BOREAS RSS-01 PARABOLA SSA SURFACE REFLECTANCE AND TRANSMITTANCE DATA

Padang lamun merupakan habitat penting pesisir yang memiliki peran kunci dalam ekosistem pesisir. Kawasan ini merupakan area asuhan bagi ikan-ikan kecil, udang, persembunyian biota dari predatornya, pendaur zat hara, serta penyerap nutrien dari limpasan air laut yang dapat membantu menstabilkan sedimen dan kejernihan air. Kepulauan Tanimbar merupakan salah satu lokasi di Provinsi Maluku dengan potensi sebaran lamun yang cukup luas, namun informasi mengenai sebaran lamun di kawasan ini tidak terdata dengan baik. Teknologi penginderaan jauh merupakan salah satu alternatif untuk mengisi gap data di area yang luas dan sulit dijangkau, termasuk untuk memetakan sebaran lamun di Kepulauan Tanimbar. Penelitian ini bertujuan untuk menyediakan data dasar sebaran dan luas habitat lamun di pesisir Kepulauan Tanimbar. Metode yang digunakan adalah analisis citra penginderaan jauh Landsat 8, menerapkan penajaman citra untuk perairan dangkal menggunakan algoritma Lyzenga. Citra Landsat yang digunakan Landsat Surface Reflectance liputan path/row 106/65 dan 106/66 tahun perekaman 2017. Pengambilan data lapangan dilakukan pada tanggal 1-10 November 2017. Metode pengambilan data lamun dilakukan menggunakan metode seagrass watch . Hasil pengolahan citra menunjukkan lamun terdistribusi merata di seluruh pesisir Kepulauan Tanimbar dengan luas total 5.615,63 hektar dengan tutupan terpadat di sekitar Pulau Seira. Hasil survei lapangan menunjukkan tutupan lamun terpadat dijumpai di Formusan dengan tutupan lamun rata-rata 95%. Kondisi lamun paling baik berada di daerah Sabal, didukung kondisi air yang sangat jernih dengan substrat utama pasir. Berdasarkan hasil pengamatan lapangan, jenis lamun yang ditemukan antara lain: E n h alu s a c o r oid e s , T h ala s sia h e m p ric hii, C y m o d o c e a s e r r ula t a , C y m o d o c e a rotundata, Syringodi um isoetifolium, Halodule uninervis, Halophila ovalis, dan Halophila minor .

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Volume and uncertainty estimates of on-farm reservoirs using surface reflectance and LiDAR data

Environmental Modelling & Software ◽

10.1016/j.envsoft.2021.105095 ◽

2021 ◽

pp. 105095

Author(s):

Ignacio Fuentes ◽

Richard Scalzo ◽

R. Willem Vervoort

Keyword(s):

Lidar Data ◽

Surface Reflectance ◽

Uncertainty Estimates ◽

On Farm

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iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2

Remote Sensing ◽

10.3390/rs13040654 ◽

2021 ◽

Vol 13 (4) ◽

pp. 654

Author(s):

Erwin Wolters ◽

Carolien Toté ◽

Sindy Sterckx ◽

Stefan Adriaensen ◽

Claire Henocq ◽

...

Keyword(s):

