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Article: Extension of the Hapke model to the spectral domain to characterize soil physical properties

TitleExtension of the Hapke model to the spectral domain to characterize soil physical properties
Authors
KeywordsBRDF
Hapke model
POLDER
Single-scattering albedo
Soil hyperspectral reflectance model
SOILSPECT model
Issue Date2022
Citation
Remote Sensing of Environment, 2022, v. 269, article no. 112843 How to Cite?
AbstractThe Hapke bidirectional reflectance model has mainly been used in planetary remote sensing and has given rise to some studies in Earth science. However, it has not yet been comprehensively evaluated using data from different sources, and its ability to model reflectance spectra needs to be further explored. Therefore, the objective of this study was to develop a tangible parametric model of soil hyperspectral bidirectional reflectance via the evaluation and extension of the Hapke model (hereafter named the Hapke-HSR model). Comprehensive directional and spectral soil reflectance datasets, including satellite, field data, two spectral libraries, and simulated reflectance spectra, were used. First, the two widely used versions of the Hapke model, namely, the SOILSPECT and original Hapke models, were compared. Thereafter, the simplified SOILSPECT model was extended to characterize soil hyperspectral reflectance by deriving an approximate relationship between the single-scattering albedo and wavelength. We obtained the following results. (1) Both versions of the Hapke model agreed well in fitting soil bidirectional reflectance data. However, the SOILSPECT model (R2 = 0.983–0.997, RMSE = 0.007–0.014) performed better than the original Hapke model (R2 = 0.800–0.988, RMSE = 0.014–0.057) when both satellite and field data were used. (2) The Hapke-HSR model could effectively capture the characteristics of the soil hyperspectral reflectance (R2 = 0.963–0.983 and RMSE = 0.018–0.028) based on both spectral libraries. The simulated reflectance spectra showed that the Hapke-HSR model can capture the soil moisture content variations (R2 = 0.987, RMSE = 0.011). In addition, the residual prediction deviation (RPD) values of the Hapke-HSR model were greater than 3, indicating a high prediction accuracy. These findings demonstrate that the Hapke-HSR model performs well with respect to the characterization of soil hyperspectral directional reflectance.
Persistent Identifierhttp://hdl.handle.net/10722/316638
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDing, Anxin-
dc.contributor.authorMa, Han-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorHe, Tao-
dc.date.accessioned2022-09-14T11:40:56Z-
dc.date.available2022-09-14T11:40:56Z-
dc.date.issued2022-
dc.identifier.citationRemote Sensing of Environment, 2022, v. 269, article no. 112843-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/316638-
dc.description.abstractThe Hapke bidirectional reflectance model has mainly been used in planetary remote sensing and has given rise to some studies in Earth science. However, it has not yet been comprehensively evaluated using data from different sources, and its ability to model reflectance spectra needs to be further explored. Therefore, the objective of this study was to develop a tangible parametric model of soil hyperspectral bidirectional reflectance via the evaluation and extension of the Hapke model (hereafter named the Hapke-HSR model). Comprehensive directional and spectral soil reflectance datasets, including satellite, field data, two spectral libraries, and simulated reflectance spectra, were used. First, the two widely used versions of the Hapke model, namely, the SOILSPECT and original Hapke models, were compared. Thereafter, the simplified SOILSPECT model was extended to characterize soil hyperspectral reflectance by deriving an approximate relationship between the single-scattering albedo and wavelength. We obtained the following results. (1) Both versions of the Hapke model agreed well in fitting soil bidirectional reflectance data. However, the SOILSPECT model (R2 = 0.983–0.997, RMSE = 0.007–0.014) performed better than the original Hapke model (R2 = 0.800–0.988, RMSE = 0.014–0.057) when both satellite and field data were used. (2) The Hapke-HSR model could effectively capture the characteristics of the soil hyperspectral reflectance (R2 = 0.963–0.983 and RMSE = 0.018–0.028) based on both spectral libraries. The simulated reflectance spectra showed that the Hapke-HSR model can capture the soil moisture content variations (R2 = 0.987, RMSE = 0.011). In addition, the residual prediction deviation (RPD) values of the Hapke-HSR model were greater than 3, indicating a high prediction accuracy. These findings demonstrate that the Hapke-HSR model performs well with respect to the characterization of soil hyperspectral directional reflectance.-
dc.languageeng-
dc.relation.ispartofRemote Sensing of Environment-
dc.subjectBRDF-
dc.subjectHapke model-
dc.subjectPOLDER-
dc.subjectSingle-scattering albedo-
dc.subjectSoil hyperspectral reflectance model-
dc.subjectSOILSPECT model-
dc.titleExtension of the Hapke model to the spectral domain to characterize soil physical properties-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.rse.2021.112843-
dc.identifier.scopuseid_2-s2.0-85121114802-
dc.identifier.volume269-
dc.identifier.spagearticle no. 112843-
dc.identifier.epagearticle no. 112843-
dc.identifier.isiWOS:000759651700001-

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