Google Earth Engine Crop Monitoring and Drought Assessment Training Course

Duration 5 days 33 contact hours
Level Foundation to Intermediate Certificate on completion
Who attends Agricultural Monitoring Officers, Food Security Analysts and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 7 Nov 2026 Course code SD-AA-017

Before you join: Participants should be comfortable using a web browser, managing files and folders, and working with basic tables in Excel or Google Sheets.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Agriculture programmes, commercial farms, insurers and food-security teams need timely evidence of where crops are stressed, how conditions compare with previous seasons, and which locations require field verification or response. Manual image downloads and spreadsheet-based monitoring are too slow for large districts or repeated assessments. This course equips participants to use Google Earth Engine to analyse satellite imagery and rainfall data at scale, producing defensible crop-condition and drought evidence for operational planning, early warning and reporting.

Participants learn the Google Earth Engine Code Editor, JavaScript-based image processing workflows, image collections, geometries, filtering, masking and reduction methods. They calculate and interpret NDVI, EVI and NDWI; create seasonal vegetation composites; analyse rainfall anomalies using CHIRPS data; and build drought indicators that compare current conditions with historical baselines. The course also covers cropland masking, zonal statistics for administrative units or farm blocks, charting time series, and exporting maps and tables for use in reports, dashboards and GIS workflows.

Instruction combines guided demonstrations with daily coding labs using an agriculture and drought-monitoring case area. Participants progressively build a reusable Google Earth Engine crop-monitoring script that imports an area of interest, generates cloud-masked Sentinel-2 vegetation indices, calculates rainfall and vegetation anomalies, summarises results by reporting unit and exports decision-ready outputs. They leave with an annotated script, a drought-assessment map layout, a tabular results export and an implementation plan for applying the workflow to their own geography.

The course is designed for professionals who need to interpret and produce geospatial evidence rather than become remote-sensing researchers. It is particularly suited to teams working in agricultural development, food security, climate adaptation, extension services, agribusiness monitoring and humanitarian early warning.

What you will be able to do

  • Configure a Google Earth Engine Code Editor workspace and organise scripts, assets and areas of interest
  • Filter Sentinel-2 image collections by date, location and cloud conditions for crop-monitoring analysis
  • Apply cloud and shadow masking to create analysis-ready optical satellite composites
  • Calculate NDVI, EVI and NDWI layers and interpret their agricultural significance
  • Build dekadal and seasonal vegetation time series for farms, districts or livelihood zones
  • Calculate CHIRPS rainfall totals, anomalies and standardised seasonal departures from a baseline
  • Produce zonal statistics and ranked drought-stress tables for administrative or operational reporting units
  • Export reproducible map layers, charts, CSV tables and an annotated Earth Engine monitoring script

Benefits of attending

For you

  • Build a portfolio-ready Google Earth Engine script for crop-condition and drought monitoring
  • Gain practical confidence interpreting vegetation indices alongside rainfall anomalies and crop calendars
  • Produce district-level crop stress summaries without downloading and processing individual satellite scenes
  • Strengthen credibility when briefing programme managers on satellite-derived agricultural evidence
  • Qualify for GIS, food-security and climate-information assignments requiring Earth Engine workflows

For your organisation

  • Reduce the time required to generate repeatable seasonal crop-condition assessments across multiple districts
  • Standardise vegetation and rainfall anomaly methods used in drought early-warning reporting
  • Improve targeting of field assessments by identifying locations with persistent or severe crop stress
  • Create auditable scripts and export procedures that reduce dependence on ad hoc map production
  • Support earlier, evidence-based decisions on agricultural assistance, extension deployment and contingency planning

Course outline

5 days

  • Google Earth Engine data catalogue, Code Editor and asset management
  • JavaScript variables, functions and server-side versus client-side operations
  • Importing feature collections and defining farm, district and livelihood-zone areas of interest
  • Coordinate reference systems, scale and pixel resolution in agricultural analysis
  • Image, ImageCollection and FeatureCollection data structures
  • Filtering satellite collections by boundary, date and metadata
  • Agricultural monitoring questions, crop calendars and indicator selection

Workshop Participants create an Earth Engine project that imports a reporting boundary, filters Sentinel-2 imagery for a defined growing season and saves a reusable starter script.

  • Sentinel-2 spectral bands and their relevance to crop observation
  • QA60 and S2 Cloud Probability approaches to cloud and shadow masking
  • Median, percentile and quality mosaic seasonal composite methods
  • NDVI calculation and interpretation for crop canopy development
  • EVI calculation for high-biomass crop conditions
  • NDWI calculation for vegetation moisture and surface-water context
  • False-colour visualisation, legends and map-layer inspection

Workshop Participants build a cloud-masked Sentinel-2 composite and generate NDVI, EVI and NDWI map layers for a selected agricultural season.

