Agricultural GIS, Satellite & Remote Sensing Consultant & Advisor
A farmer can stand at the edge of 800 acres and see one field. A good agronomist may already see five different fields hiding inside it. A satellite can reveal patterns neither person can see with the naked eye.
My Ph.D. is in Earth and Environmental Science, so I am comfortable getting technical about reflectance, spectral bands, spatial resolution, thermal sensing, GIS, terrain, raster stacks and environmental data.
I am equally interested in the much simpler question the buyer eventually asks: What does this help me see, decide, save, prevent or grow?
I help satellite-imagery companies, GIS and mapping platforms, remote-sensing firms, precision-ag businesses, crop-intelligence companies and other geospatial organizations turn complex capability into understandable value, trusted authority, stronger adoption, qualified demand, visibility and commercial growth.
Remote sensing can examine variability across farms, regions and entire agricultural markets far faster than somebody can physically walk them.
The commercial value does not come from generating more colorful layers. It comes from moving intelligently through observation → interpretation → validation → decision → execution → measurement.
I help technically sophisticated companies explain that value without dumbing down the science or requiring every prospect to become a remote-sensing scientist first.
A Satellite Image Is Not Just a Picture. Every Pixel Is a Measurement.
That is where looking down from space becomes remote sensing.
Human vision works within a relatively narrow portion of the electromagnetic spectrum.
Remote-sensing instruments can measure reflected or emitted energy in other wavelength regions too: visible, near infrared, red edge, shortwave infrared, thermal infrared and much narrower hyperspectral bands.
Vegetation interacts with those wavelengths according to canopy structure, pigments, leaf properties, water status, biomass and other physical characteristics.
Soil, residue, moisture, shadow, atmosphere, viewing geometry and mixed land cover affect the signal too.
If one section of a corn field reflects differently from the surrounding crop, something may be happening there.
It could be water. Nutrition. Disease. Insects. Stand density. Soil texture. Compaction. Drainage. An old field boundary. Or something operational that happened three weeks earlier.
The imagery narrows the question. Good agronomy helps answer it.
Detection
Something differs from surrounding crop, an earlier observation or an expected baseline.
Interpretation
The analyst, model or agronomist develops plausible explanations for the spatial pattern.
Ground Truth
Scouting, soil data, tissue sampling, sensors or direct observation determine what is actually happening.
Ground truth remains stubbornly grounded. Satellites are extraordinary. Agronomy still occasionally requires boots.
There Is No “Best Satellite.” There Is a Better Sensor for the Question You Are Asking.
Spatial resolution, revisit frequency, spectral capability, archive depth, cloud conditions and cost all involve tradeoffs.
Near-daily does not mean every satellite is daily. Different constellations operate at very different cadences. That distinction matters when somebody is paying for time-sensitive crop intelligence.
PlanetScope
CadenceNear-daily land monitoring at comparatively fine spatial resolution.
Planet currently describes PlanetScope monitoring at approximately 3.7-meter pixel size with near-daily capture frequency.
That is particularly useful when the question is not “Can I get one image?” but “Can I follow change through the season?”
Emergence, canopy development, disturbance and recovery become much easier to study when temporal cadence is high.
The differentiator is cadence plus useful field detail.
Explore Planet agriculture monitoring →Copernicus Sentinel-2
Red Edge + Open DataA remarkably useful open multispectral system for vegetation analysis.
Sentinel-2 carries a 13-band multispectral imager spanning visible, near-infrared, red-edge and shortwave-infrared wavelengths.
The bands are provided at 10-, 20- and 60-meter spatial resolution depending on wavelength.
The two-satellite mission design provides a five-day revisit at the equator.
Its multiple red-edge bands make Sentinel-2 particularly useful for agricultural vegetation analysis.
Explore Sentinel-2 →Landsat 8 & 9
History + ThermalLong-term continuity plus multispectral and thermal observations.
