GeoPard Tutorials | Precision Ag Software
  • Introduction to GeoPard
    • GeoPard Platform: Frequently Asked Questions (FAQ)
  • 🖥️Product Tour - Web App
    • Getting Started
      • Create a new farm
      • Draw a new field
      • Upload a field boundary
      • Edit a boundary
      • Edit a field name
      • Add a label
    • 🛰️Satellite Monitoring
      • Order Planet Scope (3m daily) imagery
      • Indices for Crops and Soils
      • 📈Crop Development Index Graph
    • 📊Zones Maps and Analytics
      • Assign Variable Rates in the Zones (Ag inputs Rates Distribution Tool)
      • Creating Zones Map using Satellite Imagery
      • Creating Zones Map using Soil/Yield/As Applied Data
      • Creating Zones Map using Topography
      • Creating Zones Map using a Template
      • Draw Zones Manually, Clone from an existing layer
      • Usage of Data Classification
      • Edit Zones Map: Merge & Split
      • Multi-Year Zones
      • Multi-Layer Analytics
      • Compare layers
      • Heterogeneity factor
      • Relative variation factor
    • 📊Equation-based Analytics
      • Batch Equation analytics
      • Catalog of Predefined Agronomic Equations
      • Catalog of Custom Functions
    • ⛰️Topography
    • ⛰️3D Maps
    • Import
      • Field Boundary
      • Soil Data
      • Yield Data
      • As Applied/As Planted Data
      • Machinery Proprietary Formats
      • Import from MyJohnDeere
    • Export / Download
      • Field Boundary Export
      • Batch Export of Boundaries, Zones and Scouting Pins
      • Zones Map Export as shapefile
      • Satellite Imagery Export as geotiff or geojson
      • Export Scouting Notes as shapefile
      • Export VRA map In ISOXML Format
      • Export to MyJohnDeere
    • 🤖API
    • Operations Log - Track errors/Imports and Analytics
    • Organizations and Roles
    • Farms Sharing between Accounts & Organizations
    • Managing Crop Season information with tags(labels)
      • Case: Managing fields for several clients
      • Case: Season details including crop and year
      • Case: Field operation details
    • ⚙️User Settings
      • Subscriptions, Account limits & Plans
      • Restoring password
      • Order the package
      • Changing preferences
    • 🚜John Deere Operations Center Integration
      • 🚜John Deere Operations Center Integration
        • 1. Create Free Trial Account with you John Deere Account
        • 2. Connect to JohnDeere Operations Center
        • 3. Connect to John Deere Organizations
        • 4. Import into GeoPard from John Deere Operations Center
        • 5. Configure Automated Data Sync
        • 6. Export Rx Maps to John Deere Operations Center as Files
        • 7. Export Field Boundaries into John Deere Operations Center
          • 7a. Organization configuration of export boundary to John Deere Operations Center as File
        • 8. Export Rx Maps to John Deere Operations Center as Work Plans
        • 9. Export Soil, Topography, Satellite or Analytics as Map Layers
      • 🚜John Deere Operations Center Data Sharing
        • 1. "Staff Member" sharing
        • 2. Problem-solving of "Staff Member" sharing
        • 3. "Partner Organization" sharing
        • 4. Problem-solving of "Partner Organization" sharing
        • 5. Problem-solving of import from John Deere to GeoPard
        • 6. Problem-solving of Work Plan creation
        • 7. Sharing Fields/Boundaries Between Partner Organizations in John Deere Ops Center, DataSync config
  • 🚀Changelog & Product Releases
    • Release Notes
      • Release Web April 2025 (Rates Distribution, improved Legends)
      • Release Web March 2025 (Improved Zones, WorkPlans updates, Yield data enhancements)
      • Release Web January 2025 (Free Trial, Usage-based Pricing Plan, USDA Yield Cleaning protocol, Export of calibrated Yield data to John Deere Ops Center, Import of kml)
      • Release Web August 2024 (Data Layer Previews, Spatially localized Zones; Use zones and Equations in new Equations)
      • Release Web July 2024 (Equation Map creation, Spatially localized zones, Seeding and Application Work Plans)
      • Release Web May 2024 (Raw view for Satellite Images, export of Zone Maps as WorkPlan to the John Deere Operations Center, redesign of Batch Analytics)
      • Release Web April 2024 (Batch Equation Maps and enhanced layer transparency)
      • Release Web February 2024 (Per area pricing, units)
