A near real-time automatic MODIS data processing

A near real-time automatic MODIS data processing system J. D. Shutler, T. J. Smyth, P. E. Land and S. B. Groom. The Remote Sensing Group, Plymouth Marine Laboratory, Prospect Place, Plymouth, Devon, PL1 3DH Email: {jams, tjsm, peland, sbg}@pml.ac.uk

Abstract The MODerate resolution Imaging Spectroradiometer (MODIS) on-board the Aqua and Terra platforms were designed to improve understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. The Dundee Satellite Receiving Station have two X-band receiving systems capable of capturing direct broadcast data from these spacecraft. This receiving range covers the European shelf-areas, north east-Atlantic ocean and the western Mediterranean Sea. Raw data are transferred to the Plymouth Marine Laboratory (PML) via ftp and processed in near real-time into ocean colour and sea-surface temperature products for the UK academic community. Data are free-to-air and are made available through the web within 1.5 hours of the satellite overpass time.

1

Introduction

Near real-time processing of, and access to, Earth observation data is not only essential for weather forecasting but can also play an important role in local economies and the support and guidance of scientific research campaigns. Ocean colour data is the visible wavelength water leaving radiance. These data are determined by the optical properties of seawater, any marine biomass (specifically phytoplankton eg. chlorophyll), coloured dissolved organic matter (CDOM) and suspended particulates that may be present. Ocean colour data can be exploited for tourism and local economic issues including the monitoring of coastal algal blooms [1] and are regularly used to guide research vessels towards areas of interest such as upwelling filaments, eddies or fronts [2]. These applications require fast processing of the data, otherwise time is lost or in some cases data may be deemed useless. Current ocean colour scientific support for research cruises uses data from the Sea viewing Wide Field-ofview Sensor (SeaWiFS). The original lifetime of SeaWiFS was five years, and it is now in its sixth. NASA’s approach to enable continued time-series ocean colour data until 2018 begins with a pair of sensors - the MODerate resolution Imaging Spectroradiometers (MODIS) onboard the Aqua and Terra platforms. These sensors have comparable spatial resolution and greater spectral range when compared with SeaWiFS, together with the advantage of being

on-board multi-sensor platforms. The MODIS sensors are capable of detecting thermal and visible wavelength signals over the same target area simultaneously. These coincident data allow the simultaneous monitoring of sea-surface temperature (SST) and chlorophyll enabling coincident monitoring of both physics and biology. This communication describes the successful design and implementation of a near real-time system to process visible and thermal data from MODIS Terra and Aqua. Investigators wishing to use these processed free-to-air data can gain access through http://www.npm.ac.uk/rsdas.

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MODIS data

The first MODIS sensor was launched on December 18 1999 on-board the Terra polar-orbiting spacecraft and the second sensor, launched on May 4 2002 is on-board the Aqua platform. Together the two MODIS sensors are capable of viewing the entire surface of the Earth every two days. Therefore, they provide at least two passes a day over a particular scene, one from each satellite. The MODIS sensors capture data in 36 spectral bands between 0.405 µm and 14.385 µm, and their possible spatial resolutions at nadir are 250 m, 500 m and 1000 m, dependent on the spectral band selected. Within this spectral range the MODIS SST measurements are sampled in the ranges 3.660 − 4.080 µm and 10.780 − 12.270 µm, making the results from the second interval comparable to those of the Advanced Very High Resolution Radiometer (AVHRR). The data from the two sensors are processed at the Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC). However, these data become available a few days after the satellite overpass, rendering them potentially worthless for near real-time applications. Alternatively, the Dundee Satellite Receiving Station have two X-band receiving systems capable of capturing direct broadcast data from the Terra and Aqua spacecrafts. Dundee’s receiving range covers the European shelf-areas, north east-Atlantic ocean and the western Mediterranean Sea. These data are automatically sent to PML via a 10 Mbits−1 internet connection. Using a compute cluster, 39 ocean products are produced within 1.5 hours of the satellite overpass, facilitating near real-time monitoring and support of research cruises. The generated products include three different chlorophyll algorithms (one of which is directly comparable to that of SeaWiFS), two SST algorithms, fluorescence based chlorophyll retrieval and absorption models e.g. [3, 4].

