- Crop disease and pest stress detection using hyperspectral sensing — Pathogen infection and pest feeding alter leaf pigments, water content, and cell structure days before visible symptoms appear. Narrow-band hyperspectral sensing can read those chemical shifts; broadband multispectral sensors mostly cannot.
- Crop residue cover and tillage practice detection — Post-harvest fields carry a spectral fingerprint that distinguishes no-till from ploughed ground. Short-wave infrared absorption by cellulose and lignin makes tillage practice legible from orbit, with important caveats about resolution and cloud.
- Crop type classification from multispectral imagery — Multispectral time-series imagery lets analysts distinguish wheat from maize from soya by tracking how each crop's reflectance evolves through the growing season. The method is proven but demands dense, cloud-free observations and reliable ground truth.
- Crop yield estimation using SAR and optical fusion — Combining Sentinel-1 SAR backscatter with Sentinel-2 vegetation indices lets analysts predict end-of-season crop yield at field or district scale, even through the cloud cover that makes optical-only approaches unreliable in humid growing regions.
- Actual evapotranspiration and crop water use mapping — Surface energy balance models applied to satellite thermal imagery can estimate actual crop water use at field scale, exposing irrigation inefficiency invisible to ground sensors. The method is powerful and genuinely difficult to get right.
- Farm-level crop loss verification for index and indemnity insurance — High-resolution optical and SAR imagery can verify crop losses at individual field level, but resolution floors, cloud cover, and evidentiary standards vary sharply by sensor and peril type.
- Flood inundation extent and cropland damage assessment — SAR backscatter change detection maps flood extent over farmland within hours of a satellite pass, even through cloud. Intersecting that extent with crop-type maps and calendars converts pixels into tonnes of likely production loss.
- Satellite data integration for food security early warning systems — Rainfall anomalies, vegetation departure, land surface temperature, and flood extent are the physical signals satellites contribute to food security early warning. They matter only when combined with market data, population vulnerability, and analyst judgement inside frameworks like FEWS NET and the IPC.
- Greenhouse and protected agriculture structure mapping — Plastic greenhouses and polytunnels produce a distinctive high-reflectance signature in visible and SWIR bands that saturates standard vegetation indices. Detecting them accurately requires resolving the confusion with salt pans, rooftops, and sand, and knowing where optical sensors alone fall short.
- Harvest progress monitoring at regional scale — Harvest is the fastest phenological transition in the agricultural calendar, and missing it costs governments and traders dearly. Sequential optical and SAR observations detect the abrupt canopy-removal signal, distinguish harvest from crop failure, and map regional progress at sub-weekly cadence.
- Irrigated area mapping using SAR coherence and backscatter — Sentinel-1 SAR coherence loss and seasonal backscatter shifts expose irrigated fields that optical imagery misses under cloud. This page covers the physics, the method, and the honest limits of mapping irrigation at national scale.
- Desert locust breeding habitat risk mapping — Desert locust outbreaks begin in remote semi-arid zones where moist soils and green flush create brief breeding windows. Satellite-derived soil moisture, vegetation anomalies, and rainfall estimates can flag those windows days to weeks before ground teams can reach them.
- Pasture biomass and livestock carrying capacity estimation — Satellite-derived vegetation indices and SAR backscatter can estimate above-ground herbaceous biomass across rangelands and managed pastures, converting canopy signals into carrying capacity metrics that inform stocking decisions at paddock scale.
- Planting date and crop establishment detection from dense time series — Detecting the precise date a crop establishes itself from satellite time series lets governments and traders act on supply signals weeks before any field survey. The method rests on catching an abrupt reflectance or backscatter shift as seedlings break the soil surface.
- Within-field variability mapping for precision agriculture prescriptions — Sub-metre to 3-metre multispectral imagery can resolve within-field nutrient zones that 10-metre sensors miss, but map accuracy and agronomic response are not the same thing. This page explains the physics, the sensors, and the honest limits.
- Root-zone soil moisture estimation for crop water status — Satellite sensors measure the top few centimetres of soil. Getting from that signal to the root-zone moisture that actually governs plant stress requires physics, modelling and honest accounting of what each sensor cannot see.
- Soil organic carbon mapping in cropland using bare-soil composites — Topsoil organic carbon can be estimated from satellite optical imagery when fields are bare, exploiting the reflectance contrast between carbon-rich and carbon-depleted soils. Sentinel-2 and Landsat archives make cloud-free bare-soil composites feasible at regional scale, though the method is strictly surface-limited without ground-truth depth profiles.
- Vegetation condition indices for agricultural drought monitoring — VCI and TCI normalise live NDVI and land surface temperature against multi-decade baselines to reveal moisture stress weeks before yield loss becomes visible. MODIS, VIIRS and Sentinel-3 SLSTR supply the archive depth and revisit frequency that make the baselines statistically meaningful.