Global Coverage
Available through DigiFarm’s Field Boundaries in over 30 countries, as well as for custom polygons.
Predictable Price
Convenient system of discounts for large scale use cases. The larger the area - the lower the price.
API-Ready
Available with on demand processing and we offer simple API endpoints for an easy integration.
Characteristics of Time Series Data
The temporal resolution of the data leverages the entire S2 archived data (back to 2017) and enables clients and end-users to choose Areas of Interest from DigiFarm’s global dataset of automatically delineated field boundaries as the basis for Time Series extraction, as well as using custom polygons.
Spectral Bands
Historical Time-Series data for 10 S2 spectral bands as well as vegetation indices such as EVI, NDVI, NDMI and MI.
Noise-free Data
DigiFarm's highest precision field boundaries to extract only crop data, cloud images are also filtered out.
Analytics Ready
Production scripts curated by a team of highly specialized remote sensing scientists translate to a reliable data source.
Full Support
We provide full integration support and documentation for an easy integration to your agricultural solution.
Full Control of Data Delivery
The Time Series data will initially be served through DigiFarm’s client dashboard. Access through API endpoints will be available soon after for our clients to easily integrate it in their digital solutions.
Year Range from 2017
Choose the period on which Time Series will be extracted, from a range of years back to 2017 to a specific period within a year.
Cloud Coverage %
This value includes snow, defective pixels, low and high probability clouds, cloud shadows, data cover, etc.
Chart Shapes
The output data should be smoothed or raw values - that's all customers decide switching between display types.
On Demand. In which clients can make a request to our API and have the fields of interest be processed automatically;
Time series is a valuable tool for monitoring vegetation dynamics and assessing the impact of climate change.
Improving Crop Management
By monitoring the changes in vegetation health and strength over time, farmers can identify areas of the field that may require specific attention, such as nutrient application or pest control. This proactive approach to crop management can lead to better overall crop health and higher yields.
Enhancing Yield Modeling
By analyzing the trends in vegetation health over time, farmers and researchers can develop models to predict crop yields. It allows for the estimation of potential yields based on the observed vegetation dynamics, contributing to more accurate yield projections and harvest planning.
Optimizing Irrigation Practices
By analyzing the changes in vegetation over time, farmers can make informed decisions about when and how much to irrigate, thus maximizing water use efficiency and ensuring optimal crop growth and yield.
Monitoring Crop Growth
By tracking changes in vegetation health over time, farmers can assess the progress of their crops and identify any areas that may require intervention. This proactive approach to monitoring crop growth supports timely interventions and adjustments.
Supporting Precision Agriculture
Vegetation indices are essential for precision agriculture, as they provide valuable information for targeted interventions. This approach promotes resource efficiency and sustainability, ultimately contributing to improved crop yields.
Common Questions
How can I use the Time Series?
For example, you can monitor the regular use of a particular field, and how the vegetation growth compares to previous seasons for yield estimation or if there is any water stress in the current season and you can even see if cover crops have been used between each season.
Will I be able to get data for the current season?
Can the Time Series be requested for purposes outside of agriculture?
How long does it take for the Time Series to be processed after a query?
