Shortwave infrared hyperspectral imaging as a novel method to elucidate multi-phase dolomitization, recrystallization, and cementation in carbonate sedimentary rocks

Carbonate rocks undergo low-temperature, post-depositional changes, including mineral precipitation, dissolution, or recrystallisation (diagenesis). Unravelling the sequence of these events is time-consuming, expensive, and relies on destructive analytical techniques, yet such characterization is essential to understand their post-depositional history for mineral and energy exploitation and carbon storage. Conversely, hyperspectral imaging offers a rapid, non-destructive method to determine mineralogy, while also providing compositional and textural information. It is commonly employed to differentiate lithology, but it has never been used to discern complex diagenetic phases in a largely monomineralic succession. Using spatial-spectral endmember extraction, we explore the efficacy and limitations of hyperspectral imaging to elucidate multi-phase dolomitization and cementation in the Cathedral Formation (Western Canadian Sedimentary Basin). Spectral endmembers include limestone, two replacement dolomite phases, and three saddle dolomite phases. Endmember distributions were mapped using Spectral Angle Mapper, then sampled and analyzed to investigate the controls on their spectral signatures. The absorption-band position of each phase reveals changes in %Ca (molar Ca/(Ca + Mg)) and trace element substitution, whereas the spectral contrast correlates with texture. The ensuing mineral distribution maps provide meter-scale spatial information on the diagenetic history of the succession that can be used independently and to design a rigorous sampling protocol.

An Endmember Bundle Extraction Method Based on Multiscale Sampling to Address Spectral Variability for Hyperspectral Unmixing

With the improvement of spatial resolution of hyperspectral remote sensing images, the influence of spectral variability is gradually appearing in hyperspectral unmixing. The shortcomings of endmember extraction methods using a single spectrum to represent one type of material are revealed. To address spectral variability for hyperspectral unmixing, a multiscale resampling endmember bundle extraction (MSREBE) method is proposed in this paper. There are four steps in the proposed endmember bundle extraction method: (1) boundary detection; (2) sub-images in multiscale generation; (3) endmember extraction from each sub-image; (4) stepwise most similar collection (SMSC) clustering. The SMSC clustering method is aimed at solving the problem in determining which endmember bundle the extracted endmembers belong to. Experiments carried on both a simulated dataset and real hyperspectral datasets show that the endmembers extracted by the proposed method are superior to those extracted by the compared methods, and the optimal results in abundance estimation are maintained.

DLR HySU—A Benchmark Dataset for Spectral Unmixing

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.

DLR HySU - A Benchmark Dataset for Spectral Unmixing

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixe assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

A Framework of Generating 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data

<p><strong>Abstract: </strong>Surface albedo is a fundamental radiative parameter as it controls the Earth’s surface energy budget and directly affects the Earth’s climate. A new method is proposed of generating 10-m high-resolution spectral surface albedo from Sentinel-2 L1C top-of-atmosphere (TOA) reflectance and MODIS bi-directional reflectance distribution function (BRDF) data. This high-resolution spectral surface albedo generation system will be described and consists of 5 parts: 1) retrieval of Sentinel-2 spectral surface reflectance using the Sensor Invariant Atmospheric Correction (SIAC) algorithm; 2) generation of Sentinel-2 cloud mask using machine learning; 3) extraction of pure pixels and their corresponding abundance values from 20-m Sentinel-2 data using an Endmember Extraction Algorithm; 4) inversion of high-resolution albedo from MODIS_albedo/Sentinel2_BRF ratio matrix; and 5) downscaling retrieved 20-m spectral and broadband albedo to 10-m. The SIAC algorithm is developed by [1], and has demonstrated to vastly improve the accuracy of Sentinel-2 atmospheric correction when compared against the use of in situ AERONET data. The machine learning cloud detection approach CloudFCN [2] is based on a Fully Convolutional Network architecture, and has become a standard Deep Learning approach to image segmentation. The CloudFCN exhibits state-of-the-art performance in picking up cloud pixels which is comparable to other methods in terms of performance, high speed, and robustness to many different terrains and sensor types. The endmember extraction uses N-FINDR along with Automatic Target Generation Process to identify the pure pixels from Sentinel-2 spectral data. The extracted pure pixels are used to relate the albedo-to-reflectance matrix with the abundance values of different pure pixels. The high-resolution albedo values are finally retrieved by solving this over-parameterised matrix. This framework also produces a MODIS BRDF prior based on 20-years of MCD43A1 and VNP43A1 daily BRDF data. This BRDF prior is produced on a daily basis, and will be used to temporally interpolate the high-resolution albedo values over pixels that are covered by clouds. The produced high-resolution albedo data will be validated over different tower sites where long-time series of in situ albedo products have been produced [3].</p><p>Keywords: high-resolution, surface albedo, Sentinel-2, SIAC, machine learning, endmember</p><p>[1] Yin, F.; Lewis, P.E.; Gomez-Dans, J.; Wu, Q. A sensor-invariant atmospheric correction method: Application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv, 21 Feb. 2019 web, doi:10.31223/osf.io/ps957.</p><p>[2] Francis, A.; Sidiropoulos, P.; Muller, J.-P. CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning. Remote Sens. 2019, 11, 2312. https://doi.org/10.3390/rs11192312.</p><p>[3] Song, R.; Muller, J.-P.; Kharbouche, S.; Yin, F.; Woodgate, W.; Kitchen, M.; Roland, M.; Arriga, N.; Meyer, W.; Koerber, G.; Bonal, D.; Burban, B.; Knohl, A.; Siebicke, L.; Buysse, P.; Loubet, B.; Leonardo, M.; Lerebourg, C.; Gobron, N. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes. Remote Sens. 2020, 12, 833. https://doi.org/10.3390/rs12050833.</p>