🛰️ Remote Sensing, Crop Models, and Climate Signals
Remote sensing basics, agricultural applications, crop-weather models, climate variability, and the significance of El Nino and La Nina.
Many agricultural problems cannot be understood from one field visit alone. Crop stress, drought, flood, and seasonal climate signals often operate over large areas. Remote sensing and crop modelling make it possible to observe these patterns and convert them into useful planning information.
What is remote sensing?
Remote sensing is the art and science of collecting information about an object or area without direct physical contact.
In agriculture, it is commonly used through:
- ground-based instruments
- aircraft
- satellites
The basic idea is that crops, soil, water, and clouds reflect or emit energy differently, and sensors can capture those differences.
Agricultural applications of remote sensing
Remote sensing is valuable because it can observe large areas repeatedly and quickly.
Important agricultural uses include:
- crop-growth monitoring
- cropped-area estimation
- crop-production forecasting
- drought monitoring
- flood mapping and damage assessment
- wasteland and degraded-land mapping
- land-use and land-cover mapping
- soil and soil-moisture assessment
- irrigation and drainage evaluation
- pest and disease stress detection
- groundwater exploration support
This makes remote sensing useful at both farm-support and policy-planning level.
Platforms used in remote sensing
Ground-based platforms
These are used for detailed local observation and calibration.
Examples:
- infrared thermometers
- spectral radiometers
- some radar-based ground instruments
Air-based platforms
Aircraft can provide high-detail data over a wider area than ground instruments.
Satellite-based platforms
Satellites are most important for large-area agricultural monitoring because they provide:
- repeated coverage
- wide-area observation
- easier monitoring of inaccessible regions
Polar-orbiting and geostationary satellites
Polar-orbiting satellites
These move over the poles and gradually cover large land areas as the earth rotates.
Examples from the source:
- LANDSAT
- SPOT
- IRS
They are especially useful for land and crop observation.
Geostationary satellites
These remain positioned over the same part of the earth relative to the equator.
They are particularly useful for:
- continuous weather observation
- cloud monitoring
- storm tracking
The source notes the importance of the INSAT series in the Indian context.
Polar satellites are especially useful for land observation, while geostationary satellites are especially useful for continuous weather monitoring.
Crop-weather models
Remote sensing is often used together with crop-weather models.
Crop models help estimate:
- crop growth
- crop development stage
- biomass production
- expected yield under different environments and management conditions
They are useful because they connect:
- weather data
- crop physiology
- management practice
into one predictive framework.
Climate change and climate variability
Climate change
Climate change means a long-term shift in the average behaviour of climate.
Climate variability
Climate variability means year-to-year or season-to-season fluctuation around the long-term average.
For agriculture:
- variability affects immediate seasonal risk
- climate change affects long-term planning and adaptation
Causes of climatic variability
The source groups causes into external and internal factors.
External causes
- variation in solar output
- changes in earth’s orbit
- precession of earth’s axis
- change in axial tilt
Internal causes
- change in atmospheric composition
- rise in greenhouse gases
- land-use change such as deforestation
- ocean-atmosphere interactions such as ENSO
- aerosol and pollution effects
Effects of climate change on agriculture
Important agricultural effects include:
- change in crop duration
- altered water requirement
- heat stress at sensitive stages such as flowering or grain filling
- increased pest and disease pressure
- shift in suitable crop zones
- more frequent droughts, floods, and other extremes
Higher carbon dioxide may increase photosynthesis in some cases, but the benefit may be limited by heat, nutrient shortage, or water stress.
El Nino and La Nina
El Nino and La Nina are opposite phases of the ENSO system in the Pacific Ocean.
El Nino
El Nino is associated with abnormal warming of tropical Pacific surface waters.
It often affects:
- rainfall distribution
- monsoon behaviour
- drought risk in some regions
La Nina
La Nina is associated with abnormal cooling of those waters and may produce a different rainfall response.
For Indian agriculture, both signals matter because they can alter monsoon performance and therefore affect:
- sowing
- water storage
- crop growth
- yield
Summary Cheat Sheet
| Topic | Key Point |
|---|---|
| Remote sensing | Collecting information without direct physical contact. |
| Main agricultural uses | Crop monitoring, yield forecasting, drought and flood mapping, soil and water assessment. |
| Platforms | Ground-based, aircraft-based, and satellite-based. |
| Polar satellites | Better suited to land and crop observation. |
| Geostationary satellites | Better suited to continuous weather observation. |
| Crop models | Use weather and crop information to estimate growth and yield. |
| Climate change | Long-term shift in average climate. |
| Climate variability | Short-term fluctuation around the average. |
| El Nino and La Nina | Major climate signals that can influence monsoon behaviour and farm planning in India. |
References
1 source • [1]
References
ICAR e-Courses
Lesson Doubts
Ask questions, get expert answers