Agricultural monitoring is a time-series problem: a single image of a field tells you little, but a regular cadence through the growing season reveals crop vigour, stress, irrigation problems, and yield trajectory. Planning is about consistent revisit and getting cloud-free scenes at the right phenological moments.
This guide explains how to plan in-season acquisitions: which spectral bands matter for vegetation, how to set a revisit cadence, and how cloud forecasting changes which passes are worth tasking.
Crop health indices such as NDVI rely on the contrast between red and near-infrared reflectance, so multispectral sensors with red and NIR bands are the baseline for agriculture. Red-edge and shortwave-infrared bands add sensitivity to chlorophyll content and crop water stress for more advanced analytics.
Spatial resolution should match the field size and management unit — moderate resolution suits broad-acre cropping and regional yield models, while VHR multispectral resolves within-field variability for precision-agriculture prescriptions.
Define the AOI as the farm or region and plan a recurring cadence rather than one-off passes — weekly to fortnightly revisit captures the crop-development curve and catches stress early. Stacking multiple optical/multispectral satellites tightens effective revisit and improves the odds of a clear scene at each step.
Time key acquisitions to phenological milestones (emergence, peak vegetative growth, senescence) so the series supports yield modelling and end-of-season assessment.
Cloud is the dominant constraint on optical agriculture monitoring. Before tasking, check the cloud outlook for upcoming passes and prioritise the clearest, so you do not spend a tasking slot on a scene that returns mostly cloud.
Where persistent cloud makes optical unreliable — humid tropics, monsoon seasons — SAR provides a weather-independent complement for structural crop and soil-moisture proxies, keeping the time series unbroken.
Any multispectral sensor with calibrated red and near-infrared bands. Resolution should match your field size — moderate for regional models, VHR for within-field precision agriculture.
A weekly-to-fortnightly cadence captures the crop-development curve and catches stress early; combine several satellites to maintain it despite cloud.
Check the cloud forecast for upcoming passes before tasking and prioritise the clearest overpasses; in persistently cloudy regions, add SAR to keep the series unbroken.
Yes — it is weather-independent and sensitive to crop structure and soil moisture, making it a valuable complement when optical scenes are repeatedly clouded out.
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