      • Release Web January 2024 (many UI improvements)
      • Release Web November 2023 (Clone Polygons, Subscription management)
      • Release Web October 2023 (Yield Calibration, Equation Maps as ISOXML, PDF Export and John Deere Integration)
      • Release Web September 2023 (Cleaning & Calibrating Yield Datasets, more languages support)
      • Release Mobile August 2023 (Mobile app impovements)
      • Release Web July 2023 (Operations Log page, Sum in datasets)
      • Release Web June 2023 (Improved Equations, Operations log v1)
      • Release Mobile May 2023 (Social Login)
      • Release Web May 2023 (John Deere integration improvements)
      • Release Web January 2023 (huge amount of small improvements)
      • Release Web October 2022 (Integration with AgGateway protocols, Isoxml support and more)
      • Release Web April 2022 (3D maps and Zoning Tools)
  • 👨‍🌾Agronomy
    • Precision Agronomy Use Cases & Best Practices Overview
    • Field Management Zones (Productivity Zones) Creation Process
    • Variable Rate Seeding (Planting) Maps
    • Yield Calibration & Cleaning
    • Synthetic Yield Map
    • Create Soil Sampling Zones, Points, Route, export as KML, and execute
    • Evaluate Accuracy of Seeding Application
    • Evaluate Accuracy of Fertilizer Application
    • Field Trial Analytics
    • Nitrogen Use Efficiency & Uptake
    • Comparing Yield Datasets
    • Compare Soil Scanner Data between Years
    • Flood Detection / Insurance report
    • Profit Maps (COMING)
    • VRA/Rx/Prescription Fertilizer Maps (COMING)
    • VRA/Rx/Prescription Nitrogen Maps (COMING)
    • VRA/Rx/Prescription Spraying Maps (COMING)
    • Multi-Layer Field Potential Maps (COMING)
    • VR Lime Application Based on Soil Scanner pH Data (COMING)
    • Merging Yield Datasets Belonging to the Same Field (COMING)
  • 📱Product Tour - Mobile App
    • Installation
    • Logging in
    • Viewing satellite images
    • Viewing zones maps
    • Viewing soil data
    • Viewing yield data
    • Viewing topography maps
    • Viewing as applied datasets
    • Working in the field/Scouting zones maps
    • Working offline
    • Filters
    • Options
    • Settings
  • 🤖API Docs
    • GeoPard API Overview
    • Getting Started
    • Authorization: ApiKey, Credentials or OAuth 2.0
    • Diagrams with Basic Flows
      • 1. Field Registration
      • 2. GraphQL Subscription
      • 3. Grep Satellite Imagery
      • 4. Upload Soil | AsApplied | Yield Datasets
      • 5. Execute Equations
      • 6. Generate ZonesMap
      • 7. Download Gridded Data
      • 8. Download Original Data
    • Data Schema
    • Requests Overview
      • 1. Subscription: Get Events
      • 2. Query: Get "Fields"
      • 3. Query: Get "SatelliteImages"
      • 4. Query: Get defined "SatelliteImage"
      • 5. Query: Get "RasterMaps"
      • 6. Query: Get "ZonesMaps"
      • 7. Mutation: Generate "ZonesMap"
      • 8. Mutation: Generate "RasterMap"
      • 9. Mutation: Generate "ZonesMap" asynchronously
      • 10. Mutation: Generate "RasterMap" asynchronously
      • 11. Mutation: Generate Yield based "ZonesMap" asynchronously
      • 12. Mutation: Generate Soil based "ZonesMap" asynchronously
      • 13. Mutation: Create "Farm"
      • 14. Mutation: Create a "Field" or edit the boundary of the existing field (with optional labels)
      • 15. Query: Get "TopographyMap"
      • 16. Query: Get "YieldDatasets"
      • 17. Query: Get "SoilDatasets"
      • 18. Mutation: Generate zip archive with "ZonesMap" and "Field"
      • 19. Mutation: Delete "Field"
      • 20. Mutation: Delete "Farm"
      • 21. Mutation: Delete "ZonesMap"
      • 22. Mutation: Delete "RasterAnalytisMap"
      • 23. Mutation: Delete "SoilDataset"
      • 24. Mutation: Delete "YieldDataset"
      • Notes (Pins)
        • 25. Mutation: Save "Note" attached to "Field"
        • 26. Mutation: Save "Note" attached to "ZonesMap"
        • 27. Mutation: Save "Note" attached to "SoilDataset"
        • 28. Mutation: Save multiple "Notes", Batch operation
        • 29. Mutation: Delete "Note"
        • 30. Mutation: Delete multiple "Notes"
        • 31. Query: Get all "Notes" related to "Field"
        • 32. Query: Get "Notes" related to "ZonesMap" and type
        • 33. Query: Get "Notes" related to "SoilDataset" and type
        • 34. Query: Get a selected "Note" with all "Comments"
        • 35. Mutation: Add "Comment" to the selected "Note"
