Satellite Imagery & Remote Sensing for Precision Agriculture
Monitor farmland, crop growth, and soil conditions from space. Detect pest and drought stress early, track planting area and yield potential, and get AI-driven insights that guide irrigation, fertilization, and harvest timing.
XRTech Group delivers satellite imagery for agriculture operators and government agencies worldwide on a single platform, one source of optical, multispectral, and radar data spanning pre-plow land assessment, in-season growth management, and harvest services, from first survey through yield estimation.
What is satellite imagery for agriculture?
Satellite agriculture imagery combines multi-spectral Earth observation data with AI analytics to monitor farmland across the complete growing cycle, from pre-plow land and soil assessment to in-season crop growth, pest and weather risk, and harvest yield estimation, replacing scattered field surveys with continuous, field-scale visibility for farmers, agronomists, and government agencies.
Use Cases
Overview of Use Cases
Satellite remote sensing applies across the whole farming process — pre-plow monitoring, growth management, harvest services, and digital agriculture — with large coverage, rich spectral bands, and no geographic restriction.
Seven Ways Satellite Intelligence Supports the Full Growing Season
From pre-plow land assessment to harvest yield estimation, satellite data and AI analytics support every stage of the farming cycle.
AI plot extraction delineates individual farmland boundaries directly from satellite imagery, ahead of the planting season.
01 — SOLUTION
Pre-Plow Land & Farmland Boundary Assessment
Before the first pass of a plow, high-resolution classification and AI plot extraction establish a baseline of land quality and field boundaries.
Land Resources & Cultivated Land Quality: Remote sensing classification and spatial analysis monitor land-use type and evaluate cultivated land quality, supporting quantitative assessment of production potential and production-guidance decisions.
AI Farmland Plot Identification: A self-developed AI plot-extraction algorithm delineates farmland plot boundaries from medium- and high-resolution imagery, with boundary accuracy better than 2 pixels and plot identification accuracy above 90%.
Non-Agricultural Land Screening: The same plot data supports government monitoring of farmland converted to non-agricultural use before the planting season begins.
02 — SOLUTION
Crop Growth & Planting Area Monitoring
Once crops emerge, time-series satellite imagery tracks what was planted, where, and how it is growing, at field scale across an entire region.
Planting Area & Crop Type Mapping: Crop phenology analysis and time-series classification identify the spatial distribution of major plantings, with classification accuracy better than 93% for staple food crops and 85% for cash crops.
Non-Grain Land Conversion Detection: AI change detection flags farmland converted to forestland, fish ponds, vegetable greenhouses, or abandoned land, providing data support for regulatory oversight.
Real-Time Growth & Seedling Condition: A crop-growth parameter set built from medium- and high-resolution imagery macro-estimates seedling condition, growth stage, and distribution for production managers.
Daily-to-Weekly Revisit Options: Micro-satellite constellations add daily, weekly, or monthly imagery on top of the core fleet for continuous crop-health tracking through the growing season.
03 — SOLUTION
Soil Moisture & Fertility Management
Spectral analysis of the soil surface itself, not just the crop canopy, guides irrigation and fertilization decisions before stress becomes visible.
Soil Moisture Monitoring: A soil-moisture inversion model built from spectral reflectance differences under varying water content tracks field-scale moisture, accurate to within 85% of ground measurements.
Soil Fertility & Nutrient Mapping: Spectral reflectance data is analyzed for physical and chemical soil properties to support fertilization planning, reaching over 90% accuracy on dry land and around 80% on paddy fields.
All-Weather Passive Microwave Sensing: Passive microwave and SAR-derived soil water, vegetation water content, and biomass data are unaffected by cloud cover, filling gaps optical-only monitoring leaves during monsoon or overcast stretches.
04 — SOLUTION
Pest, Disease & Meteorological Risk Monitoring
Spectral stress signatures and meteorological data combine to flag crop health risk before an outbreak or weather event causes irreversible loss.
Pest & Disease Risk Detection: A disease-diagnosis model combining optical greenness, SAR backscatter, temperature, irrigation, and planting-density data identifies disease presence and severity, exceeding 90% accuracy in tested scenarios.
