Dataset

farmland-vegetation-sentinel

Field-level agricultural analytics built on Sentinel-1 and Sentinel-2 imagery, offering vegetation indices, soil wetness readings, and yield forecasts aimed at commodity-market participants.

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Dataset card

Granular.ai publishes a farmland-vegetation-sentinel dataset drawn from Sentinel satellite imagery to monitor crop and soil conditions. The product is offered under a commercial license and is positioned within the agriculture and geospatial analytics space.

What this dataset contains

The dataset covers vegetation and soil-moisture conditions on farmland, together with yield-related signals, derived from Sentinel satellite imagery. It is tagged for agriculture, vegetation, soil-moisture, yields, and Sentinel sources, indicating field-level agronomic monitoring rather than financial transaction or market data. Granular.ai distributes the product under a commercial license, so access is restricted to paying customers rather than open download.

Structure and fields

No column schema is published in the available facts, so the exact fields and their types cannot be confirmed. Splits, row counts, and masked sample rows are likewise unstated, leaving the storage format, cadence, and spatial resolution unspecified. Analysts evaluating the product would need to request a data dictionary directly from the publisher.

How analysts use it

Given its vegetation, soil-moisture, and yield tags, the dataset is plausibly suited to forecasting crop production, benchmarking regional yields, and stress-testing agricultural-commodity exposures for commodity or macro funds. It could also support due diligence on farmland holdings by tracking in-season vegetation health. Concrete quantitative use cases cannot be described because no sample values, geographies, or time ranges are provided in the source facts.

Documentation & references 2