Top GIS Techniques for Accurate Agricultural Land Mapping
- Anvita Shrivastava
- 2 hours ago
- 4 min read
The accurate mapping of agricultural land remains an indispensable component of modern agriculture, land use planning and design, crop management and assessment, and resource management. Furthermore, with the advent of Geographic Information Systems (GIS), the way farmers and researchers, and policy-makers apply land attributes, assess land changes, and base management decisions on place, a whole new approach has been gained. Using available spatial analysis, remotely sensed data, and automated processing has elevated the confidence and potential of mapping agricultural landscapes to a level never present in the past.
Satellite Remote Sensing of Crop and Soil Attributes
The ultimate capacity of mapping crops and soils all depends on remote sensing. Satellite remote sensing platforms (e.g., Sentinel-2, Landsat 8/9; Planet) and commercially available platforms provide the opportunity to bring together high-resolution multispectral data for use in GIS software analysis.
Key Remote Sensing Applications
NDVI (Normalized Difference Vegetation Index): measures vegetation health and biomass.
NDWI (Normalized Difference Water Index): measures moisture levels or irrigation patterns.
EVI (Enhanced Vegetation Index): enhances the potential for the detection of vegetation in situations with high canopy density.
Soil Moisture Mapping: characterizing soil conditions and moisture regimes based on microwave remote sensing!
Values
The ability to provide continuous monitoring and change detection.
The provision of low-cost mapping of large areas.
Involvement in precision agriculture or yield estimation.
Classifying Land Use/Land Cover (LULC)
Land Use and Land Cover (LULC) mapping classifies agricultural land, e.g., cropland, pasture, orchards, fallow, or built-up areas.
Techniques Used
Supervised classification (e.g., Maximum Likelihood, Support Vector Machine); training samples are used.
Unsupervised clustering, or classification of the image pixels, is automated.
Object-Based Image Analysis (OBIA) (shape, color, and texture to segment and classify the imagery).
Importance
LULC classification supports:
Agricultural planning
Environmental impact assessments
Monitoring land conversion trends
GPS and GNSS Surveying for Mapping Field Boundaries
Accurate field boundary mapping is critical to determining total acreage for cropping practices, crop insurance, and compliance reporting.
Techniques Used
Differential GPS (DGPS)
Real-Time Kinematic (RTK) GNSS
Mobile GIS data collection via tablets or drones
Benefits
High positional accuracy (centimeter-level with RTK)
Robust mapping of non-rectangular field shapes
Suitability for farm-level GIS databases
Drone (UAV) Photogrammetry
Drones offer ultra-high-resolution imagery with flexible means of data capture, making them very effective for mapping small- to medium-sized agricultural surveying project plans.
Common Drone-Based Applications
Creating orthomosaics
3D surface models
Multispectral drone mapping to assess crop stress
Examples of applications
Monitoring crop growth stages
Detecting pest and disease hot spots
Precision irrigation planning
Spatial Analysis and Geostatistics
GIS spatial analysis tools assist in identifying patterns, relationships, and trends in agricultural landscapes.
Types of Spatial Analysis Techniques
Kriging and IDW Interpolation: Modeling soil and moisture data.
Slope, aspect, and elevation analysis: Evaluating the topography for suitability.
Buffer and proximity analysis: Analyzing any locations, for example, irrigation, pollution, and accessibility considerations.
Hotspot analysis: Identifying places with abnormal performance of crops.
Soil Mapping and Terrain Modeling
Having two specific types of data, soil data and terrain data, is crucial in agriculture.
GIS Techniques for Soil and Terrain Modeling
Soil texture and fertility mapping.
Watershed and drainage modeling.
Erosion risk evaluation.
Benefits
Facilitates precision fertilization.
Helps identify an appropriate type of crop.
Supports better irrigation management.
Agricultural Mapping Using Machine Learning and AI
Machine Learning (ML) and Artificial Intelligence (AI) substantially improve the precision of agricultural land mapping.
Popular ML models in GIS
Random Forest
Support Vector Machine (SVM)
Convolutional Neural Networks (CNNs) for deep learning
Use Cases
Automated crop classification
Yield prediction models
Detecting anomalies in real-time
Cloud GIS platforms
Cloud GIS options, including ArcGIS Online, Google Earth Engine, QGIS Cloud, and AWS Open Data, provide scalable and collaborative mapping for agriculture.
Advantages
Processing of large-scale satellite imagery
Sharing data across teams seamlessly
Live mapping and dashboard visualization.
Recommended Methods for Enhanced Accuracy in Agricultural Mapping
When creating agricultural land maps that can be relied upon, keep the following in mind:
Use the most recent imagery with the highest resolution, if available.
Collect ground truthing data to check your remote sensing data collection techniques.
Utilize the proper classification techniques that will rely on crop type and region.
Utilize time series data that has a seasonal structure to maximize crop identification.
Incorporate drone, satellite, and ground-truth data to advance the means of a spatial database.
GIS technology has changed drastically for agricultural land mapping, from remote sensing and GPS surveying, to AI classification and spatial cloud analytics; the new GIS tools provided surpass anything of the past. With the techniques presented above, farmers, agronomists, and land-use planners will increase crop monitoring, productivity, and support the sustainable development of agriculture.
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