Landsat 8 and 9 collect visible, near-infrared and shortwave-infrared data at 30-meter spatial resolution, along with dedicated thermal infrared observations.
Each satellite follows a 16-day repeat cycle, with Landsat 8 and 9 offset by eight days.
Landsat's greatest strategic advantage may be time itself. Sometimes the best explanation for today's strange-looking field is sitting ten years back in the archive.
Explore USGS Landsat →Pixxel Firefly
HyperspectralMuch finer spectral discrimination than conventional multispectral sensing.
Pixxel's operational Firefly constellation offers 135 available hyperspectral bands across roughly 470–900 nanometers at a 5.4-meter ground sample distance.
There is an important nuance: up to 45 bands are selectable for an individual capture.
That is more technically accurate than simply saying every image contains 135 bands.
The differentiator is spectral detail, not simply more pixels.
Explore Pixxel Firefly →Multispectral Imagery Lets Agriculture See Beyond Human Vision Without Asking Every Problem to Become a Hyperspectral Problem
A multispectral sensor measures selected wavelength regions that expose useful differences in vegetation, soil and water.
Visible Bands
Blue, green and red wavelengths provide familiar optical information while supporting classification, vegetation analysis and atmospheric processing.
Near Infrared
Healthy green vegetation typically reflects NIR strongly, creating useful contrast with red absorption and other surface materials.
Red Edge
Red-edge wavelengths sit around the rapid transition between chlorophyll absorption in red and high NIR reflectance.
Shortwave Infrared
SWIR can provide information related to vegetation and soil moisture, residue and other surface characteristics.
Time Series
A single scene is an observation. Repeated scenes begin showing emergence, growth, disturbance and recovery.
Sensor Fusion
Different sources can sometimes be combined or harmonized to improve temporal coverage and use complementary strengths.
Hyperspectral Does Not Mean “Multispectral, But More Expensive”
It is a richer way of sampling spectral behavior. Whether that richness creates value depends entirely on the question.
Narrower Bands
Hyperspectral sensors measure many narrow wavelength regions rather than a relatively small number of broad bands.
Spectral Signatures
Finer spectral sampling can help separate materials or vegetation characteristics whose signals may be blended inside broader bands.
More Complexity
More spectral dimensions also mean more calibration, processing, storage, modeling and interpretation.
A researcher attempting to separate subtle biochemical or material signatures may have an excellent reason to want hyperspectral imagery.
An agronomist deciding which soybean fields deserve attention tomorrow may value cadence, straightforward vegetation metrics and fast workflow integration more.
That gets to the commercial question: what decision actually requires the additional spectral complexity?
Firefly is especially interesting because its current constellation combines 5.4-meter spatial detail with a hyperspectral VNIR bandset.
But the strongest message is not: “Look how many bands we have.”
It is: “Here is what those wavelengths allow you to distinguish that mattered before but was difficult to see.”
Vegetation Indices Are Useful Shortcuts. They Are Not Crystal Balls.
Spectral indices combine selected bands to make particular vegetation differences easier to compare across space and time.
NDVI
(NIR − Red) / (NIR + Red)The Normalized Difference Vegetation Index is one of the most familiar remote-sensing vegetation metrics.
It uses the contrast between red absorption and strong near-infrared reflectance in green vegetation.
NDVI can be useful for broad vegetation condition and temporal change, but it tends to saturate in dense vegetation and can be affected by underlying soil color.
NDRE
(NIR − Red Edge) / (NIR + Red Edge)NDRE substitutes a red-edge wavelength for the red band.
It is frequently useful in denser vegetation where conventional NDVI can become less sensitive.
Exact band centers depend on the sensor.
Chlorophyll Indices
Example: NIR / Red Edge − 1Chlorophyll-related indices use wavelengths sensitive to pigments and canopy characteristics.
There is no single universal “chlorophyll index.” Different formulas and wavelength selections are used for different sensors and purposes.
