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Meteomatics global soil moisture index

by Meteomatics
Plus, the advanced soil type scheme derived from the root zone data (30-100 cm below the surface) of ... a number of benefits, including a high spatial resolution (10km), the frequency of data updates, with
Available for 227 countries
41 years of historical data
Pricing available upon request
Free sample available
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Meteomatics
Based in Switzerland
Meteomatics
Meteomatics weather API delivers fast, direct, simple access to an extensive range of global weather and environmental data. A single API endpoint to acce...
Spire
Based in United Kingdom
Spire
Spire is a data provider offering Alternative Data, Atmosphere Data, Aviation Data, Ionospheric Data, and 8 others. They are headquartered in United Kingdom.

The Ultimate Guide to Surface Data 2021

Learn about surface data analytics, sources, and collection.

What is Surface Data?

Surface/terrain analysis is the interpretation of the earth’s topographic features through geographic information systems. This analysis involves a detailed evaluation of the earth’s surface features such as slope, aspect, view-shed, elevation, contour lines, and upslope and downslope flow-lines. The idea around surface analysis is to model mathematical abstractions of surface terrain to describe landscapes and generate a comprehension of connections between ecological processes and physical features. Therefore, surface data rules control the mechanisms in which features are used to describe a surface.

How is Surface Data collected?

Surface data, together with its consortium of supporting tools, enhances the storage and preservation of vector-based surface capacities. Through geoprocessing functions, surface data is collected by loading data from external sources into a geo-database. The data is then edited by the features within the geo-database tool that maintain and update the data. Geo-database tools are made up of interactive display and query tools that help in the exploration and application of terrain surfaces. As such, a user hoping to get terrain insights from surface data can analyze it based on an area of interest (AOI) and level of detail (LOD), hence having a suite of tools that give wide-ranging means by which surface data is produced and used.

What are the attributes of Surface Data?

Through digital elevation models (DEMS), surface datasets provide crucial information on morphometric variations of the land surface. DEMs provide the means to extract terrain attributes that can be further scaled down to either primary or secondary attributes that give quantifiable dimensions of terrain surface. Primary attributes include variables such as elevation, slope, plain, and curvature profiles. On the other hand, secondary attributes involve an amalgamation of primary features with aspects such as solar radiation, moisture content index, sediment transport value, and others. It is using these attributes that spatial variability of given landscape surfaces can be categorized.

What is Surface Data used for?

The surface analysis uses elevation data to describe the landscape, for basic visualization, modeling, or to support decision making, generally in conjunction with other geospatial information. While tables, scatterplots, or histograms can be created by terrain analysis, a map will almost always be the main product. The two main reasons for surface analysis to be done include exploring information and seeing relationships and then sharing results with others. The analysis of surface is largely twofold; the qualitative aspect that is mainly applied in military operations for terrain analysis, and the more advanced form of numerical computations that find use in geomorphometry. Geomorphometry refers to a science of quantifying topographies with its operational concern being the extraction of land surface features and objects that are constructed from the digital elevation models (DEMs). Surface data is crucial in understanding the engineering site or in the choosing of locations for economic development, where users analyze how the terrain will affect and limit human activity.

How can a user assess the quality of Surface Data?

The core purpose of quality assessment for surface data is to maintain spatial data standards. A user can therefore assess the quality of surface data by taking into account three important quality elements: level of completion, logical consistency, and positional accuracy. Accuracy forms a very important quality aspect for surface data as it defines the difference of the value of a variable on a dataset when contrasted with a variable in the data model (reality). By accurately defining positional accuracy, a user can assess the quality of surface data by considering the position (horizontal or vertical) of objects and the precision in terms of scale and resolution.

Who are the best Surface Data providers?

Finding the right Surface Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Surface Data providers that you might want to buy Surface Data from are Meteomatics and Spire.

Where can I buy Surface Data?

Data providers and vendors listed on Datarade sell Surface Data products and samples. Popular Surface Data products and datasets available on our platform are FinPricing FX Implied Volatility Surface Data - Forex Data (Global) by FinPricing, FinPricing Cap Implied Volatility Surface Data Feed API - USA, Canada, Europe, Japan by FinPricing, and Meteomatics global soil moisture index by Meteomatics.

How can I get Surface Data?

You can get Surface Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Surface Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Surface Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Surface Data?

Surface Data is similar to Weather Data, Air Quality Index, Pollen Data, Wildfires Data, and Climate Data. These data categories are commonly used for .