The Characterization of Earth Sediments using Radiative Transfer Models from Directional Hyperspectral Reflectance

Remote sensing techniques are continuously being developed to extract physical information about the Earth’s surface. Over the years, space-borne and airborne sensors have been used for the characterization of surface sediments. Geophysical properties of a sediment surface such as its density, grain size, surface roughness, and moisture content can influence the angular dependence of spectral signatures, specifically the Bidirectional Reflectance Distribution Function (BRDF). Models based on radiative transfer equations can relate the angular dependence of the reflectance to these geophysical variables. Extraction of these parameters can provide a better understanding of the Earth’s surface, and play a vital role in various environmental modeling processes. In this work, we focused on retrieving two of these geophysical properties of earth sediments, the bulk density and the soil moisture content (SMC), using directional hyperspectral reflectance. We proposed a modification to the radiative transfer model developed by Hapke to retrieve sediment bulk density. The model was verified under controlled experiments within a laboratory setting, followed by retrieval of the sediment density from different remote sensing platforms: airborne, space-borne and a ground-based imaging sensor. The SMC was characterized using the physics based multilayer radiative transfer model of soil reflectance or MARMIT. The MARMIT model was again validated from experiments performed in our controlled laboratory setting using several different soil samples across the United States; followed by applying the model in mapping SMC from imagery data collected by an Unmanned Aerial System (UAS) based hyperspectral sensor

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected

Bidirectional Distribution Reflection Function of Algodones Dunes

The primary objective of this project was to develop the Algodones Dunes as a pseudo-invariant calibration site (PICS) suitable for absolute calibration of satellite sensors based on a surface reflectance model. Two approaches were taken during this research: a field campaign and laboratory measurements. The first approach, the field campaign, was accomplished with a trip to the Algodones Dunes from the 8th to the 13th of March, 2015. During the field campaign, several test points from spatially different regions of the Algodones Dunes were studied. Reflectance of the sand at each test point was observed from different view angles. The second approach, laboratory testing, involved bringing several sand samples from different regions of the Algodones Dunes back to SDSU for further analysis. The laboratory setup was built in the SDSU optics laboratory and included the use of a light source, digital power supply, and mechanical arm to study the spectral responses of the sand samples from the field. During the laboratory measurements, the reflectance of each of the sand sample, was observed from different view angles to replicate field measurement techniques. Through both approaches it was found that the reflectance of sand samples from the Algodones Dunes changes quadratically with respect to view zenith angle. To correlate field and laboratory measurements, two solar zenith angles were chosen for laboratory simulation, i.e. 45and 54.4. Since the solar zenith angle varies from 20to 60 over a year in the Algodones Dunes, angles within that range were chosen for the solar zenith angles used in the laboratory measurements. The spectral response of different sand samples were only observed under those two chosen solar zenith angles. Since different equipment was used in the laboratory than in the field, there was some degree of uncertainty due to each of the differing instruments which influenced the data. A Linear Mixed Model was therefore developed in order to incorporate the laboratory uncertainties and predict a more accurate model using the raw data acquired in the laboratory. The data modeled by the Linear Mixed Model approach for different BRDF runs of the same sample, and for different sand samples, were compared to determine whether the spectral response of sand samples from the Algodones Dunes is the same or not. Based on the data modeled by the Linear Mixed Model, it was found that the spectral responses of sand samples brought from the Algodones Dunes are the same. A simple BRDF model was then developed for those angles that are perpendicular to the principal plane of the solar illumination

On-site surface reflectometry

The rapid development of Augmented Reality (AR) and Virtual Reality (VR) applications over the past years has created the need to quickly and accurately scan the real world to populate immersive, realistic virtual environments for the end user to enjoy. While geometry processing has already gone a long way towards that goal, with self-contained solutions commercially available for on-site acquisition of large scale 3D models, capturing the appearance of the materials that compose those models remains an open problem in general uncontrolled environments. The appearance of a material is indeed a complex function of its geometry, intrinsic physical properties and furthermore depends on the illumination conditions in which it is observed, thus traditionally limiting the scope of reflectometry to highly controlled lighting conditions in a laboratory setup. With the rapid development of digital photography, especially on mobile devices, a new trend in the appearance modelling community has emerged, that investigates novel acquisition methods and algorithms to relax the hard constraints imposed by laboratory-like setups, for easy use by digital artists. While arguably not as accurate, we demonstrate the ability of such self-contained methods to enable quick and easy solutions for on-site reflectometry, able to produce compelling, photo-realistic imagery. In particular, this dissertation investigates novel methods for on-site acquisition of surface reflectance based on off-the-shelf, commodity hardware. We successfully demonstrate how a mobile device can be utilised to capture high quality reflectance maps of spatially-varying planar surfaces in general indoor lighting conditions. We further present a novel methodology for the acquisition of highly detailed reflectance maps of permanent on-site, outdoor surfaces by exploiting polarisation from reflection under natural illumination. We demonstrate the versatility of the presented approaches by scanning various surfaces from the real world and show good qualitative and quantitative agreement with existing methods for appearance acquisition employing controlled or semi-controlled illumination setups.Open Acces