Validation against crop, growth stage and the management question still matters.
USGS describes NDVI as useful for vegetation greenness and condition, while explicitly noting both dense-vegetation saturation and sensitivity to underlying soil color.
See the USGS NDVI overview .
The Goal Is Not to Color the Field. The Goal Is to Understand Why One Part Behaves Differently.
Vegetation indices, soils, elevation, yield history and other spatial layers can help turn one field into meaningful management zones.
Scouting Zones
Spatial anomalies help prioritize where somebody should look first.
Sampling Zones
Stable differences can help design more targeted soil, tissue or plant sampling.
Management Zones
Multi-layer analysis can divide fields according to recurring productivity or environmental patterns.
Seeding Prescriptions
Validated zones may support variable seeding rates where agronomic logic and equipment capability align.
Fertility Prescriptions
Soil testing, crop response and productivity zones can contribute to variable-rate nutrient strategy.
Irrigation Prescriptions
Soil, topography, weather, canopy temperature and crop response can help refine water management.
Do not turn an NDVI layer directly into a fertilizer prescription because the colors look persuasive. Remote sensing can identify variability. Agronomic evidence still has to explain what that variability means.
UF/IFAS describes the same basic progression in precision agriculture: characterize in-field variability, create management zones and convert validated spatial information into prescription maps for variable-rate equipment.
See UF/IFAS guidance on variable-rate technology and management-zone development .
Sometimes a Plant Gets Hot Before It Gets Visibly Ugly
Thermal sensing gives agriculture a very different view of plant and surface behavior.
Plants cool themselves partly through transpiration.
When water availability becomes limited, stomata may close, transpiration can decline and vegetation temperature may rise.
Thermal infrared observations can help reveal that change.
Surface & Canopy Temperature
Thermal data can identify areas with temperature patterns that differ from surrounding crop.
Water Stress
Reduced evaporative cooling can contribute to thermal signatures associated with vegetation water stress.
Drought Monitoring
Thermal, vegetation and moisture information can contribute to understanding developing agricultural drought.
NASA's ECOSTRESS uses thermal infrared observations to study land-surface temperature, evapotranspiration and vegetation water stress.
Hotter vegetation may reflect water limitation.
It might also relate to soil, rooting, canopy structure, disease, irrigation performance or other field conditions.
Thermal sensing helps decide where the next question should be asked.
Water Leaving the Field Is Data Too
Evapotranspiration connects energy, soil, weather, vegetation and water use into one of the most useful spatial variables in irrigated agriculture.
Evapotranspiration, or ET, combines evaporation from soil and other surfaces with transpiration from vegetation.
That makes ET a very different question from simply asking how much water was pumped or how much rain fell.
Irrigation Scheduling
ET can be compared with rainfall, irrigation and soil-moisture information to understand crop water use.
Water Accounting
Field-scale ET can support farm, basin and regional water-management programs.
Performance Comparison
Spatial ET patterns can help expose meaningful differences among fields or irrigation systems.
OpenET is a good example of remote sensing becoming a decision-oriented product.
It produces daily, monthly and annual satellite-based ET information at approximately 30-meter field scale.
Current OpenET coverage includes 23 western U.S. states as well as the Mississippi Alluvial Plain.
That geographic qualifier matters. A technically excellent product still needs to be marketed according to where the product actually works today.
GIS Is the Table Where All the Other Data Finally Has to Sit Together
Satellite imagery is one layer. Location becomes much more powerful when different layers can explain one another.
Farm & Field Boundaries
Parcels, production fields, blocks, pivots, management zones and ownership boundaries define the geometry of the operation.
Soils
Soil survey, electrical conductivity, organic matter, pH, texture and sampling data can be compared spatially with crop behavior.
Irrigation
Pivots, drip blocks, wells, pumps, pressure, flow and storage gain context when they are mapped together.
Planting & Application
As-planted, as-applied and machine-pass records become historical management layers.
