A Guide on ISRO’s Science Data Archive
1. Introduction to ISRO and ISDA
Skip to Part 3 of this post for details on accessing ISDA data if you’re already familiar with ISRO and the ISDA and just want to figure out how to use it.
Background on ISRO
You might already know of the Indian Space Research Organisation (ISRO) as a key player in global space exploration. If you somehow haven’t, ISRO is not just India’s national space agency but also a global leader in space agencies, and is known for its innovative and cost-efficient missions.
Overview of ISDA
The ISRO Science Data Archive (ISDA) plays a crucial role in the space research community by housing a vast amount of space-related data. This initiative is part of ISRO’s commitment to democratizing space data access for scientific research and enhancing public engagement. Unlike the Indian Space Science Data Centre (ISSDC) overall, which serves the international community, ISDA provides mission-specific data initially exclusive to Indian researchers, which later becomes accessible to the broader Indian public based on a proprietary data release schedule, which is different for each mission and can be found in the documentation for the respective mission.
2. Highlighting Key Missions and Data Types
Mission-specific Data
Each of ISRO’s missions contributes a distinct type of data, intended to drive forward scientific and social progress. Here are a few examples:
- Chandrayaan-2: Offers a wealth of images and scientific data from the lunar surface, aiding in lunar geological studies.
- Mangalyaan (Mars Orbiter Mission): Provides critical data on Mars’ atmosphere and surface features, enhancing our understanding of the planet.
- Astrosat: This mission stands out by offering multiband astronomical observations, supporting a broad range of scientific inquiries from stellar birth to cosmic decay.
Types of Data Available
The ISDA offers various forms of data including raw telemetry (communication/location/speed information important during the course of mission), processed imagery, scientific measurements (using instruments that measure chemical/optical properties, etc.), and datasets derived from aforementioned sources of data, each serving different research needs and facilitating a deeper understanding of our universe when used in context.
3. Data Access & Application — Technical
How to Access Data
Accessing data from the ISDA involves a few steps:
- Start at the ISDA homepage.
- Click ‘View Details’ to explore details of scientific payloads.
- Select ‘Data Download’ to navigate to specific mission archives.
- Register for an account; each mission’s data may require separate logins. It’ll ask you for details, including your nationality.
- Within the portal, utilize the navigation bar to download software for data interaction and handbooks for understanding the measuring/capturing equipment.
A request from my side is to always read the terms under which you can use the data and what you can do with the products of the data carefully.
Example Application—Plotting Lunar Surface using Lasers
Here’s a practical example using data from Chandrayaan-1’s Lunar Laser Ranging Instrument (LLRI). The LLRI is a laser altimeter used to measure the time it takes for a laser pulse to return from the moon’s surface, which helps determine the spacecraft’s altitude. This data has enabled the creation of detailed topographical maps of the lunar surface.
The LBL (label) file attached with the dataset is essential to understand the equipment, how to interpret numbers, and how to read the data.
The dataset’s gaps, presumably due to orbital trajectories, sensor quirks, or data corruption, appear as white areas in our reconstructions. When compared to NASA’s preprocessed color maps of the lunar surface, our reconstructions are reasonably detailed and quite impressive for analysis we can do on a personal computer.
To give a reasonable sense of what it’s like to work with this data, I’ll walk you through the lines of code important to create this plot.
import pandas as pd
import matplotlib.pyplot as plt
# Path to the data file
file_path = './path_to_data/LLRI_data.TAB'
# Loading the data
data = pd.read_csv(file_path, sep='\t', header=None, names=[
"UTC", "Altitude1_Status", "Altitude2_Status", "Altitude3_Status",
"Altitude4_Status", "Laser_Pointing_Latitude", "Laser_Pointing_Longitude",
"Range1_Terrain_Altitude", "Range2_Terrain_Altitude",
"Range3_Terrain_Altitude", "Range4_Terrain_Altitude"
])We start by importing Pandas, which is a Python library useful for working with structured data, as it makes it easy to manipulate the entries of data (rows) and the components of those entries (columns). We also import Matplotlib, which is a popular library for visualizing data in the form of graphs and plots.
Next, we set the path for loading the data, which is wherever you downloaded the data from ISRO’s website. We then read the data into a Pandas Dataframe (the object which will contain our data) called, well, ‘data’. Why we use something like a dataframe instead of just a list data structure in Python becomes quickly apparent with the next step.
# Cleaning the data
data['Laser_Pointing_Latitude'] = pd.to_numeric(data['Laser_Pointing_Latitude'], errors='coerce')
data['Laser_Pointing_Longitude'] = pd.to_numeric(data['Laser_Pointing_Longitude'], errors='coerce')
data['Range1_Terrain_Altitude'] = pd.to_numeric(data['Range1_Terrain_Altitude'], errors='coerce')
data.dropna(inplace=True)So it turns out, you can’t assume that data in the real world is clean, even as it comes from reputable sources. Often, the data is good enough for many purposes but just not yours. In this case, we want to just plot the elevations which were captured properly, so we clean all the numbers that are ‘NaN’-s (which stands for Not a Number, because it is improperly stored). We don’t know if their elevation is 0m or 100m so we just drop all those records.
# Plotting the data
fig, ax = plt.subplots()
sc = ax.scatter(
data['Laser_Pointing_Longitude'],
data['Laser_Pointing_Latitude'],
c=data['Range1_Terrain_Altitude'],
cmap='viridis',
s=1 # point size
)
plt.colorbar(sc, label='Terrain Altitude (km)')
ax.set_xlabel('Longitude (Degrees)')
ax.set_ylabel('Latitude (Degrees)')
ax.set_title('Lunar Surface Reconstruction from LLRI Data')
plt.show()Finally, we decide to create a scatterplot (which just displays dots of different colors, instead of lines which joins points), and set the labels and title for the graph.
Tada! We have the image from before now.
Here is a github notebook for this code (in a more complete form): https://github.com/New-Delhi-Space-Society/ISRO-resources-guide/blob/main/ISDA.ipynb that you can clone and use for your own exploration.
4. Potential Future Explorations
Scientific Research
Researchers are encouraged to utilize ISDA data for groundbreaking studies and can even request ISRO for specific data captures tailored to their projects. Collaboration with academic institutions can further enhance these opportunities.
Public Engagement
The public can engage with high-resolution images and other data through platforms provided by ISDA, potentially contributing to amateur exploratory projects or educational content. Or maybe, citizen scientists could come to play a greater role in the future, given the high level of enthusiasm around STEM in India.
Educational Use
Educational institutions can integrate ISDA data into curriculums, teaching students to handle (often messy) real-world data, and really let them get in deep with using data for space sciences, which many would find very interesting.
This blog serves as a starting point for those interested in exploring the vast resources offered by ISDA. We will keep updating this article with relevant information as we explore further uses of the data and learn more about what is/isn’t allowed, and any other things that would be useful for your usage.
Written by Ashvin Verma for New Delhi Space Society.
