Earth Science Data Sources

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Multi State Research Efforts Focus on Climate Data & Monitoring
Multi State Research Efforts Focus on Climate Data & Monitoring
Climate information—historical, real-time, and predictive—is vital for the sustainable management of agriculture and natural resources. A committee of researchers and Extension educators from Land-grant Universities coordinates climate data collection and use in the western United States. Their work is supported in part by the Hatch Multistate Research Fund through USDA’s National Institute of Food and Agriculture. Climate information—historical, real-time and predictive—is vital for the sustainable management of agriculture and natural resources.  Climate change poses major challenges to agriculture and natural resources in the western United States. Climate data are key to practices, tools, legislation, programs and policies that protect and enhance water, soil and air.  With so many different entities collecting climate data and myriad ways of storing and distributing data, it can be difficult to access relevant, reliable climate information. Good communication and coordination are needed, especially to address regional issues.  A committee of researchers and Extension educators from Land-grant Universities coordinates climate data collection and use in the western United States. Their work is supported in part by the Hatch Multistate Research Fund through USDA’s National Institute of Food and Agriculture.  For more than 30 years, the committee has leveraged existing climate data collection, analysis and distribution infrastructure and technologies to meet agriculture and natural resource management needs in the western U.S. The group has played a critical role in coordinating these groups and networks. In times of increasingly tight budgets, this multistate committee is essential to ensuring the sustainability and economic viability of climate networks and partnerships. With members in multiple states, the committee can facilitate efficient climate monitoring and widely promote the use of climate data and products for agriculture, water management, industry, emergency preparedness, transportation, policymaking, education and other purposes.  Achievements  To improve climate data and tools, the committee:  Evaluated how well existing climate data products and services are meeting user needs, identified gaps in climate monitoring networks and explored remedial options.  Developed and encouraged use of new tools, such as more accurate soil moisture sensors, that improve data quality.  Increased state-level climate data collection to help ensure high-quality data.  To make climate data collection more collaborative and efficient, the committee:  Developed standards for siting and maintaining weather stations and archiving data and metadata.  Developed technologies to cost-effectively gather and distribute data from various sources.  Coordinated climate data collection among multiple agencies.  Encouraged inter-agency support to help climate monitoring networks obtain enough funding and personnel to provide consistent, high-quality data and minimize operational and maintenance costs.  Contributed to the Community Collaborative Rain Hail and Snow Network, a low-cost, low-tech, volunteer-based precipitation monitoring network that is administered by Colorado State University.  To increase access to climate data and tools, the committee:  Developed best practices and technologies for manipulating, distributing and presenting climate data to allow easy use for agriculture and natural resource management.  Shared climate-related information via partner websites and other outlets.  Facilitated the creation of climate models, impact assessments, drought advisories and other products that incorporate climate data from multiple sources (including satellite, radar and ground observations) for policymakers, water managers and other users.  Expanded the AgriMet program and fostered cooperative relationships with private, tribal, local, state and federal agencies, making it the most commonly used source for climate data on farms in the northwestern U.S.  Created the PRISM Climate Group at Oregon State University, providing state-of-the-art weather and climate maps and products, which are downloaded over 30,000 times a day for use in agriculture, natural resource management, engineering, energy, economics and more.   How Are Climate Data & Tools Used?  Drought and flood mitigation: Climate data, tools and products helped the Arizona Governor’s Drought Task Force, Arizona Game and Fish, and local livestock producers plan for and respond to droughts. Arizona scientists also helped the Navajo, Hopi and White Mountain Apache tribes develop drought mitigation plans. California scientists worked with the National Weather Service, NASA, local water agencies and others to monitor snowpack and snowmelt and predict potential flood hazards and water supply issues. Three reservoir operators in California are using climate data to better control their water supply and reduce flood hazards. Proactive actions and early response can minimize the harmful impacts of droughts and floods on the environment, economy, and communities.  Farm and ranch management: After using AgriMet data to schedule irrigation, a potato grower in Idaho reported annual power savings between $14,000-17,000. Another grower in Idaho reported a 15% increase in potato yield after using AgriMet data for irrigation scheduling, resulting in increased revenue of $60,000. In Arizona, scientists helped wine grape growers access and use temperature data to protect grape yield and quality from frosts and high temperatures. Arizona scientists also worked with the USDA Southwest Climate Hub to develop best practices and tools for monitoring rangeland precipitation.  Crop insurance: PRISM’s weather and climate maps are delivered to approximately 6,000 crop insurance adjusters and underwriters nationwide. PRISM data have improved the quality and integrity of the USDA Risk Management Agency crop insurance program, saving taxpayer dollars by reducing inappropriate payments and improving insurance ratings.  Wildfire management: Nevada’s Desert Research Institute and Western Regional Climate Center collaborated with fire managers in Nevada and California to use the Evaporative Demand Drought Index for seasonal fire danger outlooks and real-time operations.  Storm prediction: California scientists worked with federal, local and academic collaborators to combine advanced observations, forecasts, modeling and other tools for storm hazard reduction.  Dust control: New Mexico State University weather stations and cameras provided the New Mexico Department of Transportation and the National Weather Service with valuable information on the sources of dust on I-10.  Lawn and garden irrigation: AgriMet is used as the main source of data for residential lawn “Smart Controllers” in the Northwest. These controllers apply water only when it is needed, helping homeowners save water and money.  Public health: Montana State University scientists helped develop a comprehensive report that details how climate change impacts the health of Montanans, both now and in the future.  Learn more about the Climate Data and Monitoring project here.