Meteorological Disaster Early Warning: Multi-source meteorological satellite and observation data feed a disaster-risk analysis model, giving early warning of drought, flood, and other weather hazards to reduce crop loss.
05 — SOLUTION
Crop Maturity & Yield Estimation
As harvest approaches, satellite data shifts from monitoring growth to forecasting exactly how much a field will produce and when to bring in the crop.
Crop Maturity Monitoring: Comparing current accumulated temperature at a key phenological stage against the constant accumulated temperature a crop needs to mature determines harvest readiness.
County-Level Yield Estimation: Spectral inversion of crop growth indicators such as LAI and biomass, combined with a yield model and ground-measured data, delivers per-unit and total production figures with county-level accuracy better than 85%.
06 — SOLUTION
Digital Agriculture & Aerospace-Aviation-Ground Sensing
Satellite, aerial, and ground sensors combine into one intelligent monitoring network built for large-scale agricultural digitalization.
Integrated Communications & Remote Sensing: Communications, navigation, and remote-sensing satellites are integrated into a single sensing layer for agricultural monitoring at any scale.
Deep Learning Analysis: Deep-learning algorithms process aerospace, aviation, and ground sensor data together to intelligently analyze crop and environmental conditions.
07 — SOLUTION
Precision Input & Variable-Rate Prescription Zones
Multi-season satellite archives turn a uniform field into defined productivity zones, so every input dollar goes where it earns a return.
Smart Farming Zone Definition: Historical multi-season satellite archives are analyzed to divide fields into productivity zones based on soil and yield trends.
Variable-Rate Prescription Maps: Zone data generates machinery-ready prescription maps for variable-rate seeding and fertilization, cutting input costs in low-yielding zones without cutting yield.
Individual Plant & Tree Counting: AI object detection counts individual trees or plants in orchards and vineyards to assess stand density and flag replanting needs.
NDRE chlorophyll analysis, vegetation and forestry monitoring
Wide-Swath Regional
GF-6 (800km swath), GF-1, GF-4
2m – 50m
Regional planting-area surveys, non-grain land monitoring
Infrared & Multispectral Scanning
CBERS-04 / 04A (IRS, WFI)
Medium resolution
Water resource surveys, long-term yield estimation
Vegetation & Soil Indices
Key Agriculture Vegetation Indices
Different band combinations from the same imagery isolate different crop and soil signals.
Index
Full Name
What It Measures
NDVI
Normalized Difference Vegetation Index
Overall plant vigor and biomass from red / near-infrared reflectance
NDRE
Normalized Difference Red Edge
Chlorophyll content in dense canopies where NDVI saturates
GNDVI
Green Normalized Difference Vegetation Index
Chlorophyll concentration and nitrogen status
SAVI / OSAVI
Soil-Adjusted Vegetation Index
Vegetation vigor with reduced soil-background noise on sparse canopies
LAI
Leaf Area Index
Canopy density and growth stage
CWSI
Crop Water Stress Index
Irrigation timing and water-stress severity
NNI
Nitrogen Nutrition Index
Nitrogen sufficiency for fertilizer prescriptions
CCCI
Canopy Chlorophyll Content Index
Early-season nitrogen and chlorophyll variability
Deliverables
The Outputs We Deliver
Every engagement includes raw data layers, a decision-ready report, and prescription-ready maps, not just imagery.
Original Data Layers
Raw GEOJSON, KML/KMZ, or SHP datasets delivered ready to upload into ArcGIS, QGIS, or open directly in Google Earth Pro.
Decision-Ready PDF Report
A summarized report of crop condition, risk flags, and yield outlook for your fields, ready to share with agronomists or lenders.
Variable-Rate Prescription Maps
Machinery-ready seeding and fertilization prescription maps built from productivity-zone analysis, ready to load directly into your equipment.
Pricing & Delivery
Data Delivery & Pricing
Transparent per-km² pricing for archive and new-tasking imagery, delivered in the GIS format your team already works in.
Standard Archive
Existing imagery, 90+ days old — ready to download within minutes
Super High Resolution (25-30cm)$20/km²
Very High Resolution (31-50cm)$13/km²
High Resolution (51-80cm)$5/km²
Wide Area (2m)$1/km²
Minimum order area 25km² per scene.