        • 36. Mutation: Add multiple "Comments" to the selected "Notes"
      • 37. Query: Get "SatelliteImages" in the defined interval
      • 38. Query: Get "UserData"
      • 39. Mutation: Set custom color schemas to selected "GeoMaps"
      • 40. Query: Get "Labels" on the account level
      • 41. Mutation: Save "Labels" on the account level
      • 42. Mutation: Delete "Label" on the account level
      • 43. Query Get "Fields"
      • 44. Mutation: Set Field Labels
      • 45. Mutation: Save User Data
      • 46. Mutation: Generate multi-layer "ZonesMap" asynchronously
      • 47. Query: Get "ZonesMaps"
      • 48. Query: Get Gridded Data from "TopographyMap"
      • 49. Query: Get Gridded Data from "FieldSatelliteImage"
      • 50. Query: Get Gridded Data from "VectorAnalysisMap"
      • 51. Query: Get Gridded Data from "YieldDataset"
      • 52. Query: Get Gridded Data from "SoilDataset"
      • 53. Query: Get Gridded Data from "AsAppliedDataset"
      • 54. Query: Get Vector Data from "SoilDataset"
      • 55. Upload zip files (over 6 MB)
      • 56. Upload photos
      • 57. Query: Get "Photos" attached to the selected "Note"
      • 58. Query: Get "Photos" attached to "Comments"
      • 59. Query: Get "AsAppliedDatasets"
      • 60. Mutation: Generate As-Applied-based "ZonesMap" asynchronously
      • 61. Mutation: Delete "AsAppliedDataset"
      • 62. Mutation: Share Farms
      • 63. Mutation: Save Organization
      • 64. Mutation: Add Users to Organization
      • 65. Mutation: Delete Users from Organization
      • 66. Mutation: Save Field
      • 67. Mutation: Save Farm
      • 68. Mutation: Refresh "VectorAnalysisMap" Statistics
      • 69. Mutation: Delete "Photo"
      • 70. Mutation: Delete multiple "Photos"
      • 71. Mutation: Generate a zip archive with "Notes"
      • 72. Query: Get Gridded Data as GeoJSON or GeoTIFF
      • 73. Query: Get Gridded Data with the Selected Buffer
      • 74. Mutation: Verify "Equation"
      • 75. Mutation: Generate "EquationMap" asynchronously
      • 76. Query: Get "EquationMap"
      • 77. Mutation: Delete "EquationMap"
      • 78. Query: Find "Fields" by "externalKey"
      • 79. Query: Find "Farms" by "externalKey"
      • 80. Query: Get Original Data
      • 81. Query: Get GeoJSON of "EquationMap"
      • 82. Query: Restore Subscription Events
      • 83. Query: Collect Platform Context
      • 84. Mutation: Calibrate and Clean YieldDataset
      • 85. Mutation: Assign Rates to VectorAnalysisMap (ZonesMap)
      • 86. Query: Get "Farms"
      • 87. Mutation: Save Custom VectorAnalysisMap (ZonesMap)
      • 88. Mutation: Export ZonesMap as Zipped Shapefile
      • 89. Mutation: Export ZonesMap as Zipped ISOXML
    • Geo Endpoints
      • WMS - Get Raster Pictures of Spatial Data Layers
        • 1. LAI
        • 2. RGB
        • 3. Field: boundary
        • 4. Field: thumbnail
        • 5. ZonesMap
        • 6. ZonesMap: custom color schema
        • 7. ZonesMap: thumbnail
        • 8. RasterMap
        • 9. RasterMap: custom color schema
        • 10. RasterMap: thumbnail
        • 11. TopographyMap: elevation in absolute numbers
        • 12. YieldDatasetsMap
        • 13. SoilDatasetsMap
        • 14. SoilDatasetsMap: custom color schema
        • 15. AsAppliedDatasetsMap
        • 16. Satellite Image: cropped by Field boundary
        • 17. Satellite Image: cropped by Field boundary and custom color schema
        • 18. YieldDatasetsMap: custom color schema
        • 19. Satellite Image: 10 colors visualization
      • WFS - Get Spatial Data Layers in Vector format (shp, geojson)
        • 1. Get the Field Boundary as Geojson
        • 2. Get the Zones map as Geojson
        • 3. Get Zones Attributes as JSON
        • 4. Get Soil data as Geojson
        • 5. Get Yield data as Geojson
    • Uploading Files
    • API FAQ
  • 🛣️Platform Roadmap
    • Roadmap
  • GIS quick Hints
    • QGIS: Change String to Number values in the shapefile
    • QGIS: Yield Data Manipulations
    • QGIS: Split Boundaries Into Subfields
    • QGIS: Merge Vector Layers
    • QGIS: Merge Selected Features from Vector Files
    • QGIS: Calculate NDVI for the Drone Geotiff File
    • QGIS: Split Multi-field Shapefiles
    • QGIS: Convert CSV to SHP
    • QGIS: Reproject Shapefile
  • Policies
    • Your Data Stays Yours, Securely Managed By GeoPard
    • Terms & Conditions
    • Privacy Policy
Powered by GitBook

