Soil web google earth download
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SoilWeb provides gardeners, landscapers and realtors with information relating to soil types and how to optimally use the soil. Identifying soil types is important to understanding land for agricultural production purposes and determining flooding frequencies and suitable locations for roads or septic tanks. The images are then linked to information about the different types of soil profiles, soil taxonomy, land classification, hydraulic and erosion ratings, and soil suitability ratings. The SoilWeb app provides users with information relating to soil types that are associated with their location.
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“The SoilWeb app is a portable interface to authoritative digital soil survey data from NRCS, giving users access to practical, detailed scientific soil information on the go.” “SoilWeb reached a new milestone this year when it was integrated with Google Maps and designed to scale across any device, desktop, tablet, or smart phone,” said NRCS Chief Matthew Lohr. The newly updated SoilWeb smartphone application is available as a free download on Google Play and the Apple App Store. The app now has a cleaner and more modern interface with GPS-location-based links to access detailed digital soil survey data (SSURGO) published by the NRCS for most of the United States.
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The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.USDA’s Natural Resources Conservation Service (NRCS) and the University of California, Davis Soil Resource Laboratory today announced the release of the iOS and Android SoilWeb app, version 2.0. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the project. The predictive accuracy for most soil properties declined with depth. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Soil spatial information has traditionally been presented as polygon maps at coarse scales.