Satellite Images API for Agriculture: NDVI, EVI, TRUE and FALSE color

Satellite images API is the dataset from the Landsat8 and Sentinel2 satellites 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. Satellite images API, 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.

TRUE color

Paddy fields in Pangasinan province, Philippines

Using the NDV index enables the field boundaries to be clearly discerned

With NDVI, the sections of the fields are displayed as barren, in white color. In the False color regime, it can be seen that part of them is waterlogged.

NDVI

Rice terraces in Tuguegarao province, Philippines.

 The EVI image looks clearer 

We provide a historical data as well as satellite images that are as up-to-date as possible (allowing for the data source and cloud cover) for the very nearest time period. These satellite images are available to all account users, including those who are using the free package.

The full set of the AGRO API capabilities can be seen here 

Satellite images API

Today, we would like to look in more detail at one of the essential elements of our Agro API, Satellite images API. Satellite images API is the dataset from the Landsat 8 and Sentinel 2 satellites 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. 

We provide a historical data as well as satellite images that are as up-to-date as possible (allowing for the data source and cloud cover) for the very nearest time period. These satellite images are available to all account users, including those who are using the free package. For non-paying users, the range for satellite data requests is 6 days.  For the paid service, it is one year.  Learn more.

Please note that there are limitations on the total area for which data can be requested as well as the number of requests that can be made per minute. However, should you exceed these figures, we will continue to provide you with the data you need so that your work is not affected and your customers do not run and hide!*

All of the details and the full set of the Agro API capabilities can be viewed here.
As always, we would be glad to hear your remarks and suggestions regarding our product!

* At the end of your billing month, we will provide you with a separate bill for any additional area, beyond the tariff limits, for which you requested data.

Helping farmers manage their enterprises: weather and satellite APIs for agroservices

Objectives:
As farms mainly consist of crop fields, which can be hundreds of acres in size, much time and resources are demanded of farmers in obtaining an accurate picture of the overall condition of these farms.
Drying out of plants or, conversely, an excess of moisture and a rise in the number of pests: these can all take their toll on the size and quality of the harvest and demand a rapid response. There are also such problems as the danger of overusing fertilizers, which poses a threat not only in terms of extra costs but also in that it is harmful to the environment and primarily to the health of farmers themselves.
To maximize harvests, constant monitoring is required throughout the season; and it is not easy finding the time to keep up with changes for each crop, not to mention monitoring the condition of every single acre. When deciding on long-term plans, a comparative analysis has to be carried out for both the usual course of the seasonal cycle and, in particular, any crises that have arisen.
To assess the current situation and to keep track of changes compared with preceding seasons and with the condition of neighboring fields, accurate information on both the past and the present is needed as well as future forecasts that are as precise as possible.

Solutions:
There are currently numerous services that help with managing farms for any acreage: checking boundaries and nutrient and moisture intake, monitoring the negative effects of weather conditions and diseases, and controlling pest numbers. And this can all be done without having to visit the fields, just by using a phone or tablet screen or a PC.

It is exactly to provide these services that OpenWeather offers a wide range of APIs for different weather and satellite data combined in the one product, Agro API, with universal and simple syntax.

What we offer:

About Agriculture API
Our product comprises a set of straightforward and user-friendly APIs which are easy to embed in farming applications. Each API is dedicated to its own particular area which means the user can be flexible about how often the data is updated and receive only information that is necessary.
By using our satellite and weather data, the user can assess the condition of farmland in real time and start planning.
This product is also geared toward the insurance and banking sector and can be used as a farm rating tool.
We provide the full range of data from satellites and weather stations for monitoring the condition of farmland and for making subsequent decisions.
All data processing is carried out online

Weather: Current, Forecast, History
Air temperature, humidity, wind speed, etc.. We provide not only current readings but also historical data for analysis and weather forecasts.

TSOIL & MSOIL: Temperature and Soil Moisture
Crucial indices allowing you to adjust irrigation work and prevent crop root damage

PA & TA: Accumulated Temperature and Precipitation
Temperature quantity index, expressed as the sum of diurnal temperatures exceeding a set threshold. Humidity quantity index, expressed as the sum of precipitation . This data is essential to decision-making regarding the date and favorability of conditions for sowing important crops.

NDVI & EVI: Normalized Difference Vegetation Index & Enhanced Vegetation Index
One of the most common indices allowing assessment of vegetation cover. We provide
current and historical data to allow analysis of vegetation growth rates and subsequent forecasting.

UV index: Ultraviolet Index
UV radiation has a direct effect on plant photosynthesis. Short-wavelength UV light in small quantities can increase the growth of certain types of plants and have a beneficial effect on further development, while constant exposure to medium-wavelength UV can kill crops. This index is useful for a complete analysis of potential agricultural land.

TRUE & FALSE Color:
Mainly used to visualize vegetation cover and differentiate it from urban land and any land not used for agricultural purposes. With these images it is also possible to distinguish between different types of vegetation.

Weather data can be requested both for a particular point and for a polygon*.
We are offering you the chance to try out our free package. All you have to do is set up a user account** and you will receive a personal API key.
We have also put together paid services as part of which the user is provided with a wider range of functions, which you can find out about here

* Currently, as part of the existing paid packages, we are providing data that is of the utmost relevance to agribusiness. Other territories can be added upon request.
** Users who are already registered can use their username and password for their new account 

NEW! Agro API - service for Agriculture

OpenWeatherMap team are pleased to announce that we are launching a new product aimed primarily at specialists developing agricultural services and addressing the specific requirements of this sector. This product is also geared toward the insurance and banking sector and can be used as a farm rating tool.

