vegetation monitoring, crucial for precision agriculture and environmental sustainability. However, accurately estimating vegetation traits, such as nitrogen concentration (N%), from Landsat 7 (L7), Landsat 8 (L8), and Sentinel-2 (S2) satellite data is challenging due to the diverse sensor configurations and complex atmospheric interactions. To address these limitations, we developed a unified and physically based method that combines a soil–plant–atmosphere radiative transfer (SPART) model with the bottom-of-atmosphere (BOA) spectral bidirectional reflectance distribution function. This approach enables us to assess the effect of rugged terrain, viewing angles, and illumination geometry on the spectral reflectance of multiple sensors. Our methodology involves inverting radiative transfer model variables using numerical optimization to estimate N% and creating a hybrid model. We used Gaussian process regression (GPR) to incorporate the inverted variables into the hybrid model for N% prediction, resulting in a unified approach for N% estimation across different sensors. Our model shows a validation accuracy of 0.35 (RMSE %N), a mean prediction interval width (MPIW) of 0.35, and an R (Formula presented.) of 0.50, using independent data from multiple sensors collected between 2016 and 2019. Our unified method provides a promising solution for estimating N% in vegetation from L7, L8, and S2 satellite data, overcoming the limitations posed by diverse sensor configurations and complex atmospheric interactions.
dc.description.confidentialfalse
dc.edition.editionMay 2023
dc.identifier.citationDehghan-Shoar MH, Pullanagari RR, Kereszturi G, Orsi AA, Yule IJ, Hanly J. (2023). A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data. Remote Sensing. 15. 10.
dc.identifier.doi10.3390/rs15102491
dc.identifier.eissn2072-4292
dc.identifier.elements-typejournal-article
dc.identifier.number2491
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71644
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2072-4292/15/10/2491
dc.relation.isPartOfRemote Sensing
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectgrasslands nitrogen concentration
dc.subjectmultispectral imagery
dc.subjectradiative transfer modeling
dc.subjecttopographical correction
dc.subjectBRDF
dc.titleA Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data
dc.typeJournal article
pubs.elements-id461968
pubs.organisational-groupOther
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