Multi State Research Efforts Focus on Climate Data & Monitoring
Texas Blackland Prairie Crop Yields
Texas Blackland Prairie Crop Yields
This visualization was created using data from the Gridded National Soil Survey Geographic Database via Microsoft’s Planetary Computer. Related blog
Texas Blackland Prairie Crop Yields
The Transformative Partnership Platform on Agroecology
The Transformative Partnership Platform on Agroecology
pspan style="font-size:16px;"A joint initiative to address critical knowledge gaps about agroecological transitions, to provide evidence to underpin advocacy and inform policy makers and donors about the potential of agroecological approaches to foster innovation that can sustainably improve livelihood and landscape resilience. /span/p
The Transformative Partnership Platform on Agroecology
Enterprise Neurosystem
Enterprise Neurosystem
Central Intelligence Platform This will be the core framework where the AI models reside and operate. This workstream proposes a self-describing digital asset catalog as a foundation for community use, and will eventually lead to a cross-correlation AI engine for deeper pattern analysis.  Secure AI Connectivity Fabric This track is building secure connectivity between AI models, data resources and the cross-correlation engine. It will use application layer messaging techniques found in other open source projects, in conjunction with a new policy engine
Enterprise Neurosystem
USDA ERS - U.S. Agricultural Baseline Projections
USDA ERS - U.S. Agricultural Baseline Projections
The Agricultural Baseline Database's Visualization tool allows users to illustrate the 10-year projection data, and is updated annually in November. The Visualization tool covers projections for major U.S. field crops (corn, sorghum, barley, oats, wheat, rice, soybeans, and upland cotton) and livestock (beef, pork, poultry and eggs, and dairy) commodities.
USDA ERS - U.S. Agricultural Baseline Projections
How well does digital soil mapping represent soil geography? An investigation from the USA
How well does digital soil mapping represent soil geography? An investigation from the USA
pstrong class="journal-contentHeaderColor"Abstract./strong We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %–95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil–landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor./p
How well does digital soil mapping represent soil geography? An investigation from the USA
Federal and State Listed Species in Texas
Federal and State Listed Species in Texas
In Texas, animal or plant species of conservation concern may be listed as threatened or endangered under the authority of state law and/or under the U.S. Endangered Species Act (ESA). Species may be listed as state threatened or endangered and not federally listed. The state list deals only with the status of the species within Texas.
Federal and State Listed Species in Texas
GBIF and Apache-Spark on Microsoft Azure tutorial
GBIF and Apache-Spark on Microsoft Azure tutorial
GBIF now has a snapshot of 1.3 billion occurrences✝ records on Microsoft Azure. It is hosted by the Microsoft AI for Earth program, which hosts geospatial datasets that are important to environmental sustainability and Earth science. Hosting is convenient because you could now use occurrences in combination with other environmental layers and not need to upload any of it to the Azure. You can read previous discussions about GBIF and cloud computing here. The main reason you would want to use cloud computing is to run big data queries that are slow or impractical on a local machine.
GBIF and Apache-Spark on Microsoft Azure tutorial
AI for Earth - Microsoft AI
AI for Earth - Microsoft AI
Microsoft AI for Earth empowers organizations and individuals working to solve environmental challenges. Learn more about AI for conservation.
AI for Earth - Microsoft AI
How to Build a Geospatial Lakehouse, Part 1
How to Build a Geospatial Lakehouse, Part 1
In this first part of a 2-part series, we explore the importance of geospatial data and analysis to a range of business use cases and how a data lakehouse is the best framework for extracting valuable insights at scale.
How to Build a Geospatial Lakehouse, Part 1