Skip to main content

India remote sensing


1. Foundational Phase (Early 1970s – Early 1980s)

Objective: To explore the potential of space-based observation for national development.

  • 1972: The Space Applications Programme (SAP) was initiated by the Indian Space Research Organisation (ISRO), focusing on applying space technology for societal benefits.

  • 1975: The Department of Space (DoS) was established, providing an institutional base for space applications, including remote sensing.

  • 1977: India began aerial and balloon-borne experiments to study Earth resources and assess how remote sensing data could aid in agriculture, forestry, and hydrology.

  • 1978 (June 7): Bhaskara-I launched by the Soviet Union — India's first experimental Earth Observation satellite.

    • Payloads: TV cameras (for land and ocean surface observation) and a Microwave Radiometer.

    • Significance: Proved that satellite-based Earth observation was feasible for India's needs.

  • 1981 (November 20): Bhaskara-II launched.

    • Improvements: Enhanced radiometric and optical sensors for land use, forestry, and hydrology.

    • Outcome: Successful demonstration of indigenous data analysis and interpretation.

2. Institutional Strengthening and User Involvement (1980–1985)

Objective: To integrate remote sensing into national planning.

  • 1980: Aryabhata, India's first satellite (for science/technology), laid the foundation for satellite engineering.

  • 1983: Formation of the National Natural Resources Management System (NNRMS).

    • Purpose: Coordinate and integrate the use of remote sensing data with conventional data for national resource management.

    • Structure: Involved multiple user ministries (agriculture, water, forestry, etc.) and established Regional Remote Sensing Service Centres (RRSSCs).

    • Impact: Shift from experimental use to operational planning.

  • 1984: India began receiving data from LANDSAT and NOAA satellites through its own ground station in Shadnagar, Hyderabad — enhancing domestic data processing capabilities.

3. First-Generation Operational Satellites (1988–1995)

Objective: To launch and operate India's own Earth observation satellites.

  • 1988 (March 17): IRS-1A launched by the Soviet Union's Vostok launcher from Baikonur Cosmodrome.

    • Payload: LISS-I (72.5 m) and LISS-II (36.25 m) multispectral sensors.

    • Orbit: Sun-synchronous polar orbit.

    • Applications: Agriculture, forestry, water resource mapping, and soil studies.

    • Significance: Marked the beginning of the Indian Remote Sensing (IRS) series — India's first operational remote sensing satellite.

  • 1991 (August 29): IRS-1B launched — a near-identical successor with improved calibration and longer life.

    • Outcome: Data from IRS-1A/1B validated the national system for operational resource monitoring.

    • Impact: India achieved self-reliance in Earth observation for key resource sectors.

4. Second-Generation IRS Satellites (1995–2000)

Objective: To enhance spatial resolution and expand thematic applications.

  • 1995 (December 28): IRS-1C launched (from Baikonur).

    • Sensors:

      • PAN (5.8 m),

      • LISS-III (23.5 m),

      • WiFS (188 m).

    • Applications: Cartography, urban planning, and vegetation monitoring.

    • Significance: India entered the high-resolution mapping era.

  • 1997 (September 29): IRS-1D launched — similar to IRS-1C, providing stereo coverage and better radiometric accuracy.

  • 1998: Data from IRS-1C/1D began supporting international users, establishing India as a global remote sensing data provider.

5. Diversification and Specialized Missions (1999–2010)

Objective: To develop satellites tailored for thematic domains — oceans, cartography, and natural resources.

  • 1999 (May 26): IRS-P4 (Oceansat-1) launched.

    • Payload: Ocean Colour Monitor (OCM) and Multi-frequency Scanning Microwave Radiometer (MSMR).

    • Use: Ocean productivity, fisheries, and climate studies.

  • 2001 (May 5): IRS-P6 (Resourcesat-1) launched.

    • Sensors: LISS-III, LISS-IV, and AWiFS.

    • Purpose: Detailed agricultural and natural resource management.

  • 2005 (May 5): IRS-P5 (Cartosat-1) launched.

    • Payload: Two panchromatic cameras providing 2.5 m stereo imagery.

    • Applications: High-resolution cartography and digital elevation models (DEMs).

  • 2007 (October 23): Cartosat-2 launched — sub-meter resolution imagery.

    • Use: Urban planning, infrastructure, and defense.

  • 2009 (September 23): Oceansat-2 launched — followed by the Megha-Tropiques and SARAL (joint missions for climate and ocean studies).

6. Modernization and Microwave Era (2011–Present)

Objective: Integration of optical and microwave sensors, enhanced revisit times, and global data distribution.

  • 2013 (April 28): Resourcesat-2 launched — continuation of Resourcesat-1 with improved radiometry.

  • 2016 (March 10): Cartosat-2C launched — 0.65 m resolution; supported Smart City and infrastructure projects.

  • 2018 (March 12): Cartosat-2F launched, maintaining high-resolution optical imaging continuity.