  • CHIRPS rainfall data structure and temporal aggregation
  • Dekadal, monthly and seasonal rainfall total calculations
  • Long-term baseline construction for rainfall comparison
  • Absolute and percentage rainfall anomaly methods
  • Vegetation condition index and historical NDVI comparison
  • Combining rainfall and vegetation signals for drought interpretation
  • Limits of satellite indicators and requirements for field validation

Workshop Participants calculate seasonal CHIRPS rainfall anomalies and compare them with NDVI departures to identify candidate drought-stress locations.

  • Cropland masks and land-cover considerations for agricultural summaries
  • Reducing raster indicators over districts, farms and management blocks
  • Mean, median, percentile and area-threshold zonal statistics
  • Time-series charting with ui.Chart for rainfall and vegetation trends
  • Threshold design for crop stress classification
  • Ranking reporting units by vegetation and rainfall conditions
  • Exporting GeoTIFF, CSV and chart outputs to Google Drive

Workshop Participants produce a ranked district table, vegetation time-series chart and exported crop-stress map for an operational reporting cycle.

  • Structuring reusable Earth Engine functions and parameter blocks
  • Managing season dates, baseline periods and indicator thresholds
  • Quality assurance checks for imagery, masks, boundaries and summary tables
  • Interpreting conflicting rainfall and vegetation signals
  • Map design for early-warning briefs and management decisions
  • Integrating Earth Engine exports with QGIS and spreadsheet reporting
  • Workflow documentation, ownership and update schedules

Workshop Participants complete and present an annotated crop-monitoring and drought-assessment script, map output, results table and 90-day implementation plan.

Who should attend

  • Agricultural Monitoring Officers — who need repeatable satellite-based evidence of crop condition across large areas
  • Food Security Analysts — who assess seasonal production risks and support early-warning briefings
  • GIS and Remote Sensing Officers — who need to operationalise Google Earth Engine workflows for agriculture programmes
  • Climate Adaptation Specialists — who track rainfall deficits and vegetation response for resilience planning
  • Agribusiness Field Operations Managers — who require comparable crop-performance indicators across production zones
  • Humanitarian Programme Officers — who need spatial evidence to target field verification and drought-response activities

Skills and tools covered

Earth Engine scriptingVegetation index analysisRainfall anomaly mappingCloud maskingZonal statisticsDrought evidence reporting

Tools and standards

Google Earth Engine Code Editor, Google Earth Engine JavaScript API, QGIS, Google Drive

How the course runs

Training methodology

The course uses short instructor-led concept sessions followed by guided work in the Google Earth Engine Code Editor. Each day, participants adapt working JavaScript examples to an agricultural case area, inspect intermediate outputs and troubleshoot common issues such as cloud contamination, inappropriate date windows and mismatched boundaries. Case discussions connect satellite indicators to crop calendars, rainfall performance and field verification decisions. Small-group reviews test how maps could be interpreted in an early-warning meeting. The final session is an application workshop in which participants refine their own crop-monitoring workflow and implementation plan.

Requirements and prerequisites

Participants should be comfortable using a web browser, managing files and folders, and working with basic tables in Excel or Google Sheets. Familiarity with maps, coordinates, administrative boundaries, crop calendars or agricultural seasons is helpful, but not essential. The course introduces Google Earth Engine JavaScript syntax from the ground up, so prior programming experience is not required; complete beginners should expect to write and adapt short scripts during every day of the course. Participants need a laptop, a reliable internet connection and a Google account that can access Google Earth Engine. Prior experience with GIS software such as QGIS is useful but not assumed.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

No prior programming is required; the course introduces the JavaScript patterns needed for Google Earth Engine through guided examples. Basic familiarity with maps, agricultural seasons or spreadsheets will help participants interpret outputs, but remote-sensing expertise is not assumed.

Bring a laptop capable of running a modern web browser, with reliable internet access and permission to use Google services. You will need a Google account with access to Google Earth Engine; instructions for checking access are provided before the course.

Yes. The methods are designed for agricultural development, food-security monitoring, drought early warning, insurance support and extension planning as well as commercial crop operations. Exercises use reporting units such as districts and livelihood zones alongside farm-block examples.

This course concentrates on operational crop-condition and drought workflows in Google Earth Engine rather than broad GIS functions or remote-sensing theory. Participants work directly with Sentinel-2, CHIRPS, vegetation indices, seasonal baselines, zonal statistics and reporting exports.

You can adapt the supplied script by replacing the boundary, season dates, crop calendar and baseline period for your own programme area. The workflow supports recurring monthly, dekadal or seasonal updates and exports outputs for QGIS, spreadsheets and management briefs.

You will leave with an annotated Google Earth Engine script that creates cloud-masked vegetation composites, rainfall anomalies, zonal summaries and exports. You will also produce a drought-assessment map, a ranked results table and an implementation plan for your organisation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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