Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

Nur, Nayma Binte; Bachmann, Charles M.

doi:10.1029/2023jg007381

Cited by 5 publications

(4 citation statements)

References 86 publications

(165 reference statements)

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“…Distance between contours in the BRFs denote a change in reflectance of 2 %, whereas dotted contours in the difference maps denote a change in reflectance of 1 %. These field panels have been used in numerous field experiments over many years [22]- [32], altering their reflective properties. We therefore sought to use panel models applied separately for the field panels by replicating the procedures used for the pristine panel.…”

Section: Field Application Of the Spectralon® Modelmentioning

confidence: 99%

“…This imaging spectrometer was affixed to a BlueSky Mast system, which can elevate the spectrometer platform to various heights [41], [43], [44]. Drone images were acquired with a Headwall Nano VNIR hyperspectral imaging spectrometer within a multi-sensor imaging payload mounted on a DJI Matrice 600 Pro drone platform [32], [45]- [47]. Both unshadowed and shadowed hyperspectral measurements of Spectralon® panels were acquired onsite with an ASD FR4 field spectrometer, while DDRF measurements of the various field panels were acquired in our laboratory with a small Labsphere integrating sphere.…”

Section: B Applying Panel Models To Hyperspectral Imagery In a Field ...mentioning

confidence: 99%

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A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

Lee,

Bachmann,

Nur

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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We present a comprehensive hyperspectral bidirectional reflectance model for a pristine optical-grade white Spectralon® panel with full azimuthal coverage, a wide range of view zenith angles and incident illumination zenith angles, throughout ultraviolet, visible, near infrared and shortwave infrared wavelengths (350-2500 nm). Measurements were acquired using the Goniometer of the Rochester Institute of Technology-Two, which incorporates Analytical Spectral Device FieldSpec FR4 spectroradiometers. Residual plots show that the empirical model is accurate to within 1-2% reflectance within most of the observing hemisphere. We demonstrate an application of the Spectralon® panel model to hyperspectral imagery acquired during a field experiment at the RIT Tait Preserve where a pair of field Spectralon® panels was imaged from various view geometries from both drone and mast-mounted hyperspectral imaging systems.

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Section: Field Application Of the Spectralon® Modelmentioning

confidence: 99%

Section: B Applying Panel Models To Hyperspectral Imagery In a Field ...mentioning

confidence: 99%

A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

Lee,

Bachmann,

Nur

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…151,152 An extended pitchback maneuver over a large angular range would provide an unprecedented opportunity to examine the spectral BRDF to achieve these goals over coastal zone waters and margins. Data downloaded can be directly related to surface measurements, for example, using a hyperspectral goniometer system, 10,[153][154][155][156][157][158][159] which can measure the surface BRDF for validation purposes. However, to undertake a detailed analysis to understand these distributions would require the direct downlink of each frame in the pitchback maneuver necessitating significantly higher bandwidth.…”

Section: Brdf Benefitsmentioning

confidence: 99%

Aquatic ecosystems science using an imaging spectrometer

Lockwood,

Bachmann,

Chrisp

et al. 2023

Imaging Spectrometry XXVI: Applications, Sensors, and Processing

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The study of aquatic ecosystems is an important research area addressing diverse problems such as carbon sequestration in coastal margins and wetlands, kelp and seagrass studies, coral reefs, harmful algal blooms and hypoxia, and carbon cycling in this dynamic environment. The application of an imaging spectrometer to aquatic ecosystem study is particularly challenging due to low water-leaving radiance levels adjacent to the shore region with its higher values. The Committee on Earth Observation Satellites (CEOS) has established more stringent performance standards for the visible/near infrared wavelengths than are typically available in imaging spectrometer designs. We have recently developed a compact form imaging spectrometer, the Chrisp Compact VNIR/SWIR Imaging Spectrometer (CCVIS), that facilitates their modular usage with a wide field telescope without sacrificing performance. The CCVIS design and the operational concept have predicted performance that approaches the CEOS standards. The envisioned satellite implementation requires a pitchback maneuver where the imaging of the slit projected onto the surface is slowly scanned while recording focal plane array readouts at a higher rate thereby avoiding saturation over the land surface while obtaining a high signal-to-noise ratio over the water. The effective frame rate is determined by the time it takes to scan the projected slit one ground sample distance (GSD). This approach has the added benefit of measuring a range of angles during a single GSD acquisition, providing insight into the bidirectional reflectance distribution function (BRDF). DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

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“…Such approaches can provide a valuable means for investigating the relationship between instrument parameters and scene characteristics in airborne and satellite missions, by considering the resource constraints and scales involved. However, physical simulation-based RTM models are mostly used to quantify the physical and biochemical properties of vegetation [14][15][16][17] and these models are still relatively Remote Sens. 2024, 16, 498 3 of 31 unexploited in heterogeneous landscapes like complex forest stands, due to their high computational requirements and the complexity of models involved.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Limits of Species Identification via a Convolutional Neural Network in a Complex Forest Scene through Simulated Imaging Spectroscopy

Chaity,

van Aardt

2024

Remote Sensing

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Imaging spectroscopy (hyperspectral sensing) is a proven tool for mapping and monitoring the spatial distribution of vegetation species composition. However, there exists a gap when it comes to the availability of high-resolution spatial and spectral imagery for accurate tree species mapping, particularly in complex forest environments, despite the continuous advancements in operational remote sensing and field sensor technologies. Here, we aim to bridge this gap by enhancing our fundamental understanding of imaging spectrometers via complex simulated environments. We used DIRSIG, a physics-based, first-principles simulation approach to model canopy-level reflectance for 3D plant models and species-level leaf reflectance in a synthetic forest scene. We simulated a realistic scene, based on the same species composition, found at Harvard Forest, MA (USA). Our simulation approach allowed us to better understand the interplay between instrument parameters and landscape characteristics, and facilitated comprehensive traceability of error budgets. To enhance our understanding of the impact of sensor design on classification performance, we simulated image samples at different spatial, spectral, and scale resolutions (by modifying the pixel pitch and the total number of pixels in the sensor array, i.e., the focal plane dimension) of the imaging sensor and assessed the performance of a deep learning-based convolutional neural network (CNN) and a traditional machine learning classifier, support vector machines (SVMs), to classify vegetation species. Overall, across all resolutions and species mixtures, the highest classification accuracy varied widely from 50 to 84%, and the number of genus-level species classes identified ranged from 2 to 17, among 24 classes. Harnessing this simulation approach has provided us valuable insights into sensor configurations and the optimization of data collection methodologies to improve the interpretation of spectral signatures for accurate tree species mapping in forest scenes. Note that we used species classification as a proxy for a host of imaging spectroscopy applications. However, this approach can be extended to other ecological scenarios, such as in evaluating the changing ecosystem composition, detecting invasive species, or observing the effects of climate change on ecosystem diversity.

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Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

Abstract: Soil moisture content (SMC) is a prominent geophysical variable that is a vital factor in many processes, including carbon cycle, energy budget, erosion, evapo-transpiration, global annual photosynthesis, infiltration,

Cited by 5 publications

References 86 publications

A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

A Comprehensive BRF Model for SpectralonⓇ and Application to Hyperspectral Field Imagery

Aquatic ecosystems science using an imaging spectrometer

Exploring the Limits of Species Identification via a Convolutional Neural Network in a Complex Forest Scene through Simulated Imaging Spectroscopy

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