Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth

Labarre, S.; Jacquemoud, Stéphane; Ferrari, C.; Delorme, Arthur; Derrien, Arthur; Grandin, R.; Jalludin, M.; Lemaitre, F.; Métois, M.; Pierrot-Deseilligny, Marc; Rupnik, Ewelina; Tanguy, B.

doi:10.1016/j.rse.2019.02.014

Cited by 21 publications

(6 citation statements)

References 46 publications

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“…However, it should be noted that soils are very complex systems and the correlation of a spectral feature to one soil property is not as straightforward as, for instance, SOC spectral features, and can be hidden or weakened by iron oxides [84,85]. Additionally, soil water content [85,86] and soil roughness [87,88] can affect the spectral reflectance of soils. The latter comprises the non-Lambertian behavior of soil surfaces due to irregularities and the effects from shadowed areas.…”

Section: Application Fields Of Desismentioning

confidence: 99%

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Alonso¹,

Bachmann

Burch

et al. 2019

Sensors

129

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Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

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Section: Application Fields Of Desismentioning

confidence: 99%

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Alonso¹,

Bachmann

Burch

et al. 2019

Sensors

129

View full text Add to dashboard Cite

show abstract

“…The formulation of the radiative transfer theory derived by Hapke [36] describes the interaction of light with a medium, and taking the physical, geometry, and optical parameters of the particles into account. It is the most widely used photometric model in planetary astronomy, earth observation research and mineralogical information estimation [37,38], and this model is simple and can reproduce the photometric response of bare surfaces well [39]. In the Hapke model, the bidirectional reflectance r(µ 0 , µ) can be derived using Equation (1):…”

Section: Description Of Smr-hapke Modelmentioning

confidence: 99%

Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

et al. 2020

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Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.

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“…Since our study site, the salt panne, was a relatively flat and smooth surface, we chose not to include Hapke's correction for surface roughness [12] in our model. In addition, limitations of the Hapke surface roughness correction have been identified in recent studies by us and others [26,49]. In [26], for example, we showed that the Hapke model does not faithfully reproduce the effects of sub-centimeter scale roughness, especially in the forward scattering direction.…”

Section: Methods: Sources Of Uncertaintymentioning

confidence: 61%

Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

et al. 2020

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This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

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Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth

Cited by 21 publications

References 46 publications

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Data Products, Quality and Validation of the DLR Earth Sensing Imaging Spectrometer (DESIS)

Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

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