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Why Satellite imagery API? 

How to work with Satellite imagery API?

When you work with single-channel images, you need to focus in depth on a narrow range of values and clearly evaluate researched data.

If you want to download Sentinel-2 satellite data, then you’ve come to the right place! Our Satellite Images API was created exactly for this purpose.

This article is a step-by-step description of of how to work with polygons using our instruments QueryBuilder and Agro API. The previous part explained to you how to use your personal account of the OpenWeather company, be sure to use it before starting to read this article.

1) Satellite data search.
1.1. Selection of suppliers or distributors of satellite data.
1.2. Data search in archives. Search is based on the following criteria: - date of satellite imagery, percent of cloud coverage, type of satellite imagery system, level of data preprocessing, geographical area of interest (AOI).

While the whole of Europe suffers from abnormal heat, our team held teambuilding in our Riga office. This event was very productivity and invigorating. Besides, just meeting with colleagues is great thing!

This April, the OpenWeather company presented a brand new product, Agro API, which was intended to change the working process of agricultural applications. Our algorithms for collecting and processing satellite scenes and weather data combined with the simplified delivery of prepared products to the end user make this product wholly unique and convenient.

 A couple of weeks ago, the OpenWeather team visited the GEO Business 2018 event at the Business Design Centre in London. As promised, we will share our impressions with you.
The exhibition had an excellent programme that included practical demonstrations of the latest research and workshops on the use of services and technology in action. We were particularly interested in the Earth Observation and Satellite Applications seminar. One of the presentations was about the use of satellite image recognition technologies to track the construction phase.

The fundamentals specific to the market are that satellite data is provided mostly for global territories and enterprises, and there are quite a few, if any, solutions for giving data for small customers.
We are happy to say that our service, Agro API , can provide satellite data for any size of customer, with on-the-fly data processing and availability. It is an intuitive, fast, and robust API to satellite imagery that is suitable for use in applications in either a large area or a small field. We provide the most useful tools for empowering applications for farmers and machine learning. There are so many spheres where environmental data is needed nowadays, and agricultural applications are among them.

The OpenWeather team visited GEO Business Show 2018  in London on 22–23 May. The show is an international exhibition for the geospatial industry that provides an excellent opportunity to communicate with experts and enthusiasts in the area of geospatial and satellite technologies, listen to nearly 200 speakers skilled in working with spatial data, and meet new companies of the industry.

We were delighted to explore stands of exhibitors who demonstrated new capabilities for the gathering, storing, processing, and delivery of satellite data. We were also thrilled to meet and talk face-to-face with exhibitors who already use our weather and other geodata in their applications.

There was a great event programme that included demonstrations, workshops, and speeches. Describing all of the impressions at once is difficult, so please stay tuned for more details.

We will share practical cases and photos in the next post.

 Satellite images API on the basis of which we calculate quantitative indices, such as NDVI, EVI, and others, and from which we also obtain ready-made images of territories in True and False color, NDVI, and EVI , along with other APIs to data such as Weather Data, Soil Data, Accumulated Temperature and Precipitation Data all go into making our new Agro API product.

TRUE Color and False Color

TRUE Color — "True color" is a rendering of red, green and blue satellite imagery spectral bands to the RGB composite image that seems to look natural.

False Color (b5 b4 b3) — "False color" is a rendering using NIR (near infrared) band which is more useful to visualize land cover and differentiate it from the urban and farmland areas. In these images it is possible to pick out different types of vegetation. Also easily discernible is the boundary between land and water, which enables changes in shorelines to be tracked.

NDVI and EVI vegetation indices

Some of the most common indices enabling quantitative assessment of vegetation cover. Convenient for tracking the growth rate of plants and monitoring any changes to them.

NDVI - This is an index calculated according to a set formula which uses near infrared and red wavelengths. Used in calculating and monitoring vegetation growth and its dynamics. NDV Index is displayed in images using the white to green palette where dark green indicates a good yield and white indicates a poor one or lack of vegetation.

EVI  -  In areas of the dense canopy where the Leaf Area Index (LAI) is high, NDVI values can be improved using information from the blue wavelength. Information in this part of the spectrum can help correct atmospheric influences and background interference caused by soil.