New Satellite Tasking
Fresh capture of your exact location and date range
Super High Resolution (25-30cm)$30/km²
Very High Resolution (31-50cm)$20/km²
High Resolution (51-80cm)$8/km²
Wide Area (2m)$2/km²
Minimum order area 100km² per scene. Priority tasking available for time-critical growth stages.
Case Examples
Satellite Monitoring in Practice
Yield Forecasting
Tracking China's Wheat Belt From Germination to Harvest
Multi-temporal imagery and AI growth models followed a wheat-growing region through a full season, flagging early drought stress well before it was visible on the ground and sharpening the pre-harvest yield forecast.
Irrigation Efficiency
Daily Soil Moisture Mapping Across Henan Province
Field-scale soil moisture maps updated daily triggered automated irrigation alerts across a major growing region, cutting water use while keeping yield-limiting dry spells from going unnoticed.
Insurance & Subsidy Validation
Independent Crop-Health Verification for Insurance Claims
Satellite-derived crop health and damage assessment gave insurers and subsidy programs an independent field record, used across programs in India and Nigeria to validate disaster claims without a ground visit to every plot.
Why Satellite Monitoring
Application Benefits
Increase Yield
Field-scale monitoring catches stress and disease early, before it costs bushels.
Cut Input Costs
Variable-rate maps target water, fertilizer, and pesticide only where the data says it is needed.
Protect Crop Health
Early pest, disease, and weather warnings help prevent losses before they spread.
Conserve Water
Field-scale soil moisture data supports precise, efficient irrigation scheduling.
Improve Decision-Making
Data and analysis results support faster, more accurate planting and harvest decisions.
Frequently Asked Questions
What satellite data does XRTech Group use for agriculture monitoring?
We combine ultra-high-resolution optical imagery (SuperView Neo-1 at 0.3m, SuperView-2 at 0.42m with a 1+8 band Red Edge sensor) with GF-6's 2m/8m red-edge multispectral data, CBERS-04/04A infrared scanning, and multi-source meteorological satellite data, covering everything from single-field crop stress to province-scale yield and weather risk.
How accurate is satellite-based crop type and planting area monitoring?
Time-series classification identifies staple food crop plantings with better than 93% accuracy and cash crop plantings with better than 85% accuracy, based on crop phenology analysis applied to multi-source medium- and high-resolution imagery.
Can satellites really measure soil moisture and fertility from space?
Yes. Soil moisture is inverted from the different spectral reflectance of soil at different water content, reaching about 85% accuracy against field measurements. Soil fertility is assessed from spectral reflectance of physical and chemical soil properties, with over 90% accuracy on dry land and around 80% on paddy fields.
How does satellite monitoring detect crop pests and diseases before an outbreak spreads?
A disease-diagnosis model combines optical greenness, SAR backscatter, temperature, irrigation conditions, and planting density to identify disease presence and severity, exceeding 90% accuracy in tested scenarios, well before symptoms are visible to a ground scout walking the field.
How accurate is satellite-based crop yield estimation?
County-level production measurement accuracy is better than 85%, achieved by inverting crop growth indicators like LAI and biomass from spectral data, building a yield model, and combining it with ground-measured data for the region.
What resolution is needed to identify farmland plot boundaries?
Medium- and high-resolution imagery processed through an AI plot-extraction algorithm delivers boundary accuracy better than 2 pixels and plot identification accuracy above 90%, enough to support agricultural production, non-agricultural conversion, and non-grain land monitoring.
How is agriculture satellite imagery delivered and priced?
Imagery is delivered through a cloud platform in GeoTIFF, SHP, DWG, or UTM format. Standard archive imagery starts at $1/km² for 2m wide-area resolution up to $20/km² for 25-30cm, while new tasking for a fresh capture runs from $2/km² up to $30/km², with priority tasking available for time-critical growth stages.
What outputs are included with an agriculture satellite monitoring report?
Each engagement includes original GEOJSON, KML/KMZ, or SHP data layers ready to upload into ArcGIS, QGIS, or Google Earth Pro, a decision-ready PDF report summarizing crop condition and risk flags, and machinery-ready variable-rate prescription maps for seeding and fertilization.
Ready to monitor your fields from orbit?
Search our live archive or request new tasking over your farmland, optical, multispectral, or radar.
