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How to make online products on satellite images and integrate them to agricultural applications.
It is a scope of workouts that might be useful for precision agriculture applications. You can learn how to query satellite images to form scenes in RGB, NDVI with any color scheme with flexible customization of any parameters for any crops. Just integrate any query into the application to get instant result.
http://owm.io/cases/agri

In the current version of VANE Language, we use images from Landsat8 satellite which captures the Earth’s entire surface every 16 days. The satellite makes hundreds of images with a unique name for each one like LC81410552016219LGN00 and a pixel size of 30 meters, each image consists of 11 bands, the size of the uncompressed image is 2 GB.
Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. New Band 1 (ultra-blue) is useful for coastal and aerosol studies, and also new Band 9 is applicable for cirrus cloud detection. The resolution of Band 8 (panchromatic) is 15 meters. Thermal Bands 10 and 11 provide more accurate surface temperatures and are collected at 100 meters. Approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi).
By default, we get B2, B3, B4, B5, B7, but it is possible to download any other bands

The face of our planet is changing constantly. There are several reasons of this process, they are of natural and technogenic (i.e. a result of human activity) origin.
One of such processes that we can observe during the past decades is ice melting. And in particular, there is melting of big glaciers. This factor is a crucial one that impacts changing of the Earth’s surface the most. The matter is that this type of ice melting has the most influence on the level of the World Ocean, in comparison with melting of sea ice which is already in the water. Thus, observation of glacier melting has a great significance not only as a signal point in global warming, but also due to its impact on the level of a global ocean.

OpenWeatherMap is happy to announce good news for owners of private weather stations! We are launching a new version of Weather Stations API 3.0. Now there are more easy ways to manage your stations and transmit its data.  

In recent years, the problem of decreasing productivity of agriculture has arisen sharper due to climate change and long-term weather fluctuations; there is even the threat of famine in some countries, which are more dependable on crop productivity. This problem can be solved with traditional and known for centuries tools, i.e. by expansion of sowed land through deforestation.

Simultaneously, the intensification of manufacturing or the expansion of cultivated land into previously wild areas can lead to additional emission of greenhouse gases due to the removal of trees and also these processes can increase the amount of used fertilizers. These both actions are factors contributing to climate change and appearance of negative weather phenomena such as drought.

These are maps, which you can compose by a combination of any three bands. For instance, use SWIR (band 7) instead of the red spectrum (band 4) and use NIR (band 5) instead of the green one (band 3), then you will get a map of land victimized by fire (See Fire detection, above).
 To get a map of a necessary color, just choose a combination of bands and you will find out what it looks like to see the world through eagle or wolf eyes.

The NDVI is a major vegetation index for the agricultural industry and farming, and with VANE Language you can customize a colour scheme to get a proper picture. Here in contrast to just the NDVI option a user can set a color scale and define the colors of zones. 

The NDVI, i.e. the normalized difference vegetation index, is a simple graphical indicator of biomass active photosynthetically.The NDVI is one of the most common and widely used indexes for evaluation of vegetated areas, their quality and quantity. Using the NDVI it is possible to detect presence of plants, its dynamics of emergence and growth. For getting of this index there is Band 5 which measures the near infrared spectrum, or NIR (Near Infrared). This part of the spectrum is reflected by water in leaves of healthy plants. Maps of the NDVI with dynamics (in various seasons) let tracing peculiar features and deviations of seasonal vegetation.

Thank you for the article  Francesco Azzola
  http://www.survivingwithandroid.com
 @survivingwithan
  https://it.linkedin.com/in/francescoazzola

This post describes how to use OpenWeatherMap UV index. This is an interesting API because we can use it to explore some important aspects about Android and location aware API. Openweathermap provides this API for free! As you may already know, OpenWeatherMap provides also a full set of API about weather information: you can get current weather conditions, forecast, historical information and so on. This information is free and we can use OpenWeatherMap API free of charge.

Focusing on this article, at its end, we will build an Android app that gets UV index and show it using Material design guidelines.

Before diving into the app details is useful to have an idea about UV index.

One of the simplest operations is to generate RGB map. Here an image consists of Bands 4-3-2 which correspond to the well-known RGB color model. Red, green and blue spectra combine together for creation of full color images.