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits

Evaluation of sensor, environment and operational factors impacting the use of multiple sensor constellations for long term resource monitoring

Moderate resolution remote sensing data offers the potential to monitor the long and short term trends in the condition of the Earth’s resources at finer spatial scales and over longer time periods. While improved calibration (radiometric and geometric), free access (Landsat, Sentinel, CBERS), and higher level products in reflectance units have made it easier for the science community to derive the biophysical parameters from these remotely sensed data, a number of issues still affect the analysis of multi-temporal datasets. These are primarily due to sources that are inherent in the process of imaging from single or multiple sensors. Some of these undesired or uncompensated sources of variation include variation in the view angles, illumination angles, atmospheric effects, and sensor effects such as Relative Spectral Response (RSR) variation between different sensors. The complex interaction of these sources of variation would make their study extremely difficult if not impossible with real data, and therefore, a simulated analysis approach is used in this study. A synthetic forest canopy is produced using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and its measured BRDFs are modeled using the RossLi canopy BRDF model. The simulated BRDF matches the real data to within 2% of the reflectance in the red and the NIR spectral bands studied. The BRDF modeling process is extended to model and characterize the defoliation of a forest, which is used in factor sensitivity studies to estimate the effect of each factor for varying environment and sensor conditions. Finally, a factorial experiment is designed to understand the significance of the sources of variation, and regression based analysis are performed to understand the relative importance of the factors. The design of experiment and the sensitivity analysis conclude that the atmospheric attenuation and variations due to the illumination angles are the dominant sources impacting the at-sensor radiance

Uncertainty Analysis for Input Parameters of the Atmospheric Compensation Process in Airborne Imaging Spectroscopy.

In airborne imaging spectroscopy for the Visible Shortwave Infrared (VSWIR) wavelength range the state of the atmosphere can have a large influence on the values detected by optical sensors like APEX or AVIRIS NG. Since the value of interest is the reflectance property of the surface, atmospheric effects need to be compensated for. Atmospheric compensation algorithms like ATCOR-4 use radiance images as input data. In theory, the algorithm would then estimates the hemispherical-directional reflectance factor as a function of radiance intensity minus the influence of atmospheric particles and processes. Assuming the atmospheric parameters to be independent of the radiance intensity, a higher obtained radiance would necessarily lead to a higher estimated reflectance factor. The atmospheric compensation, however, is not a linear function and therefore the resulting images might show errors. This thesis presents an uncertainty analysis for the radiance intensity as one of several independent and non-independent input parameters and variables of ATCOR-4. The analysis is done by modelling the radiance images with factors drawn from a normal probability distribution and simulating the corresponding reflectance factor images with consideration of various other parameters and variables. Generally, the resulting HDRF can be found to have a wavelength dependent standard uncertainty of 0 to 0.15% associated with the radiance intensity. The uncertainty values are put into perspective by showing how other factors like a) the solar reference spectrum, b) the solar azimuth and zenith angle, c) the sensor uncertainty, d) different steps within the atmospheric compensation process, e) the choice of terrain mode in ATCOR-4 and f) the adjacency effect do all have an influence on the HDRF as well

Absolute Radiometric Calibration of Narrow-Swath Imaging Sensors with Reference to Non-Coincident Wide-Swath Sensors