Remote Sensing
Repeated satellite and aerial observations can be compared with the operational layers beneath them.
Yield
Harvest data becomes much more interesting when aligned with soil, terrain, inputs, weather and imagery.
Water Still Runs Downhill, Which Is Why Topography Keeps Sneaking Into the Agronomy Conversation
A field can look flat from the truck and still contain enough elevation variation to change drainage, moisture and crop performance.
When “Close Enough” Is Not Close Enough
Real-Time Kinematic GNSS uses correction information to produce positioning precise enough for many agricultural operations where ordinary navigation is not sufficient.
RTK can support field elevation surveys, drainage design, land leveling, controlled traffic, machine guidance and repeated spatial measurements.
UF/IFAS describes agricultural RTK systems with positioning accuracy around a few centimeters under appropriate conditions.
The important distinction is between owning “an elevation layer” and having elevation information accurate enough for the management decision.
See UF/IFAS guidance on positioning systems in precision agriculture .
A DEM Turns Elevation Into a Surface You Can Analyze
A digital elevation model represents terrain as a continuous elevation surface.
GIS can then derive slope, aspect, flow direction, accumulation and other topographic relationships.
Those layers can be compared with yield, standing water, crop vigor, erosion and soil characteristics.
The Low Spot Usually Knows It Is the Low Spot Before the Spreadsheet Does
Repeated weak-yield areas may align with depressions, flow paths, poor drainage or persistent saturation.
Somewhere else, higher positions may lose water faster and create droughtier conditions.
Topography does not explain every spatial pattern.
It is simply one of the first layers worth asking questions of.
One Bad Harvest Can Lie to You. Five Harvests Start Telling a Story.
Yield maps become more valuable when the question changes from “What happened this year?” to “What keeps happening in the same place?”
A single yield map can reflect weather, disease, harvest conditions, operator behavior, machine calibration, temporary management choices or any number of one-season events.
Align several seasons and recurring patterns begin to stand out.
Stable High Zones
Areas that repeatedly outperform deserve investigation just as much as weak areas. What keeps working there?
Stable Low Zones
Persistent weakness suggests something structural may be limiting performance.
Variable Zones
Areas that swing dramatically from year to year may be unusually sensitive to weather or management.
Cross-Layer Analysis
Stable yield patterns can be compared with soil, elevation, drainage, imagery, planting and input history.
Yield data needs cleaning too. Header width, flow lag, moisture, GPS errors, turns, partial passes and calibration problems can create patterns that look agronomic but are really mechanical.
The Map Is Not the Finish Line. It Is Somewhere Near the Beginning.
Spatial intelligence creates value when observation becomes an operating loop.
Acquire
Satellite, aerial, machine, weather, sensor and historical data enter the workflow.
Correct
Georeferencing, cloud masking, atmospheric processing, calibration and quality control prepare data for comparison.
Analyze
Indices, classification, thermal analysis, terrain models and statistics expose patterns.
Compare
Current observations are evaluated against other dates, other zones and historical baselines.
Validate
Scouts, agronomists, soil tests, sensors and field knowledge determine what the signal means.
Zone
Meaningful differences become scouting, sampling or management zones.
Execute
Prescriptions, equipment, irrigation changes or crews carry the decision into the field.
Measure
Follow-up imagery, yield, cost and field results reveal whether it worked.
Some Companies Build the Eyes. Others Build the Workbench.
Satellite data has to be stored, processed, analyzed, combined and delivered somewhere.
Esri ArcGIS
ArcGIS is powerful because imagery can live beside field boundaries, soils, irrigation, yield, costs, mobile field collection and real-time data streams.
That makes GIS less of a mapmaking exercise and more of an operating environment for spatial decisions.
Explore Esri agriculture GIS →Google Earth Engine
Earth Engine combines a massive geospatial data catalog with cloud-scale processing.
