Remote Sensing for Agricultural Water Management Training Course

Participants can select appropriate satellite data for defined agricultural water-monitoring tasks.

• Sentinel-2 spectral bands selected for crop and surface-water monitoring
• Landsat thermal imagery assessed for evapotranspiration estimation requirements
• Sentinel-1 radar backscatter interpreted under cloud-prone seasonal conditions
• Spatial, temporal, spectral and radiometric resolution compared systematically
• Revisit frequency matched to crop stages and irrigation decision cycles
• Open data catalogues assessed for coverage, latency and licensing constraints

Participants can prepare satellite and reference datasets for consistent agricultural water analysis.

• Coordinate reference systems checked before combining imagery and field boundaries
• Cloud masks and shadow masks applied to optical satellite scenes
• Atmospheric correction outputs reviewed using surface-reflectance quality flags
• Mosaic and compositing methods selected for seasonal image coverage
• Field boundaries cleaned, attributed and aligned with image pixels
• Meteorological, soil and irrigation records joined through common identifiers

Participants can create crop and irrigated-area masks suitable for water-use assessment.

• Training samples designed from field records, imagery and local knowledge
• Supervised classification separates crops, bare soil, water and settlements
• Random forest classification parameters tuned using independent validation samples
• Irrigated area inferred from seasonal greenness and cropping persistence
• Confusion matrices used to report classification accuracy and class errors
• Smallholder fragmentation assessed against sensor pixel-size limitations

Participants can derive and interpret vegetation indices for crop-condition monitoring.

• NDVI calculated from near-infrared and red reflectance values
• EVI applied where dense canopies cause NDVI saturation
• NDWI variants distinguished for vegetation moisture and open-water detection
• Red-edge indices interpreted for chlorophyll response and crop vigour
• Phenology curves constructed from multi-date vegetation-index observations
• Anomalies compared against seasonal baselines and reference-year conditions

Participants can interpret land-surface temperature and energy-balance components for water analysis.

• Land-surface temperature retrieved and interpreted with atmospheric correction assumptions
• Surface energy balance partitions net radiation into sensible and latent heat
• Hot and cold pixels selected for energy-balance model calibration
• Thermal anomalies separated between water stress and exposed dry soil
• Albedo, emissivity and surface roughness effects examined in calculations
• Cloud contamination and thermal resolution limits documented for each scene

Participants can calculate reference evapotranspiration and crop water requirements from weather data.

• FAO-56 Penman-Monteith equation applied using standard meteorological inputs
• Weather-station quality checks identify missing, implausible and inconsistent records
• Crop coefficients selected by crop type, growth stage and management
• Effective rainfall estimated using locally appropriate rainfall accounting methods
• Root-zone depletion linked to irrigation timing and allowable stress
• Daily crop water requirement tables prepared for irrigation advisory use

Participants can estimate actual evapotranspiration using satellite-derived and meteorological inputs.

• Actual evapotranspiration distinguished from reference and potential evapotranspiration
• SEBAL and METRIC model logic compared for operational applications
• Evaporative fraction converted into daily evapotranspiration estimates
• Crop coefficient approaches combined with vegetation-index time series
• Satellite overpass timing assessed against daily irrigation and weather cycles
• Evapotranspiration maps aggregated by field, canal block and scheme

Participants can assess agricultural water performance using spatially explicit indicators.

• Water productivity calculated from yield, biomass or revenue per evapotranspiration
• Relative evapotranspiration identifies areas receiving insufficient seasonal water
• Adequacy, equity, dependability and reliability assessed across command areas
• Delivery performance compared with crop water-demand patterns spatially
• Yield gaps interpreted alongside water stress, soil and management factors
• Indicator thresholds tailored to crop calendars and scheme objectives

Participants can use radar and soil-moisture evidence to identify irrigation patterns and constraints.

• Radar backscatter responses interpreted for soil moisture and crop structure
• VV and VH polarisation compared across crop and irrigation conditions
• Change detection identifies probable irrigation events between radar acquisitions
• Satellite soil-moisture products assessed against field-scale resolution requirements
• Rainfall and irrigation signals separated using weather and canal records
• Radar speckle filtering applied without removing meaningful field patterns

Participants can analyse seasonal satellite time series to detect irrigation and crop-condition trends.

• Regular time series constructed from cloud-filtered image collections
• Gap-filling methods documented where optical observations are unavailable
• Seasonal trajectories compared between irrigated, rainfed and fallow fields
• Breakpoint analysis identifies abrupt vegetation decline and recovery periods
• Trend analysis distinguishes persistent change from short-term weather variation
• Dashboards structured around weekly, monthly and seasonal management decisions

Participants can validate remote-sensing outputs using fit-for-purpose field and operational data.

• Validation plans specify variables, sampling locations, timing and acceptance criteria
• Field observations record crop stage, irrigation status and management conditions
• Volumetric soil-moisture measurements compared with satellite-derived moisture indicators
• Eddy covariance and lysimeter data interpreted for evapotranspiration validation
• RMSE, bias and coefficient of determination reported transparently
• Uncertainty sources traced through imagery, models, weather and field data

Participants can integrate remote-sensing estimates into irrigation-scheme and basin water accounts.

• Water accounting tracks inflows, depletion, storage changes and return flows
• WA+ concepts applied to agricultural evapotranspiration and land-use outputs
• Canal deliveries reconciled with field consumption and conveyance losses
• Remote-sensing estimates compared with abstraction permits and meter records
• Seasonal water accounts aggregated across schemes, sub-basins and crop classes
• Accounting assumptions documented for allocation, audit and planning purposes

Participants can design repeatable workflows for routine agricultural water monitoring.

• QGIS workflows organise layers, symbology, calculations and map layouts
• Google Earth Engine scripts filter collections and calculate image indices
• Reusable parameter tables store dates, thresholds, crops and boundaries
• Processing chains record inputs, versions, assumptions and quality controls
• File naming and metadata conventions support team handover and audit
• Automated exports prepared for spreadsheets, dashboards and management reports

Participants can translate remote-sensing findings into practical irrigation and water-allocation recommendations.

• Map legends designed to prevent false precision and misinterpretation
• Priority areas ranked using water stress, crop value and service gaps
• Irrigation scheduling recommendations stated with evidence and confidence limits
• Water-allocation scenarios tested against crop demand and available supply
• Management briefings distinguish observations, inferences, risks and recommended actions
• Farmer, scheme-manager and basin-authority information needs compared carefully

Participants can manage governance, ethical and technical risks in agricultural water-monitoring systems.

• Data-sharing agreements define ownership, access rights and update responsibilities
• Farmer privacy considered when publishing field-level irrigation observations
• Algorithmic bias assessed across farm sizes, crops and geographic areas
• Uncertainty communicated before enforcement, payment or allocation decisions
• Quality assurance protocols specify review points and sign-off roles
• Monitoring systems aligned with institutional mandates and dispute-resolution procedures

Participants can build, review and present a complete remote-sensing agricultural water-monitoring and advisory package.

• Define management question, geographic boundary, users and decision timetable
• Select imagery, weather records, field data and reference boundaries
• Produce crop-condition, evapotranspiration and irrigation-performance map outputs
• Validate findings against available observations and document material uncertainty
• Develop priority actions for irrigation scheduling, maintenance or allocation
• Present technical methods, findings and recommendations to management stakeholders