An inter-calibration method is developed to provide absolute radiometric calibration of narrow-swath imaging sensors with reference to non-coincident wide-swath sensors. The method predicts at-sensor radiance using non-coincident imagery from the reference sensor and knowledge of spectral reflectance of the test site. The imagery of the reference sensor is restricted to acquisitions that provide similar view and solar illumination geometry to reduce uncertainties due to directional reflectance effects. Spectral reflectance of the test site is found with a simple iterative radiative transfer method using radiance values of a well-understood wide-swath sensor and spectral shape information based on historical ground-based measurements. At-sensor radiance is calculated for the narrow-swath sensor using this spectral reflectance and atmospheric parameters that are also based on historical in situ measurements. Results of the inter-calibration method show agreement on the 2 5 percent level in most spectral regions with the vicarious calibration technique relying on coincident ground-based measurements referred to as the reflectance-based approach. While the variability of the inter-calibration method based on non-coincident image pairs is significantly larger, results are consistent with techniques relying on in situ measurements. The method is also insensitive to spectral differences between the sensors by transferring to surface spectral reflectance prior to prediction of at-sensor radiance. The utility of this inter-calibration method is made clear by its flexibility to utilize image pairings with acquisition dates differing in excess of 30 days allowing frequent absolute calibration comparisons between wide- and narrow-swath sensors

Cross Calibration and Validation of Landsat 8 OLI and Sentinel 2A MSI

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross calibration procedure involves i) correction of the MSI data to account for spectral band differences with the OLI; and ii) correction of BRDF effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF corrections, standard least-squares linear regression is used to determine the cross calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. In general, the estimated cross calibration offsets in all bands were small, on the order of 0.0075 or less in magnitude. The cross calibration gains generally varied less than 1.0% from unity; for the Blue and Red bands, the gains varied by approximately -2.5% and - 1.4% from unity, respectively. For a forced zero offset, the estimated gain in all but the Blue band changed little; the Blue band gain varied by approximately 1.86% from unity. Consequently, cross calibration of the Blue band requires both the gain and nonzero offset. To maintain processing consistency, it is recommended to use the gain and (nonzero) offset in all bands. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty, on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include: seasonal changes in solar zenith and azimuth angles, on the order of 2.27%; target site non-uniformity, on the order of 1.8%; variability in atmospheric water vapor and/or aerosol concentration, on the order of 1.29%; and potential shifts in each sensor’s spectral filter central wavelength and/or bandwidth, on the order of 0.82% and 0.28%, respectively

Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (Epics) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors

An increasing number of Earth-observing satellite sensors are being launched to meet the insatiable demand for timely and accurate data to help the understanding of the Earth’s complex systems and to monitor significant changes to them. The quality of data recorded by these sensors is a primary concern, as it critically depends on accurate radiometric calibration for each sensor. Pseudo Invariant Calibration Sites (PICS) have been extensively used for radiometric calibration and temporal stability monitoring of optical satellite sensors. Due to limited knowledge about the radiometric stability of North Africa, only a limited number of sites in the region are used for this purpose. This work presents an automated approach to classify North Africa for its potential use as an extended PICS (EPICS) covering vast portions of the continent. An unsupervised classification algorithm identified 19 “clusters” representing distinct land surface types; three clusters were identified with spatial uncertainties within approximately 5% in the shorter wavelength bands and 3% in the longer wavelength bands. A key advantage of the cluster approach is that large numbers of pixels are aggregated into contiguous homogeneous regions sufficiently distributed across the continent to allow multiple imaging opportunities per day, as opposed to imaging a typical PICS once during the sensor’s revisit period. In addition, this work proposes a technique to generate a representative hyperspectral profile for these clusters, as the hyperspectral profile of these identified clusters are mandatory in order to utilize them for performing cross-calibration of optical satellite sensors. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. Furthermore, this work investigates the performance of extended pseudo-invariant calibration sites (EPICS) in cross-calibration for one of Shrestha’s clusters, Cluster 13, by comparing its results to those obtained from a traditional PICS-based cross-calibration. The use of EPICS clusters can significantly increase the number of cross-calibration opportunities within a much shorter time period. The cross-calibration gain ratio estimated using a cluster-based approach had a similar accuracy to the cross-calibration gain derived from region of interest (ROI)-based approaches. The cluster-based cross-calibration gain ratio is consistent within approximately 2% of the ROI-based cross-calibration gain ratio for all bands except for the coastal and shortwave-infrared (SWIR) 2 bands. These results show that image data from any region within Cluster 13 can be used for sensor crosscalibration. Eventually, North Africa can be used a continental scale PICS