That becomes especially useful when the problem expands from one scene to thousands, from one field to entire regions, or from one season to decades of imagery.
Explore Google Earth Engine →Agricultural Decision Platforms
Most growers should not need to know what a raster stack is to benefit from one.
The strongest agricultural interface may simply say: “These three zones changed. Scout them first.”
Hiding unnecessary complexity is not dumbing down the science. It is product design.
The grower usually does not want a GIS. The grower wants to know which 37 acres deserve attention before sending somebody across 4,000.
Satellite Data Is Powerful. It Is Also Quite Capable of Being Confidently Misused.
Technical credibility increases when a company explains limitations as clearly as capabilities.
Clouds
Optical satellites need a usable view of the surface. High revisit frequency does not guarantee a cloud-free observation.
Mixed Pixels
One pixel may contain crop, soil, road, shadow, water or several canopy conditions.
Atmosphere
Aerosols, haze, water vapor and illumination affect the observed signal.
Sensor Differences
Sensors differ in spectral response, calibration, resolution, acquisition geometry and timing.
Index Saturation
Familiar vegetation indices have known limits. Familiar is not the same thing as universal.
False Precision
A smooth map can suggest a level of certainty the source data does not support.
Wrong Timing
The perfect sensor at the wrong crop stage may be less useful than a simpler observation at the right time.
Weak Ground Truth
A model trained against poor reference data can convert uncertainty into confident-looking output.
Bad Economics
A technically detectable difference is not automatically economically worth treating.
The Person Looking at the Map May Not Be the Person Buying the Platform
Agricultural spatial products often have complicated buying groups. The same technology has to make sense to very different people.
Growers
Want useful decisions, reasonable cost and less time wasted chasing noise.
Agronomists
Want trustworthy signals, context and tools that strengthen professional judgment.
Ag Retailers
May use spatial intelligence to improve scouting, input recommendations and customer service.
Cooperatives
Need systems that can work across many growers, fields and agronomists.
Seed & Input Companies
Use imagery for trials, monitoring, product development and field support.
Insurers & Lenders
Spatial data can contribute to acreage, risk, damage and portfolio understanding.
Water Managers
ET, thermal information and irrigation layers extend the value well beyond crop-vigor mapping.
Governments & NGOs
Crop mapping, food security, disaster assessment and large-area monitoring create another scale entirely.
A grower may ask, “Will this save me a trip across the field?” A global agribusiness may ask, “Can this work across four million acres?” Same underlying technology. Very different commercial conversation.
A Spatial-Intelligence Company Should Be Easier to Understand Than the Data It Processes
Too many geospatial companies explain themselves as though every prospect already has a graduate degree in remote sensing.
Sometimes the reader is another scientist.
Sometimes it is a chief agronomist, grower, product manager, water manager, insurer, CIO, investor or procurement executive.
Technical credibility should not require unnecessary opacity.
company → constellation → sensor → data product → wavelength → spatial resolution → temporal resolution → index → crop → use case → integration → decision → outcome
Explain the Sensor
Bands, resolution, revisit, swath, processing level, archive and limitations.
Explain the Use Case
Water stress, vigor, disease scouting, crop classification, yield, boundaries or the actual customer problem.
Explain the Workflow
API, GIS, dashboard, alert, agronomist review, mobile scouting, prescription or machine execution.
Explain the Difference
Why this sensor, dataset, model or workflow deserves consideration beside established alternatives.
Publish Methodology
Technical audiences respect enough transparency to understand how conclusions are produced.
Publish Evidence
Field validation, peer-reviewed research, trials, academic partnerships and known limitations create real authority.
That connects naturally with my AI Search & Organic Growth work and Marketing Analytics & Reporting .
Florida Gives Remote Sensing Plenty to Look At— and Clouds Plenty of Opportunities to Get in the Way
I am based in DeLand, Florida. Citrus, specialty crops, water, heat, storms and extremely variable soils create a useful proving ground for spatial agriculture.
