| What value we create?
One of the biggest problem in remote sensing are clouds. It is estimated (King et al, 2013) that about 67 percent of Earth’s surface is typically covered by clouds. Especially cloudy conditions dominate around mid-latitudes and areas near the equator as seen in figure 1.Optical satellites (for example Landsat, MODIS Terra/Aqua, Sentinel-2, DigitalGlobe series, etc) are not capable of seeing through clouds which creates shorter or longer gaps in time series of images when observing some phenomena. As stated by the unofficial Murphy's law invented by KZ, the most important events or trends happen during the gaps in the time series. We can fill the gaps with data from radar satellite Sentinel-1. Sentinel-1 has a synthetic-aperture radar (SAR), which can acquire data in all-weather, day or night. Although SAR data might not substitute optical data in all cases, there are several examples from agriculture domain, civil engineering and environmental monitoring, where SAR data is a valuable supplement to optical data.
How do we do it?
We have developed an automated processing chain for Sentinel-1 satellite images to produce time series for several parcel-related parameters. These are the main steps in our process:
Which parameters and statistics we can calculate?
All our parameters are parcel-related, which means they are calculated over the area of your interest (one or many polygons).
Why our Sentinel-1 time series are best on the market?
Where time series from satellite data is already used?
Our time series together with machine learning algorithms are being successfully used in nation-wide system for automated monitoring of mowing events on grassland. This near real-time system has been operational in Estonia since 2017. In addition to working solution in Estonia we have performed several user trials in Sweden, Denmark and Poland to enhance the existing cutting and grazing detection methodology. Check out our demo map. Different parameters have been used to monitor activities in peat production fields, mapping flood areas and monitoring characteristics of buildings as well as ice coverage, de-forestation, etc.Satellite data is more and more used in precision farming, providing both small and large scale insights into crop health and soil moisture, recommendations for harvesting date and fertilization need. Sentinel-1 time series can improve models which at the moment solely base on optical data.
Where are our and Sentinel-1 limits?
Spatial resolution: We use Sentinel-1 products (SLC IW) which have spatial resolution approx. 5x20m.
We can process smaller than 1 ha geometries depending on the shape of the field. Maximum time span: From year 2017 to present. Coverage: Sentinel-1 revisit frequency over Europe, parts of Antarctica, Hawaii islands and Galapagos islands is 6 days.
This coverage is ensured from same, repetitive relative orbits. Over other parts of world the frequency is 12 days.
How are the results delivered?
At the moment we have a very personal approach for each case and we deliver the data in a custom format. We suggest following formats:
• API service
• Postgres database .dump file
• .csv file
• .xls fileThis doesn’t suit for you? Definitely we can find the appropriate solution and help with access via API, visualization and web-map creation for larger projects.
We have also capability to process Sentinel-2 images and provide NDVI and other indices time-series in addition.
Feel free to contact us firstname.lastname@example.org.
NASA Earth Observatory Website: https://earthobservatory.nasa.gov/images/85843/cloudy-earth
Sentinel-1 SAR User Guide Introduction: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar
King et al, (2013, January 28) Spatial and Temporal Distribution of Clouds Observed by MODIS Onboard the Terra and Aqua Satellites. IEEE Transactions on Geoscience and Remote Sensing, 51 (7), 3826-3852.
K. Koppel, K. Zalite, K. Voormansik & Thomas Jagdhuber (2017) Sensitivity of Sentinel-1 backscatter to characteristics of buildings, International Journal of Remote Sensing, 38:22, 6298-6318, DOI: 10.1080/01431161.2017.1353160
Tamm, T.; Zalite, K.; Voormansik, K.; Talgre, L. Relating Sentinel-1 Interferometric Coherence to Mowing Events on Grasslands. Remote Sens. 2016, 8, 802.
Voormansik, K., Praks, J., Antropov, O., Jagomägi, J. and Zalite, K., 2013. Flood mapping with TerraSAR-X in forested regions in Estonia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2), pp.562-577.