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Processing

Figure 1 shows the flow diagram of the processing system. The system is formed around the scientific code supplied by the NASA DAAC. Each data pass is typically 1 GB in size and takes around twenty minutes to transfer to Plymouth. The pass is first split into multiple five-minute granules, allowing them to be processed in parallel using an eight node (dual processor) Beowulf cluster running Red Hat. The raw pass data (level 0) are processed using the NASA level 0 to level 1b software. Each five minute granule taking around fifteen minutes to complete. The geolocated and calibrated data (level 1b) are then passed to the ocean

software developed at the University of Miami. This stage taking a further twenty minutes to complete for a five minute granule. Once all granules have been completed, the results are Dundee Satellite Station Satellite swath L0 pass data Internet

Geolocated and calibrated L0 − L1b

Ancillary data

Ocean products L2

Mapping

Web database

Example standard areas

Figure 1: The complete near real-time processing chain.

then stitched back into a complete pass. These are then mapped to standard areas and projections using in-house library routines. These areas are taken from the SeaAPS processing system [5], allowing continuity with the PML SeaWiFS dataset. Running in parallel to the main processing is the automatic downloading and processing of various ancillary datasets, including daily meteorological, ozone data and updated sensor, optical and mirror calibration parameters. These ancillary data are required to produce the ocean products and their automatic integration allows seamless processing. Further, the use of the cluster enables the processing of multiple MODIS passes simultaneously. This results in the efficient processing of Aqua and Terra passes and also the use of the system as a day-to-day processing tool, without any loss in processing speed. Figure 2 shows the comparison of data from MODIS Terra, MODIS Aqua, AVHRR and SeaWiFS for January 27 2004 covering the Celtic sea. All of these MODIS data have processed in near-real time, mapped and then overlaid with a land mask. The difference between the cloud clearing methods and sampling times of the AVHRR and SeaWiFS passes reduce the number of coincident data points, illustrating the advantage of coincident SST and ocean colour measurements using MODIS. Further, these example MODIS data illustrate the higher frequency of measurements that are possible with these two sensors. However, it is noted that MODIS Terra data is effected by ‘striping’ issues as illustrated in Figure 2, an ongoing problem being investigated by NASA. Figure 3 illustrates one advantage of the high number of MODIS passes per day. The data from the Terra pass has been used to replace missing data, due to cloud in the Aqua pass, producing a composite with reduced cloud masking. Figure 3b shows in white those pixels that have benefited from this composite process. It is noted that this composite assumes that the measurements between

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: a) Example MODIS Terra SST data covering the Celtic sea and b) the coincidental chlorophyll estimates of a), both captured at 11:30 UTC january 27 2004. c) The AVHRR SST image at 13:29 UTC and d) The SeaWiFS chlorophyll data for January 27 2004 captured at 13:44 UTC. e) The MODIS Aqua SST image at 13:10 UTC January 27 2004 and f ) the coincidental chlorophyll estimates of e).

(a)

(b)

Figure 3: a) The MODIS Aqua pass of Figure 2f with supplementary data from the Terra pass of Figure 2b replacing areas of cloud b) areas of white represent those pixels in a) that have benefited from the Terra data of 2b.

MODIS Aqua and Terra are comparable, an assumption that will undoubtedly improve as the calibration tables for both MODIS sensors are updated by NASA.

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Future developments and applications

The UK academic community has come to rely upon SeaWiFS as the primary source of ocean colour data since its launch in August 1997. The development of the MODIS processing system at the PML has allowed the seamless transition between SeaWiFS and MODIS; indeed, they currently operate in unison. The ocean colour data from NASA MODIS Aqua and Terra are free-to-air and will be used in much the same way as data from SeaWiFS i.e., the observation of algal blooms, the determination of new and improved algorithms, the assimilation of data into models and the support of research cruises. All these applications will benefit from the multiple daily passes available from the MODIS sensors. However, due to the greater spectral range of data from MODIS, new applications in earth system science should be forthcoming exploiting the synergy between SST and ocean colour. The MODIS sensors are the beginning of a long term ocean colour data initiative by the US agencies, paving the way for the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The processing undertaken by NASA (as previously mentioned) has allowed for a preliminary comparison between the ocean colour products of the two systems. Early results show that there is a good comparison between the two product streams. Future developments will concentrate on the full validation of the products and investigation into producing terrestrial products.

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Acknowledgements

This work was funded by the Natural Environment Research Council (NERC) through the Remote Sensing Data Analysis Service (RSDAS). The authors gratefully acknowledge the inputs of Trevor Reeves, Tony Chen and Andrew Brooks. We thank the SeaWiFS project (code 970.2) and the Dundee Satellite Receiving Station for the reception and transmission of the SeaWiFS and MODIS data to the Plymouth Marine Laboratory. We gratefully acknowledge the Direct Readout Laboratory for the Institutional Algorithms.

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