Citrus
Tree size, canopy condition, yield, irrigation, soil and disease pressure create strong spatial patterns.
Water
Sandy soils, drainage, irrigation demand and nutrient movement make water inseparable from spatial management.
Heat
Thermal information becomes particularly interesting where crop temperature and water availability interact strongly.
Storms
Hurricanes, flooding and wind can create abrupt change across very large areas.
Specialty Crops
Vegetables, strawberries, nurseries, sod and other high-value crops create different scale requirements from broadacre grain.
Cloud Cover
Florida is a useful reminder that satellite revisit and usable optical imagery are two different things.
UF/IFAS precision-ag guidance shows exactly why spatial layers become more powerful together: yield, soil, aerial imagery, satellite information and management zones can all contribute to better site-specific decisions.
UF/IFAS also documents GPS-based citrus yield mapping that identifies high- and low-production areas within a grove.
See UF/IFAS precision agriculture for Florida citrus and UF/IFAS variable-rate technology guidance .
For the broader regional market, see Florida Agricultural Marketing .
Florida gives the work a useful local reference point. It does not define the boundaries of the market.
Space Is Global. Agricultural Decisions Are Still Local.
Earth observation scales across borders beautifully. Agricultural interpretation requires much more respect for local context.
Field Size
A 30-meter pixel behaves differently in a 5,000-acre operation than among fragmented smallholder fields.
Crop Type
Rice, wheat, maize, sugarcane, citrus, vineyards, coffee and horticultural crops create different signals.
Cloud Climate
Tropical cloud cover changes the usefulness of an optical-only monitoring strategy.
Ground Reference
Models developed in one crop, geography or management system may require validation elsewhere.
Connectivity
An elegant cloud platform still has to work for users with uneven rural connectivity.
Economics
A technically useful insight can still fail commercially if acting on it costs more than the value created.
The Product Does Not Need Less Science. It Needs a Better Bridge Between Science and Value.
I help geospatial and agricultural-technology companies build that bridge across positioning, product communication, search, authority and go-to-market strategy.
Market Positioning
Define which crops, problems, geographies and buyers fit the technology best.
Technical Translation
Turn wavelengths, rasters, algorithms, models and APIs into understandable value.
Product Architecture
Organize datasets, analytics, subscriptions, use cases and integrations around buyer decisions.
Demand Generation
Reach growers, agronomists, retailers, enterprises and institutions with the right use-case story.
SEO & AI Search
Build visibility around sensors, indices, crops, applications and spatial problems.
Authority Development
Turn scientists, agronomists, research and technical expertise into attributable authority.
Enterprise GTM
Align product, sales, pilots, evidence, procurement and stakeholder messaging.
Partner Ecosystems
Satellite, GIS, agronomy, machinery and farm-software companies often create more value together.
Executive Strategy
Provide senior judgment when technology, product, marketing and commercialization overlap.
From Satellite Constellations to the Startup Turning a GeoTIFF Into Something a Farmer Will Actually Pay For
I am comfortable with both sides of that spectrum and with the technical, executive and commercial people working between them.
Earth-Observation Companies
Constellation operators, imagery providers and geospatial-data businesses.
Precision-Ag SaaS
Platforms combining imagery, crop history, fields and agronomy into usable workflows.
GIS Companies
Enterprise GIS, mapping, spatial analytics and imagery-management businesses.
Hyperspectral Companies
Deep-technology businesses commercializing new sensing and analytical capabilities.
Water Intelligence
ET, thermal, irrigation and water-accounting platforms.
Enterprise Agriculture
Seed, crop-input, insurance, finance, food and agribusiness organizations deploying spatial intelligence at scale.
I Can Talk With the Scientist, the Sales Team and the Person Who Just Wants to Know What the Map Means
Technical companies often live between people who enjoy complexity and people who are paying to have complexity removed.