Start Working with GeoPard

  • Go to GeoPard Website
  • Demo Request
  • LinkedIn

This Portal Sections

  • Introduction
  • Product Tour - Web App (incl. video)
  • Product Tour - Mobile App (incl. video)
  • Precision Agronomy Use Cases

Powered by GeoPard Agriculture - Automated precisionAg platform

On this page

Was this helpful?

  1. Product Tour - Web App
  2. Satellite Monitoring

Indices for Crops and Soils

GeoPard can calculate Vegetation/ Moisture/ Chlorophyll, Soil Brightness, and other indices

List of Indices based on Satellite Imagery

All satellite imagery is corrected and optimized for viewing and analysis. Selecting different imagery views allows you to understand your field better by recognizing well-developed areas and areas with crop emergence anomalies. These views include:

  1. RGB – Natural or true color (red, green, and blue).

  2. NIR – (default) a combination of non-visible (near-infrared) and visible (red, green) parts of the spectrum. It increases the interpretability of the data: vegetation is in shades of red with highly vegetated areas in bright red, and soil ranges from dark to light green or grey.

  3. EVI2 – Enhanced Vegetation Index (from 0 to 2.4), is preferable to NDVI for fields with high canopy density where NDVI may saturate. This view can be used to analyze crops in all growth stages.

  4. LAI – Leaf Area Index (from 0 to 5.86), a dimensionless quantity that characterizes plant canopies. Distribution from bare soil to dense canopy. It ranges from the bare ground (index value 0) to the dense canopy (index value is 3.5 and higher for the peak of the growing season) and is shown in red to green colors.

  5. NDVI – Normalized Difference Vegetation Index (from 0 to 1) is a good crop health indication, representing green vegetation distribution. Though there are limitations when using this view at the start of the growing season (influenced by soil) and at the peak of vegetation (saturation). It is shown from red to green on the map.