Optical Thickness ◽

Vegetation Indices ◽

Atmospheric Correction ◽

Negative Bias ◽

Surface Reflectance ◽

Temporal Consistency ◽

Correction Algorithm ◽

Land Surfaces ◽

Relative Differences ◽

Level 2

To validate the iCOR atmospheric correction algorithm applied to the Sentinel-3 Ocean and Land Color Instrument (OLCI), Top-of-Atmosphere (TOA) observations over land, globally retrieved Aerosol Optical Thickness (AOT), Top-of-Canopy (TOC) reflectance, and Vegetation Indices (VIs) were intercompared with (i) AERONET AOT and AERONET-based TOC reflectance simulations, (ii) RadCalNet surface reflectance observations, and (iii) SYN Level 2 (L2) AOT, TOC reflectance, and VIs. The results reveal that, overall, iCOR’s statistical and temporal consistency is high. iCOR AOT retrievals overestimate relative to AERONET, but less than SYN L2. iCOR and SYN L2 TOC reflectances exhibit a negative bias of ~−0.01 and −0.02, respectively, in the Blue bands compared to the simulations. This diminishes for RED and NIR, except for a +0.02 bias for SYN L2 in the NIR. The intercomparison with RadCalNet shows relative differences < ±6%, except for bands Oa02 (Blue) and Oa21 (NIR), which is likely related to the reported OLCI “excess of brightness”. The intercomparison between iCOR and SYN L2 showed R2 = 0.80–0.93 and R2 = 0.92–0.96 for TOC reflectance and VIs, respectively. iCOR’s higher temporal smoothness compared to SYN L2 does not propagate into a significantly higher smoothness for TOC reflectance and VIs. Altogether, we conclude that iCOR is well suitable to retrieve statistically and temporally consistent AOT, TOC reflectance, and VIs over land surfaces from Sentinel-3/OLCI observations.

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Improved method of hydrous mineral detection by latitudinal distribution of 0.7-μm surface reflectance absorption on the asteroid Ryugu

Icarus ◽

10.1016/j.icarus.2021.114348 ◽

2021 ◽

Vol 360 ◽

pp. 114348

Author(s):

Shingo Kameda ◽

Yasuhiro Yokota ◽

Toru Kouyama ◽

Eri Tatsumi ◽

Marika Ishida ◽

...

Keyword(s):

Surface Reflectance ◽

Latitudinal Distribution ◽

Improved Method ◽

Hydrous Mineral ◽

Mineral Detection

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Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

Remote Sensing ◽

10.3390/rs13132604 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2604

Author(s):

Patrick Osei Darko ◽

Margaret Kalacska ◽

J. Pablo Arroyo-Mora ◽

Matthew E. Fagan

Keyword(s):

Statistical Physics ◽

Near Infrared ◽

Information Gain ◽

Hyperspectral Imagery ◽

Full Range ◽

Surface Reflectance ◽

Mangrove Species ◽

New Approach ◽

Spatio Temporal ◽

Shortwave Infrared

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

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One-shot BRDF imaging system to obtain surface properties

Optical Review ◽

10.1007/s10043-021-00689-x ◽

2021 ◽

Author(s):

Hiroshi Ohno ◽

Takahiro Kamikawa

Keyword(s):

Surface Properties ◽

Imaging System ◽

Reconstruction Method ◽

Surface Reflectance ◽

Bidirectional Reflectance Distribution Function ◽

Bidirectional Reflectance ◽

Color Mapping ◽

System A ◽

Cone Surface ◽

Bidirectional Reflectance Distribution

AbstractThe bidirectional reflectance distribution function (BRDF) that describes an angle-resolved distribution of surface reflectance is available for characterizing surface properties of a material. A one-shot BRDF imaging system can capture an in-plane color mapping of light direction extracted from a surface BRDF distribution. A surface roughness identification method is then proposed here using the imaging system. A difference between surface properties of a matt paper and a glossy paper is experimentally shown to be detected using the method. A surface reconstruction method of an axisymmetric micro-object using the imaging system is also proposed here. The imaging system experimentally shows that it can reconstruct an axisymmetric aluminium cone surface with a height of 37 μm.

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Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

Remote Sensing ◽

10.3390/rs13122309 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2309

Author(s):

Jingjing Tian ◽

Yunyan Zhang ◽

Stephen A. Klein ◽

Likun Wang ◽

Rusen Öktem ◽

...

Keyword(s):

Great Plains ◽

Land Surface ◽

Detection Threshold ◽

Department Of Energy ◽

Surface Reflectance ◽

Cloud Detection ◽

Southern Great Plains ◽

Clear Sky ◽

Shallow Cumulus ◽

Stereo Cameras

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

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Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13101865 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1865

Author(s):

Gabriel Calassou ◽

Pierre-Yves Foucher ◽

Jean-François Léon

Keyword(s):

Matched Filter ◽

Estimation Method ◽

Optimal Estimation ◽

Model Parameters ◽

Surface Reflectance ◽

Sequential Method ◽

Modal Radius ◽

Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

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