Consultant
I can diagnose positioning, commercialization, product architecture, website, search or demand-generation problems and help build the strategy.
Advisor
I can work directly with founders, executives and leadership as an outside perspective across product, market and growth decisions.
Fractional CMO
I can provide ongoing senior marketing and growth leadership when the organization needs more structure, prioritization and accountability.
Ideator & Strategic Partner
Sometimes the opportunity appears when somebody finally connects three datasets, two markets and one customer problem that had been sitting separately.
My Earth and Environmental Science background helps me participate comfortably in technical conversations without pretending technical complexity itself is the product.
I am comfortable working with scientists, remote-sensing specialists, GIS professionals, agronomists, developers, product teams, founders, executives, sales teams, channel partners and boards.
The role can be project-based consulting, retained advisory, strategy, ideation or fractional CMO and executive leadership .
Measure Adoption and Decisions, Not Just Acres Processed
A platform can process billions of pixels and still have a commercial problem.
| Metric | What It Shows | Why It Matters |
|---|---|---|
| Acres / Hectares Under Monitoring | Scale of deployed coverage. | Useful when connected to active customers and actual use. |
| Active Fields | How much licensed coverage is actually being used. | Separates contracts from adoption. |
| Alert Engagement | Whether users investigate detected changes. | Tests relevance of the signal. |
| Scout Conversion | How often imagery results in field investigation. | Connects remote sensing with operations. |
| Validated Detection Rate | How often flagged areas correspond with meaningful field conditions. | Central to product trust. |
| Prescription Adoption | Whether management zones reach machine execution. | Moves analytics into action. |
| Time Saved | Whether scouting and analytical workflows become faster. | One of the easiest ROI stories to understand. |
| Renewal / Expansion | Whether customers keep and expand the product. | Evidence that the information is worth paying for. |
| Organic Visibility | Whether qualified buyers find the business around real spatial-ag questions. | Measures discoverability and technical authority. |
| AI Search Visibility | Whether generative systems understand the company and product accurately. | Increasingly important in technical research. |
Spatial Intelligence Is One Layer in a Much Larger Agricultural System
Agricultural GIS, Satellite & Remote Sensing Consulting FAQs
What does an agricultural GIS and remote sensing consultant do?
What is remote sensing in agriculture?
What is the difference between GIS and remote sensing?
What is the difference between multispectral and hyperspectral imagery?
Does Pixxel Firefly capture 135 bands in every image?
What is NDVI?
What are the limitations of NDVI?
What is NDRE?
What is a chlorophyll index?
Can satellite imagery detect crop disease?
Can thermal infrared imagery detect crop water stress?
What is evapotranspiration and why does it matter?
Where is OpenET currently available?
Which satellite is best for precision agriculture?
Can vegetation indices become variable-rate prescription maps?
What is RTK-GNSS used for in agriculture?
What is a digital elevation model or DEM?
Why stack multiple years of yield maps?
Can you help satellite and GIS companies explain highly technical products?
Can SEO and AI search help a precision-ag or geospatial company grow?
Do you work only with Florida agricultural companies?
Do you provide agronomic prescriptions, surveying or engineering advice?
You Can See the Entire Field From Space. The Hard Part Is Still Knowing What Matters.
Maybe your satellite sees something competing systems cannot. Maybe the GIS platform is powerful and surprisingly difficult to explain. Maybe the hyperspectral science is excellent and buyers are still wondering why they need the additional bands.
Maybe customers generate maps but do not act on them. Maybe agronomists understand the technology and growers do not yet see the economics. Maybe the product has plenty of data and not enough narrative connecting the data to the decision.
Or maybe you need somebody who can move comfortably among scientists, technology teams, executives, salespeople and agricultural customers and help connect those layers into one clearer growth strategy.
I can work with you as a consultant, advisor, fractional CMO, strategic partner, ideator or the experienced outside person who helps determine which signal actually matters.