  • 2019 (December 11): RISAT-2BR1 launched — radar imaging satellite with all-weather day-night capability.

    • Microwave sensors: Enabled mapping even under cloud cover, marking India's operational SAR (Synthetic Aperture Radar) era.

  • 2020–2023:

    • Launch of EOS (Earth Observation Satellite) series — replacing IRS nomenclature.

    • Satellites: EOS-01 (RISAT-type), EOS-04 (Radar), EOS-06 (Oceansat-3).

    • Integration of optical, microwave, and hyperspectral imaging systems.

7. Current Framework and Data Access (2020s–Present)

Objective: Streamline commercial and data dissemination processes.

  • 2020: Formation of NewSpace India Limited (NSIL) — a commercial arm of ISRO, responsible for marketing remote sensing data globally.

  • 2021: Launch of Bhoonidhi (Bhuvan-NIDHI) — ISRO's centralized data dissemination platform.

    • Provides access to IRS and EOS datasets for research, academia, and government users.

  • 2023–2025:

    • Integration with Digital India and Gati Shakti initiatives.

    • IRS data now forms the backbone of India's resource management, agriculture monitoring, climate resilience, and disaster response.

    • Over 15 active IRS/EOS satellites, making it the largest civilian remote sensing constellation globally.


PhaseYearsKey SatellitesMajor Achievements
Experimental1978–1981Bhaskara I & IIFirst Earth observation experiments
Institutionalization1983–1987NNRMS setupIntegration of users and ministries
First Generation1988–1991IRS-1A, 1BOperational remote sensing begins
Second Generation1995–2000IRS-1C, 1DHigh-resolution multispectral imaging
Specialized Missions1999–2010Oceansat, Cartosat, ResourcesatThematic data for various domains
Microwave & Modern Era2011–PresentRISAT, EOS seriesAll-weather, day-night, multi-sensor observation


Comments

Post a Comment

Popular posts from this blog

How to find drugs against the Corona. Covid 19

FOR SCIENTISTS (and others interested): How to find drugs against the coronavirus: First clues on how we can beat COVID-19. This shows the many ways we can interfere with its replication cycle by repurposing existing drugs - summarized in today's Science journal. LINK TO ARTICLE:  https://science.sciencemag.org/content/367/6485/1412 .... Vineesh V Assistant Professor of Geography, Directorate of Education, Government of Kerala. https://g.page/vineeshvc
Post a Comment
Read more

Disaster Management

1. Disaster Risk Analysis → Disaster Risk Reduction → Disaster Management Cycle Disaster Risk Analysis is the first step in managing disasters. It involves assessing potential hazards, identifying vulnerable populations, and estimating possible impacts. Once risks are identified, Disaster Risk Reduction (DRR) strategies come into play. DRR aims to reduce risk and enhance resilience through planning, infrastructure development, and policy enforcement. The Disaster Management Cycle then ensures a structured approach by dividing actions into pre-disaster, during-disaster, and post-disaster phases . Example Connection: Imagine a coastal city prone to cyclones: Risk Analysis identifies low-lying areas and weak infrastructure. Risk Reduction includes building seawalls, enforcing strict building codes, and training residents for emergency situations. The Disaster Management Cycle ensures ongoing preparedness, immediate response during a cyclone, and long-term recovery afterw...
Post a Comment
Read more

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...
Post a Comment
Read more

Geographic phenomena fields objects boundaries.

In geography, geographic phenomena refer to features or processes that can be observed and studied on Earth's surface. These phenomena can be classified into three main categories: fields , objects , and boundaries . Each category has distinct characteristics, representations, and applications in Geographic Information Systems (GIS). 1. Fields A field represents continuous, spatially varying data where a value is present at every location within the study area. It describes conditions that exist across a geographic area. Characteristics : Continuity : Fields have no discrete boundaries; the data is continuous. Gradual Variability : The values of a field change gradually across space. Representation : Typically modeled using raster data in GIS, where a grid structure assigns a value (e.g., temperature or elevation) to each cell. Examples : Temperature Map : Shows temperature variation across a region. Rainfall Distribution : Displays rainfall levels over a large g...
Post a Comment
Read more

GIS data continuous discrete ordinal interval ratio

In Geographic Information Systems (GIS) , data is categorized based on its nature (discrete or continuous) and its measurement scale (nominal, ordinal, interval, or ratio). These distinctions influence how the data is collected, analyzed, and visualized. Let's break down these categories with concepts, terminologies, and examples: 1. Discrete Data Discrete data is obtained by counting distinct items or entities. Values are finite and cannot be infinitely subdivided. Characteristics : Represent distinct objects or occurrences. Commonly represented as vector data (points, lines, polygons). Values within a range are whole numbers or categories. Examples : Number of People : Counting individuals on a train or in a hospital. Building Types : Categorizing buildings as residential, commercial, or industrial. Tree Count : Number of trees in a specific area. 2. Continuous Data Continuous data is obtained by measuring phenomena that can take any value within a range...
Post a Comment
Read more