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(RDR), Sensor Data Record (SDR), Intermediate Product (IP), Application Related Product (ARP), Environmental Data Record (EDR), and Climate Data Record (CDR) so that the data are ready to be utilized by the end users. VI themselves are one of the EDR level data that are processed from VIIRS instrument data.
The rawdata level data to RDR level data processing system has been built in the previous activity (Gustiandi, Indradjad, & Bagdja, 2013). Then, the system was developed so that it can produce SDR level data from RDR level data (Gustiandi and Indradjad, 2013). Although the system is physically located remote from the ground station, but it behaves as an integrated system with the ground station.
However, the processing system that can produce VI from their corresponding SDR level data was not available yet int LAPAN’s remote sensing ground station. Hence, we developed the system further so that it can produce VI products by adopting previous system and integrating it to the ground station seamlessly.
2 System Description, Data, and Evaluation Method
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2014) states that the software only compatible with RT-STPS version 5.5, hence we keep on using RT-STPS version 5.5 in developing the system. Whereas both CSPP SDR and EDR softwares are maintained and developed by University of Wisconsin Space Science and Engineering Center (SSEC) Cooperative Institute for Meteorological Satellite Studies (CIMSS) to support the DB meteorological and environmental satellite communities.
Traditionally, architecture of a system or software integration can be explained by a flowchart. The flowchart has purpose to describe the computation domain (components or sub systems) and communication domain (connectors) that construct the system as a whole (Bass, Clements, &
Kazman, 2003). Figure 1 shows a flowchart of Suomi NPP remote sensing satellite data processing system to produce vegetation indices that has been developed.
Figure 1 Suomi NPP remote sensing data processing system to produce vegetation indices that has been developed.
First, LAPAN remote sensing ground station acquires Suomi NPP satellite signals in direct broadcast mode. Then the signals are converted by ingesting system to become rawdata level data and stored in acquisition storage system. Later, these rawdata are copied from the acquisition storage system in Parepare to the developed processing storage system in Jakarta through VPN. RT-STPS software then is used to create native binary format EOS-compliant in Raw Data Record (RDR) level data from the rawdata level data. The conversion process includes frame synchronizing, Pseudo-Noise (PN) decoding, Reed-Solomon (RS) decoding, time ordering, and separating instrument data streams into independent files as well as writing to socket for real-time data relay. In this stage, the developed system produces 4 (four) RDR level data, each of them are VIIRS RDR, ATMS RDR, CrIS RDR, and OMPS RDR. The RDR level data from the CERES instrument, however, have not been produced yet by the current version of RT-STPS that is used in the developed system. All of the RDR level data then are saved back into processing storage system.
Afterwards, VIIRS instrumen RDR level data are processed into Sensor Data Record (SDR) level data by using CSPP SDR software. Note that RDR level data from other instruments are not processed further because vegetation indices only require VIIRS instrument data. SDR level data are full resolution instrument data that are time marked, geolocated, and calibrated by applying the ancillary information (downloaded automatically from NOAA’s server computers at runtime), including radiometric and geometric calibration coefficients and Earth-referencing parameters such as satellite attitude. Ancillary data not only includes coefficients and parameters that are required to process RDR level data into SDR level data, but also coefficients and parameters that are required to transform back SDR level data into corresponding RDR data. The resulted SDR level data then are also saved back into processing storage system.
Last, CSPP EDR software was used to process VIIRS instrument SDR level data into vegetation indices. The processing step requires a number of static and dynamic ancillary data. The software first diagnoses the VIIRS instrument SDR level data based on date and time to compare with the mandatory ancillary data. It then will search the ancillary data internally, and if they are not exist, the
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software will download the mandatory ancillary data from the SSEC servers. Although they are different than the ancillary data from the NOAA’s server, the mandatory ancillary data will have the similar structure. All VIIRS instrument SDR level data are required to be located in the same directory. Vegetation indices that are produced as the output of the developed processing system then are saved back into processing storage system.
