Water Vapor Retrieval Using the FLAASH Atmospheric Correction Algorithm

Chen

2011 IEEE International Geoscience and Remote Sensing Symposium

et al. 2011

HJ-1A is one member of the Environment and Disaster Monitoring Microsatellite Constellation, which carries a HyperSpectral Imager (HSI) with the spectral resolution about 5nm from 450nm to 950nm. Retrieving columnar water vapor content is essential to the quantitative applications of hypespectral imagery. The ideal best band (940nm) for water vapor retrieval has lower signal-tonoise(S/N) value. In this paper, we choose another weak water absorption band (820nm) to retrieval the columnar water vapor content. Two common water vapor retrieval algorithms, called the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA), are implemented and compared with each other. Simulation results show that these two algorithms have less difference in the accuracy of water vapor retrieval because the weak water vapor absorption effect in 820nm. The water vapor contents derived from HJ-1A HSI by CIBR algorithm are compared with MODIS products and a systematic error may exist in the radiometric calibration of HSI.

Section: Results and Analysissupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Comparison of two water vapor retrieval algorithms for HJ1A hyperspectral imagery

Chen

2011 IEEE International Geoscience and Remote Sensing Symposium

et al. 2011

“…In such situations, techniques such as FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) 10 and QUAC (QUick Atmospheric Correction) 11 can provide atmospheric compensation. While less accurate than ELM, they have the significant advantage of not requiring in-situ targets of known reflectance values.…”

Section: Image Processing Methodologymentioning

confidence: 99%

Crude oil, petroleum product, and water discrimination on terrestrial substrates with airborne imaging spectroscopy

Allen

Krekeler

2011

SPIE Proceedings

The Deepwater Horizon explosion and subsequent sinking produced the largest oil spill in U.S. history. One of the most prominent portions of the response is mapping the extent to which oil has reached thousands of miles of shoreline. The most common method of detecting oil remains visual spotting from airframes, supplemented by panchromatic / multispectral aerial photography and satellite imagery. While this imagery provides a synoptic view, it is often ambiguous in its ability to discriminate water from hydrocarbon materials.By employing spectral libraries for material identification and discrimination, imaging spectroscopy supplements traditional imaging techniques by providing specific criteria for more accurate petroleum detection and discrimination from water on terrestrial backgrounds. This paper applies a new hydrocarbon-substrate spectral library to SpecTIR HST-3 airborne imaging spectroscopy data from the Hurricane Katrina disaster in 2005. Using common material identification algorithms, this preliminary analysis demonstrates the applicability and limitations of hyperspectral data to petroleum/water discrimination in certain conditions. The current work is also the first application of the petroleum-substrate library to imaging spectroscopy data and shows potential for monitoring long term impacts of Deepwater Horizon.

Reconstructing Cloud Contaminated Pixels Using Spatiotemporal Covariance Functions and Multitemporal Hyperspectral Imagery

2019

One of the major challenges in optical-based remote sensing is the presence of clouds, which imposes a hard constraint on the use of multispectral or hyperspectral satellite imagery for earth observation. While some studies have used interpolation models to remove cloud affected data, relatively few aim at restoration via the use of multi-temporal reference images. This paper proposes not only the use of image time-series, but also the implementation of a geostatistical model that considers the spatiotemporal correlation between them to fill the cloud-related gaps. Using Hyperion hyperspectral images, we demonstrate a capacity to reconstruct cloud-affected pixels and predict their underlying surface reflectance values. To do this, cloudy pixels were masked and a parametric family of non-separable covariance functions was automated fitted, using a composite likelihood estimator. A subset of cloud-free pixels per scene was used to perform a kriging interpolation and to predict the spectral reflectance per each cloud-affected pixel. The approach was evaluated using a benchmark dataset of cloud-free pixels, with a synthetic cloud superimposed upon these data. An overall root mean square error (RMSE) of between 0.5% and 16% of the reflectance was achieved, representing a relative root mean square error (rRMSE) of between 0.2% and 7.5%. The spectral similarity between the predicted and reference reflectance signatures was described by a mean spectral angle (MSA) of between 1° and 11°, demonstrating the spatial and spectral coherence of predictions. The approach provides an efficient spatiotemporal interpolation framework for cloud removal, gap-filling, and denoising in remotely sensed datasets.