The measure of the quality and fertility of the soil is essentially what soil health data covers. The data measures aspects of the soil itself as well as the microbes and animals that live within it.
Researchers and scientists from university-based or government agency-based programs publish most soil health data. However, agricultural companies and individual farmers also provide this information.
The technologies used to collect and measure soil data are many. From pH test strips, radar, satellites, to spectroscopy, and more.
There are many types of soil health attributes, they’re presented in columns or as graphs or maps. The location of the soil is always denoted. Other typical attributes include soil color, consistency, and porosity. The data also frequently measures nutrient content, moisture content, salt content, and mineral composition.
Naturally, the agricultural industry uses soil health data the most. After them come the conservationists, geologists, and educational institutions.
Perhaps surprisingly, however, city planners and professionals in the medical, construction, and space exploration fields show a keen interest in soil health data too.
Yet, the uses of this data for these professions are not so mysterious. City planners, for example, use this data to plan drainage or irrigation projects to reduce flooding. Construction planners need stable soil in order to build. Biomedical engineers must determine whether toxic compounds have made it into the soil or from the soil into waterways. And engineers and scientists involved in space exploration want to determine the soil content of other planets or asteroids.
More and more, soil quality datasets are being published online, where a worldwide community can catch errors. However, if you create your own dataset, just note that no universal standard of data collection or sampling exist, though chemical methods become increasingly popular as they reduce inconsistencies between datasets. Additionally, keep in mind that depending on the time of year there are differences in the quality of the same batch of soil. So be sure to measure or collect data that has been measured at different times but with the same methods.
A Japanese spacecraft is nearing Earth after a yearlong journey home from a distant asteroid with soil samples and data that could provide clues to the origins of the solar system, a space agency official said on Friday.
JAXA, the space agency, plans to drop the capsule containing the samples onto a remote, sparsely populated area in Australia from 220,000 kilometres (136,700 miles) away in space, a big challenge requiring precision control.
The capsule, protected by a heat shield, will turn into a fireball during re-entry in the atmosphere at 200 kilometers (125 miles) above ground.
Coordinate grid constructed in projection ETRS89-GRS80, which divides displayed area into bands defined by meridians of the GRS80 ellipsoid, more precisely into zones defined by parallels of the GRS80 ellipsoid. The coordinate grid ETRS89-GRS80 is created as European standard for interoperability of data with geodetic coordinates (especially orthoimages and elevation data).
WMS-KLADY view service is provided as a public view service for the Base Maps of the Czech Republic of medium scales and Orthophotomaps of the Czech Republic Map Layouts vector data. It serves above all for orientation in the division of these products into individual export units. The service fulfils the OGC WMS 1.1.1 and 1.3.0 standards.
The Digital Surface Model of the Czech Republic of the 1st generation (DMP 1G) represents a picture of the territory including buildings and vegetation cover in the form of irregular network (TIN) with total standard error of height 0.4 m for precisely delineated objects (buildings) and 0.7 m for objects not precisely limited (forests and other elements of vegetation cover). The model is based on the data acquired by altimetry airborne laser scanning of the Czech Republic territory between years 2009 and 2013. DMP 1G is established to analyse terrain situation (DMR 5G) and geographical objects located on it (buildings, vegetation) of regional and partially local character, e.g. to analyse visibility, radio wave propagation modelling, atmospheric pollutants propagation, generating of terrain virtual images for flight