Since 2011, the Bureau of Land Management (BLM) has collected field information to inform land health through its Assessment Inventory and Monitoring (AIM) strategy. To date, more than 6,000 terrestrial AIM field plots have been collected over BLM lands. The BLM AIM data archive is …
The FLDAS dataset (McNally et al. 2017), was designed to assist with food security assessments in data-sparse, developing country settings. It includes information on many climate-related variables including moisture content, humidity, evapotranspiration, average soil temperature, total precipitation rate, etc. There are multiple different FLDAS datasets; …
NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model …
NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model …
M2T1NXLND (or tavg1_2d_lnd_Nx) is an hourly time-averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface diagnostics, such a baseflow flux, runoff, surface soil wetness, root zone soil wetness, water at surface layer, water at …
Land Data Assimilation System (LDAS) combines multiple sources of observations (such as precipitation gauge data, satellite data, and radar precipitation measurements) to produce estimates of climatological properties at or near the Earth''s surface. This dataset is the primary (default) forcing file (File A) for Phase …
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is …
Predicted USDA soil great groups at 250 m (probabilities). Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles. To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS …
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail here. Antarctica is …
Soil bulk density (fine earth) 10 x kg / m3 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth …
Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Predicted from a global compilation of soil points. Processing steps are described in detail here. Antarctica is not included. …
Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m Derived from predicted soil texture fractions using the soiltexture package in R. Processing steps are described in detail here. Antarctica is not included. To access …
Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Training points are based on a global compilation of soil profiles: USDA NCSS AfSPDB ISRIC WISE EGRPR SPADE …
Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution Processing steps are described in detail here. Antarctica is not included. To access and visualize maps outside of Earth Engine, use this page. If you …
Predicted USDA soil great group probabilities at 250m. Distribution of the USDA soil great groups based on machine learning predictions from global compilation of soil profiles. To learn more about soil great groups please refer to the Illustrated Guide to Soil Taxonomy - NRCS - …
NASA Global Land Data Assimilation System Version 2 (GLDAS-2) has three components: GLDAS-2.0, GLDAS-2.1, and GLDAS-2.2. GLDAS-2.0 is forced entirely with the Princeton meteorological forcing input data and provides a temporally consistent series from 1948 through 2014. GLDAS-2.1 is forced with a combination of model …
The Soil and Landscape Grid of Australia (SLGA) is a comprehensive dataset of soil attributes across Australia at 3 arc-second resolution (~90m pixels). The surfaces are the outcomes from modelling that describe the spatial distribution of the soil attributes using existing soil data and environmental …
Data starting from 2023-12-04 are available in the NASA/SMAP/SPL3SMP_E/006 collection. This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band radiometer. The daily data here were collected from the descending (local …
Data prior from 2023-12-04 are available in the older NASA/SMAP/SPL3SMP_E/005 collection. They will eventually be reprocessed and added to this collection. This Level-3 (L3) soil moisture product provides a daily composite of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) L-Band …
Volumetric Water Content at 10kPa, 33kPa, and 1500kPa suction in 10^-3 cm^3/cm^3 (0.1 v% or 1 mm/m) at 6 standard depths (0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm). Predictions were derived using a digital soil mapping approach based on Quantile Random Forest, drawing on a global …
Bulk density, <2mm fraction at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/100. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) …
Clay content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative …
Depth to bedrock at 0-200 cm depth, predicted mean and standard deviation. Due to the potential cropland mask that was used for generating the data, many areas of exposed rock (where depth to bedrock would be 0 cm) have been masked out and therefore appear …
Effective Cation Exchange Capacity predicted mean and standard deviation at soil depths of 0-20 cm and 20-50 cm, Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) …
Extractable calcium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable iron at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable magnesium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable phosphorus at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable potassium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable sulfur at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Extractable zinc at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Soil fertility capability classification derived using slope, chemical, and physical soil properties. For more information about this layer, please visit this page. The classes for the 'fcc' band apply to pixel values that must be back-transformed with x modulo 3000. In areas of dense jungle …
Organic carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Sand content at soil depths of 0-20 cm and 20-50 cm,\npredicted mean and standard deviation. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative …
Silt content at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Stone content at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Total carbon at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
Total nitrogen at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/100)-1. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be …
USDA Texture Class at soil depths of 0-20 cm and 20-50 cm. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. Soil property predictions were made by Innovative Solutions for Decision …
Extractable aluminium at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with exp(x/10)-1. Soil property predictions were made by Innovative Solutions for Decision Agriculture Ltd. (iSDA) at 30 m pixel size using machine learning coupled …
pH at soil depths of 0-20 cm and 20-50 cm, predicted mean and standard deviation. Pixel values must be back-transformed with x/10. In areas of dense jungle (generally over central Africa), model accuracy is low and therefore artifacts such as banding (striping) might be seen. …
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["Google Earth Engine hosts a variety of soil datasets, including SLGA for Australia and iSDAsoil for Africa, offering diverse soil property information."],["iSDAsoil data often requires back-transformation for accurate values and may have lower accuracy in dense jungle areas."],["GLDAS-2 provides time-series climate and geophysical data globally at 3-hourly intervals."],["OpenLandMap offers global coverage (excluding Antarctica) of key soil properties like bulk density and organic carbon content at 250m resolution."],["Datasets vary in resolution and prediction methods, with many utilizing machine learning techniques."]]],[]]