  6. GNDVI – Green Normalized Difference Vegetation Index (from 0 to 1). It is more sensitive to chlorophyll difference than NDVI and recommended for crops in early to mid-growth stages. Distribution is from red to green on the map.

  7. IPVI – Infrared Percentage Vegetation Index (from 0 to 1). This index is functionally the same as NDVI, but it is computationally faster.

  8. GCI – Green Chlorophyll Index (from 0 to 7). This index is used to evaluate leaf chlorophyll content and is applicable to a wide range of plant species. It helps to measure plant health as chlorophyll content decreases in stressed plants. Distribution is from light green to dark green on the map.

  9. SAVI – Soil Adjusted Vegetation Index (from 0 to 1.5) minimizes the influences of soil brightness. Most useful at the beginning of the season when plants are separated or in rows and when the soil is clearly visible and to the mid-growth stage when plants are still not touching.

  10. OSAVI – Optimized Soil Adjusted Vegetation Index (from 0 to 1). It is best used in areas with relatively sparse vegetation and for crops in early to mid-growth stages. It is shown using a red-to-green legend.

  11. NDWI – Normalized Difference Water Index. It is used to differentiate water from dry land and for water body mapping. It is shown in shades of blue on the map.

  12. WDRVI – Wide Dynamic Range Vegetation Index (from -0.6 to 0.4). This index is used for a more sophisticated analysis of the crop's physiological and phenological characteristics. It uses the same bands as the NDVI but applies an enhanced dynamic range.

  13. SBI – Soil Brightness Index. It is a proxy for soil organic matter, sands, and salinity areas, and is important for studying changes in soil conditions over time. Sandy soils, with their lighter color and coarser texture, reflect more light, thus scoring higher on the Soil Brightness Index. Conversely, clayey soils, richer in organic matter and moisture, appear darker and score lower.

  14. NDMI – Normalized Difference Moisture Index. Normalized Difference Moisture Index is used to determine vegetation water content. It is ideal for finding water stress in plants. Better vegetation has higher values. Lower moisture index values suggest plants are under stress from insufficient moisture. Interpretation:

    • (-1; -0.8) Bare soil;

    • (-0.8; -0.2) Almost no or very low canopy cover;

    • (-0.2; 0) Low canopy cover with high water stress OR very low canopy cover with low water stress;

    • (0; 0.2) Average canopy cover with high water stress OR low canopy cover with low water stress;

    • (0.2; 0.4) High canopy cover with high water stress OR average canopy cover with low water stress;

    • (0.4; 1) High and very high canopy cover with no water stress.

  15. MSI – Moisture Stress Index. Moisture Stress Index is used for canopy stress analysis, productivity prediction, and biophysical modeling. Higher values of the index indicate greater plant water stress and less soil moisture and water content. The values of this index range from 0 to more than 3 with the common range for green vegetation being 0.2 to 2.

  16. CCCI – Canopy Chlorophyll Content Index. The Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index that is proposed for inferring crop N status. The CCCI uses reflectances in the near-infrared (NIR) and red spectral regions to account for seasonal changes in canopy density, while reflectances in the NIR and far-red regions are used to detect relative changes in canopy chlorophyll, a surrogate for N content.

  17. MCARI – Modified Chlorophyll Absorption Ratio Index is responsive to leaf chlorophyll concentration and ground reflectance. Generally, high MCARI values indicate low leaf chlorophyll content. MCARI shows weakness in predicting low chlorophyll concentrations, especially because the impact of soil signal limits its functionality.

  18. TCARI – Transformed Chlorophyll Absorption Reflectance Index is one of several CARI indices that indicates the relative abundance of chlorophyll. It is affected by the underlying soil reflectance, particularly in vegetation with a low LAI.

  19. MCARI/OSAVI and TCARI/OSAVI are the integrated forms of CARI to have better linearity with chlorophyll content and resistance to leaf area index (LAI). The improvement happens because the red and nir reflectances were replaced by the green, red, and red-edge reflectances. The combinations of indices can be used for canopy chlorophyll content estimation.

PreviousOrder Planet Scope (3m daily) imageryNextCrop Development Index Graph

Last updated 1 year ago

Was this helpful?

🖥️
🛰️