Jason Brazile

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

Abstract During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.

APEX: current status of the airborne dispersive pushbroom imaging spectrometer

Over the past few years, a joint Swiss/Belgium ESA initiative resulted in a project to build a precursor mission of future spaceborne imaging spectrometers, namely APEX (Airborne Prism Experiment). APEX is designed to be an airborne dispersive pushbroom imaging spectrometer operating in the solar reflected wavelength range between 4000 and 2500 nm. The system is optimized for land applications including limnology, snow, and soil, amongst others. The instrument is optimized with various steps taken to allow for absolute calibrated radiance measurements. This includes the use of a pre- and post-data acquisition internal calibration facility as well as a laboratory calibration and a performance model serving as a stable reference. The instrument is currently in its breadboarding phase, including some new results with respect to detector development and design optimization for imaging spectrometers. In the same APEX framework, a complete processing and archiving facility (PAF) is developed. The PAF not only includes imaging spectrometer data processing up to physical units, but also geometric and atmospheric correction for each scene, as well as calibration data input. The PAF software includes an Internet based web-server and provides interfaces to data users as well as instrument operators and programmers. The software design, the tools and its life cycle are discussed as well.

Uniformity of Imaging Spectrometry Data Products

The increasing quantity and sophistication of imaging spectroscopy applications have led to a higher demand on the quality of Earth observation data products. In particular, it is desired that data products be as consistent as possible (i.e., ideally uniform) in both spectral and spatial dimensions. Yet, data acquired from real (e.g., pushbroom) imaging spectrometers are adversely affected by various categories of artifacts and aberrations including as follows: singular and linear (e.g., bad pixels and missing lines), area (e.g., optical aberrations), and stability and degradation defects. Typically, the consumer of such data products is not aware of the magnitude of such inherent data uncertainties even as more uncertainty is introduced during higher level processing for any particular application. In this paper, it is shown that the impact of imaging spectrometry data product imperfections in currently available data products has an inherent uncertainty of 10%, even though worst case scenarios were excluded, state-of-the-art corrections were applied, and radiometric calibration uncertainties were excluded. Thereafter, it is demonstrated how this error can be reduced (<5%) with appropriate available technology (onboard, scene, and laboratory calibration) and assimilation procedures during the preprocessing of the data. As a result, more accurate, i.e., uniform, imaging spectrometry data can be delivered to the user community. Hence, the term uniformity of imaging spectrometry data products is defined for enabling the quantitative means to assess the quality of imaging spectrometry data. It is argued that such rigor is necessary for calculating the error propagation of respective higher level processing results and products.

Advanced processing of hyperspectral images

Hyperspectral imaging offers the possibility of characterizing materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral data processing. Our main focus is on the development of approaches able to naturally integrate the spatial and spectral information available from the data. Special attention is paid to techniques that circumvent the curse of dimensionality introduced by high-dimensional data spaces. Experimental results, focused in this work on a specific case-study of urban data analysis, demonstrate the success of the considered techniques. This paper represents a first step towards the development of a quantitative and comparative assessment of advances in hyperspectral data processing techniques.

Cluster versus grid for operational generation of ATCOR's modtran-based look up tables

A critical step in the product generation of satellite or airborne earth observation data is the correction of atmospheric features. Due to the complexity of the underlying physical model and the amount of coordinated effort required to provide, verify and maintain baseline atmospheric observations, one particular scientific modelling program, modtran, whose ancestor was first released in 1972, has become a de facto basis for such processing. While this provides the basis of per-pixel physical modelling, higher-level algorithms, which rely on the output of potentially thousands of runs of modtran are required for the processing of an entire scene. The widely-used atcor family of atmospheric correction software employs the commonly-used strategy of pre-computing a large look up table (lut) of values, representing modtran input parameter variation in multiple dimensions, to allow for reasonable running times in operation. The computation of this pre-computed look up table has previously taken weeks to produce a dvd (about 4GB) of output. The motivation for quicker turnaround was introduced when researchers at multiple institutions began collaboration on extending atcor features into more specialized applications. In this setting, a parallel implementation is investigated with the primary goals of: the parallel execution of multiple instances of modtran as opaque third-party software, the consistency of numeric results in a heterogeneous compute environment, the potential to make use of otherwise idle computing resources available to researchers located at multiple institutions, and acceptable total turnaround time. In both grid and cluster environments, parallel generation of a numerically consistent lut is shown to be possible and reduce ten days of computation time on a single, high-end processor to under two days of processing time with as little as eight commodity CPUs. Runs on up to 64 processors are investigated and the advantages and disadvantages of clusters and grids are briefly explored in reference to the their evaluation in a medium-sized collaborative project.

Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features

Initial steps are proposed and tested in the development of a method for retrieving and (or) refining instrument spectral characteristics for dispersive hyperspectral imagers such as the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS), Compact Airborne Spectrographic Imager (casi), HyMap, Hyperion, and compact high-resolution imaging spectrometer (CHRIS) based on data acquired by the instrument in operation using statistical spectrum matching with moderate-resolution transmittance code (MODTRAN) modelled instrument results in the vicinity of reference Fraunhofer feature windows. Until now, such scene-based retrieval has focused primarily on refining spectral band-centre shifts while assuming that spectral response function (SRF) parameters remain static. In particular, most methods assume that the SRF is of a Gaussian shape. As a consequence of recent investigations showing that scene-based discernment of SRF shape should be feasible given current typical instrument performance, this paper explores algorithmic components deemed necessary for the development of a look-up table (LUT) based retrieval method for obtaining SRF parameters on a band-by-band basis, even in the presence of minor band-centre or bandwidth deviations from nominal instrument specifications. The proposed method employing these components is appropriate for dispersive hyperspectral imagers but not for others, for example Fourier transform hyperspectral imagers. In experiments using nominal implementations of the proposed components, reference spectra match expected LUT spectra in nearly all cases, even when band-centre and bandwidth deviations are considered. This holds true for all three modelled instruments and nearly all of the six selected Fraunhofer windows. Expected signal-to-noise requirements are in many cases challenging, yet feasible using signal-enhancement techniques such as along-track averaging.

Calibration Methodology for the Airborne Dispersive Pushbroom Imaging Spectrometer (APEX)

APEX is a dispersive pushbroom imaging spectrometer operating in the spectral range between 380 - 2500 nm. The spectral resolution will be better than 10 nm in the SWIR and < 5 nm in the VNIR range of the solar reflected range of the spectrum. The total FOV will be ± 14 deg, recording 1000 pixels across track with about 300 spectral bands simultaneously. A large variety of characterization measurements will be performed in the scope of the APEX project, e.g., on-board characterization, frequent laboratory characterization, and vicarious calibration. The retrieved calibration parameters will allow a data calibration in the APEX Processing and Archiving Facility (PAF). The data calibration includes the calculation of the required, time-dependent calibration coefficients from the calibration parameters and, subsequently, the radiometric, spectral and geometric calibration of the raw data. Because of the heterogeneity of the characterization measurements, the optimal calibration for each data set is achieved using a special assimilation algorithm. In the paper the different facilities allowing characterization measurements, the PAF and the new data assimilation scheme are outlined.

Status of the airborne dispersive pushbroom imaging spectrometer APEX (Airborne Prism Experiment)

Over the past few years, a joint Swiss/Belgian initiative resulted in a project to build a new generation airborne imaging spectrometer, namely APEX (Airborne Prism Experiment) under the ESA funding scheme named PRODEX. APEX is designed to be a dispersive pushbroom imaging spectrometer operating in the solar reflected wavelength range between 400 and 2500 nm. The spectral resolution is designed to be better than 10 nm in the SWIR and 5 nm in VIS/NIR range of the spectrum. The total FOV is on the order of /spl plusmn/14/spl deg/, recording 1000 pixels across track, and max. 300 spectral bands simultaneously. The final radiance data products are well characterized and calibrated to be traceable to absolute standards. APEX is subdivided into an industrial team responsible for the optical instrument, the calibration home base, and the detectors, and a science and operational team, responsible for the processing and archiving of the imaging spectrometer data, as well as its operation. APEX is in its design phase with partial breadboarding activities and will be operationally available to the user community in the year 2005.

Assimilation of Heterogeneous Calibration Measurements for the APEX Spectrometer

The underlying algorithmic architecture of the level 0 to 1 processing of the APEX spectrometer is presented. This processing step calculates the observed radiances in physical units from the recorded raw digital numbers. APEX will operate airborne and record radiance in the solar reflected wavelength range. The system is optimized for land applciations including limnology, snow, soil, amongst others. The instrument will be calibrated with a flexible setup in a laboratory as well as on-board. A concept for the dynamic update of the radiance calibration coefficients for the APEX spectrometer is presented. The time evolution of the coefficients is calculated from the heterogeneous calibration measurements with a data assimilation technique. We propose a Kalman filter for the initial version of the processor. Additionally, the structure of the instrument model suitable for the analysis of APEX data is developed. We show that this model can be used for the processing of observations as well as for the calculation of calibration coefficients. Both processes can be understood as inverse problems with the same forward model, i.e. the instrument model.

Spectroscopic Discrimination of Shit from Shinola

We conducted an experiment to determine whether people can tell shit from Shinola (a brand of shoe polish once manufactured in the United States). We find that to the human eye, the two substances are inseparable given similar morphology, whereas with near-infrared spectroscopy one is easily distinguished from the other.