As part of this product, we are providing an API for receiving weather data (current weather, forecasts and history), satellite data (current and historical) and weather and vegetation indices based upon this.  As well as the data we already provide in other products, here we have added specialized agricultural indices such as soil temperature and moisture, accumulated temperature, cumulative precipitation and satellite data*: images from space and vegetation indices (EVI and NDVI) based upon them. Weather data can be requested both for a particular point and for a polygon. Find out more here.
We are offering you the chance to try out our free package. All you have to do is set up a user account** and you will receive a personal API key.

We have also put together paid services as part of which the user is provided with a wider range of functions, which you can find out about here

* Currently, as part of the existing paid packages, we are providing data that is of the utmost relevance to agribusiness. Other territories can be added upon request.
** Users who are already registered can use their username and password for their new account

We look forward to your feedback and comments! We will be glad to answer any questions you might have.

OpenWeatherMap 2017: Results of the Year

So the, new year, 2018 has come, and it's time to sum up the results of the past year for our company, OpenWeatherMap (UK, US and Latvia), developers of one of the best weather APIs in the world. 
A lot happened and much has changed. 
During the year, our number of users grew from 600,000 to 1 million. We participated in the Startup Grind Global Conference in Silicon Valley where the team from OpenWeatherMap was named in the top 50 Startup Exhibitions of 2017. Our mature team was filled up with excellent professionals and wonderful people. 
We did a lot of new things and qualitatively improved our current developments. 
In 2017, we were pleased to present to you:

Weather data: API and Weather Maps

• The Open Dashboard for Agricultural Monitoring 
   can help give you an idea of the possible use for meteorological and satellite data in your agricultural applications.
• Specially for the agriculture sector, we launched an API for accumulated temperature data and an API for accumulated precipitation data.
• The Weather Historical Bulk service. 
  Now you can simply choose a city (or several cities) and download an archive, which contains bulk file with the weather history up to 5 years - any day, week or even several years.
• A new and improved version of an API for UV-index
    
  Throughout the entire year, we worked constantly on our history weather API. For this year, the amount of data supplied and speed to process that data increased significantly. Also, we made it possible to quickly upload data in a format that doesn’t require additional processing and can be understood by any user. 
  Our Weather Maps app changed qualitatively. 
  This year, we added the ability to switch the layers of weather and satellite maps, create various combinations with them, and connect them to mobile and web apps.

Satellite data: Vane platform

This year, we significantly improved our satellite platform. Our team undertook a huge amount of work and in June were able to present  a new version  of the satellite image processing  platform VANE.

Based on the Vane platform, we developed a new product called  Global Satellite Base Map, which uses visual tools and query language to generate a map from satellite images. The uniqueness of the product is that all data processing is done on the fly, and there are no presets or pre-made calculations. The user defines the parameters for a calculation and image processing and immediately receives a result for any territory. This capability was only possible thanks to Vane, our super powerful data processing platform.

We recently presented you with Query Builder, our new interface for the Vane platform. Now you can use this simple tool to create your own map in just a few seconds, and with just one click receive a completed link for display on your site or app using a web map library like Leaflet, Open Layer, Mapbox and Google Map.

We are grateful to everyone that worked with us for all this time. We thank you all your feedback and for not getting bored by our tech support. 
We have a ton of plans for the coming year. Stay tuned, and you will see a lot of the new and interesting things to come. Subscribe to our Telegram Channel https://t.me/openweathermap and get news first about our updates and new products!

Cloudless: global cloudless composite coverage based on the VANE Platform

In the drawing is global coverage obtained between 06.01.2017 and 09.01.2017 using data from the MODIS spectroradiometer aboard KA Terra and Aqua.

The current cloudless coverage of the Earth by medium and low-resolution satellite images are an important element in the regional and global systems that monitor the territorial changes caused by natural and man-made factors. For example, assessing the damage inflicted by forest fires caused by deforestation, volcanic eruptions, flooding,etc. Also, such types of coverage are popular as the base layer for cartographic web services.

The main stages of creating such coverage are: the selection of images, expert as a rule, the masking of clouded areas, tonal adjustment of images taken at different times of the year, and pasting them into single coverage using so-called “cutlines” which enable, to a certain extent, the joins between the pasted images to be hidden. Such operations, as a rule, are carried out in semi-automatic mode and require specialized software and highly qualified experts, which substantially increases both the time taken to create such a product and its cost.

We have designed and delivered a completely different approach to producing such coverage. At the heart of this approach lie the principles used in processing Big Data and machine-learning algorithms. The technology we have implemented allows us to obtain, in a fully automatic mode, the very best coverage, in terms of cloud cover, of the Earth for a specified time frame. Not only this but the technology we use for data preprocessing means that a composite product can be obtained using either one or several different data sources. To obtain a composite image, we carry out a pixel by pixel analysis of all images stored in our database for a specified time frame. As a result, a single value, the best in terms of cloud cover, is selected. By assembling such values from there, complete coverage is produced. 
At the present time, the most popular, due to its high performance and general accessibility, is the data received from images taken by the Landsat-8 satellite and the Sentinel-2 pair of satellites. It is also worth noting that images from satellite data are taken in near spectral bands which, in turn, simplifies its simultaneous analysis.

In the drawings below are fragments of composite coverage collected from data from Landsat-8 and Sentinel-2, 2016-2017.