All the softwares that are used in the developed system were available separately. They were integrated by using bash shell scripting language into one integrated processing system. The main consideration to use this language is because the language has been known widely as the most comprehensive scripting language to work within Linux environment (Michael, 2008; Parker, 2011;
Shoots Jr., 2012) that is used as the operating system. Moreover, all of the softwares that are used in the developed system are mostly have been built by using bash shell scripting.
The system directory structure was designed to have separately directories for softwares installation, processing steps, and processing results storage. The directories for each processing steps were made separately because each processing steps require that there must no data exist in the directory before they can begin processing the data. Furthermore, the directories structure for the data storage requirement were separated based on their processing level and acquisition datetime to facilitate easier data search in the future. The simplified directories structure for the developed system is illustrated in Figure 2.
Figure 2 Simplified directories structure for Suomi NPP remote sensing satellite data processing system to produce vegetation indices.
2.2 Data
There are 2 (two) kinds of data that were used in developing the system. First, sample data in rawdata, RDR, SDR levels that accompanied RT-STPS, CSPP SDR, and CSPP EDR softwares.
These data were used to validate the installation progress for each of the softwares. These data also
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were used to validate the integrated developed system to ensure that there is no mistake both in processing flow and output data for each processing steps. Second, Suomi NPP remote sensing satellite “real” data in rawdata level that were acquired in direct broadcast mode by LAPAN remote sensing ground station. These data were used to validate conformity of the developed system not only for sample data but also for real data.
2.3 Evaluation Method
All of the hardwares that were used in the developed system have fullfiled the minimum specifications that are required as stated in operating system and each of the integrated softwares installation technical document. But, the main consideration must be taken to the storage system. The capacity of the storage system is limited so that it can contribute to one of the key system quality attributes, maintainability (Pahl et al., 2009). Maintainability can determine whether a system is useful or not.
Hence, it is important to know whether the existing storage system can accommodate Suomi NPP remote sensing satellite data in rawdata level and their associated resulted SDR level and vegetation indices data.
The storage requirement can be determined by using one of the three kinds of evaluation methods or techniques that are existed, as follows: empirical method, simulation technique, and analysis modeling (Lilja, 2000). Empirical method is done through metric calculation or measurement activities. Simulation technique is done by using artificial behaviour of a program execution. Analysis modeling is done via explanation in mathematical way about the running program. From the three methods or techniques, the empirical method gives the most accurate result because it directly connects with the actual data and do not use asumptions as many as the other method or technique.
Therefore, the storage system requirement was evaluated by using empirical method.
Empirical method itself can be done by using 2 (two) kinds of approach, namely sampling and event tracing. Sampling approach is done by taking some processing results or output data after the system runs several times. One of the benefits of this approach is evaluation implementation can be done without stopping the running system first. In the other hand, event tracing approach is done by inserting tracing code into the programs that formed the developed system. Before the evaluation can be implemented, the running system must be stopped first, the tracing code must be inserted, and then the stopping system is run back again. Evaluation implementation by using event tracing approach will require more system resource such as memory be compared with sampling approach (Lilja, 2000). Hence, the evaluation implementation of storage system requirement was evaluated by using empirical method with sampling approach.
Sample data that were used in evaluation implementation were 688 data in rawdata level and their corresponding SDR level data as well as vegetation indices data. The acquisition times were from June 27th, 2014 to February 25th, 2015. Actually there were more than 688 data in rawdata level and their corresponding SDR level data. But, there were only 688 vegetation indices data that have been produced in the period of question. It was because not all rawdata level data were successfully processed into SDR level data or not all SDR level data were successfully processed into vegetation indices data. Several factors can influence this, such as too little scanned area or incomplete SDR level data. Evaluation was implemented by measuring smallest, biggest, and average size of resulted vegetation indices data.
Besides size of resulted data, evaluation was also implemented to processing speed so that it can be known whether the developed system has potential to be developed further in the future by implementing automation method in order to make the system as the near real time system.