Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

Columnar aerosol properties in a northeastern atlantic site (plymouth united kingdom) by means of ground based skyradiometer data during years 2000-2008.

Between 2000 and 2008, columnar optical and radiative properties were measured at the Plymouth Marine Laboratory (PML), UK (50° 21.95′N, 4° 8.85′W) using an automatic Prede POM01L sun–sky photometer. The database was analyzed for aerosol optical properties using the SKYRAD radiative inversion algorithm and calibrated using the in situ SKYIL calibration method. Retrievals include aerosol optical depth, Ångström wavelength exponent, aerosol volume distribution, refractive index and single scattering albedo. The results show that the Plymouth site is characterized by low values of aerosol optical depth with low variability (0.18 ± 0.08 at 500 nm) and a mean annual Ångström exponent of 1.03 ± 0.21. The annual mean of the single scattering albedo is 0.97, indicative of non-absorbing aerosols. The aerosol properties were classified in terms of air mass back trajectories: the area is mainly affected by Atlantic air masses and the dominant aerosol type is a mixture of maritime particles, present in low burdens with variable size. The maritime air masses were defined by annual mean values for the AOD (at 500 nm) of 0.13–0.14 and a wavelength exponent of 0.96–1.03. Episodic anthropogenic and mineral dust intrusions occasionally occur, but they are sporadic and dilute (AOD at 500 nm about 0.20). Tropical continental air masses were characterized by the highest AOD at 500 nm (0.34) and the lowest wavelength exponent (0.83), although they were the least represented in the analysis.

Coccolithophore bloom detection in the north east Atlantic using SeaWiFS: Algorithm description, application and sensitivity analysis

Coccolithophores are the largest source of calcium carbonate in the oceans and are considered to play an important role in oceanic carbon cycles. Current methods to detect the presence of coccolithophore blooms from Earth observation data often produce high numbers of false positives in shelf seas and coastal zones due to the spectral similarity between coccolithophores and other suspended particulates. Current methods are therefore unable to characterise the bloom events in shelf seas and coastal zones, despite the importance of these phytoplankton in the global carbon cycle. A novel approach to detect the presence of coccolithophore blooms from Earth observation data is presented. The method builds upon previous optical work and uses a statistical framework to combine spectral, spatial and temporal information to produce maps of coccolithophore bloom extent. Validation and verification results for an area of the north east Atlantic are presented using an in situ database (N = 432) and all available SeaWiFS data for 2003 and 2004. Verification results show that the approach produces a temporal seasonal signal consistent with biological studies of these phytoplankton. Validation using the in situ coccolithophore cell count database shows a high correct recognition rate of 80% and a low false-positive rate of 0.14 (in comparison to 63% and 0.34 respectively for the established, purely spectral approach). To guide its broader use, a full sensitivity analysis for the algorithm parameters is presented.

Absorption-based algorithm of primary production for total and size-fractionated phytoplankton in coastal waters

Most satellite models of production have been designed and calibrated for use in the open ocean. Coastal waters are optically more complex, and the use of chlorophyll a (chl a) as a first-order predictor of primary production may lead to substantial errors due to significant quantities of coloured dissolved organic matter (CDOM) and total suspended material (TSM) within the first optical depth. We demonstrate the use of phytoplankton absorption as a proxy to estimate primary production in the coastal waters of the North Sea and Western English Channel for both total, micro- and nano+pico-phytoplankton production. The method is implemented to extrapolate the absorption coefficient of phytoplankton and production at the sea surface to depth to give integrated fields of total and micro- and nano+pico-phytoplankton primary production using the peak in absorption coefficient at red wavelengths. The model is accurate to 8% in the Western English Channel and 22% in this region and the North Sea. By comparison, the accuracy of similar chl a based production models was >250%. The applicability of the method to autonomous optical sensors and remotely sensed aircraft data in both coastal and estuarine environments is discussed.

Metocean Comparisons of Jason-2 and AltiKa—A Method to Develop a New Wind Speed Algorithm

As well as range, the AltiKa altimeter provides estimates of wave height, Hs and normalized backscatter, s0, that need to be assessed prior to statistics based on them being included in climate databases. An analysis of crossovers with the Jason-2 altimeter shows AltiKa Hs values to be biased high by only »0.05m, with a standard deviation (s.d.) of »0.1m for seven-point averages. AltiKa’s s 0 values are 2.5–3 dB less than those from Jason-2, with a s.d. of »0.3 dB, with these relatively large mismatches to be expected as AltiKa measures a different part of the spectrum of sea surface roughness. A new wind speed algorithm is developed through matchinghistogram of s0 values to that for Jason-2 wind speeds. The algorithm is robust to the use of short durations of data, with a consistency at roughly the 0.1 m/s level. Incorporation of Hs as a secondary input reduces the assessed error at crossovers from 0.82 m/s to 0.71 m/s. A comparison across all altimeter frequencies used to date demonstrates that the lowest wind speeds preferentially develop the shortest scales of roughness.