Tools tagged with "MODIS"

This resource provides a curated collection of geospatial data portals and datasets specifically focused on Michigan and the Great Lakes. It includes links to state-level open data, imagery, transportation data, and various water-related resources. The guide also features data for specific regions like Southeast Michigan and the City of Detroit.

SatPM2.5 provides comprehensive estimates of fine particulate matter (PM2.5) concentrations by integrating aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS) with simulation models (GEOS-Chem) and ground-based measurements. This geophysical estimate is further refined through statistical fusion with direct PM2.5 measurements to explain most of the variance observed. The dataset is updated yearly and is available under a Creative Commons Attribution 4.0 International license.

Google Earth Engine provides a comprehensive data catalog for scientists, researchers, and developers. It combines a vast collection of satellite imagery and geospatial datasets with powerful analysis tools. Users can detect changes, map trends, and quantify differences on the Earth's surface at a planetary scale.

This platform provides global, remote-sensing supported environmental layers crucial for assessing biodiversity, ecosystems, and climate. It features a unique 1-km cloud cover dataset derived from 15 years of twice-daily MODIS satellite observations. The data offers insights into spatio-temporal cloud cover dynamics for ecological research and habitat mapping.

Satpy is an open-source Python library designed for reading, processing, and visualizing data from various earth-observing satellites. It provides a flexible framework for handling different satellite instruments, enabling users to generate composites, apply atmospheric corrections, and perform re-sampling operations. This tool is invaluable for researchers and developers working with meteorological and environmental satellite imagery.

The Global Land Cover by National Mapping Organizations (GLCNMO) provides geospatial land cover information in raster format, classifying the entire globe into 20 categories based on FAO's LCCS. Developed through international collaboration, it offers three versions (2003, 2008, 2013) using MODIS data, allowing for comparisons and integration with other LCCS-based products.

NASA's Applied Remote Sensing Training (ARSET) program provides free online and in-person webinars and workshops. It aims to empower individuals and organizations to leverage Earth-observing data for informed environmental management and decision-making across various applications globally.

This platform provides access to a variety of geospatial datasets focusing on the Antarctic. It serves as a central hub for researchers and users to explore and download critical environmental data, including satellite imagery and ice-related products. The service aims to support monitoring and analysis of the dynamic polar environment.

This resource offers global datasets quantifying spatial heterogeneity of terrestrial habitats. Utilizing textural features from MODIS Enhanced Vegetation Index (EVI) imagery, it provides 14 distinct metrics at multiple resolutions. These layers are crucial for researchers and modelers focused on biodiversity and ecosystem assessments.

This website serves as a central hub for data collected by the MODIS instruments aboard NASA's Terra and Aqua satellites. It offers a wide array of raw and processed data products covering Earth's atmosphere, land, cryosphere, and oceans. Users can access scientifically validated data for various environmental and climate research applications.

This GitHub repository compiles a comprehensive list of open geospatial datasets hosted on Amazon Web Services (AWS). It serves as a valuable resource for developers, researchers, and data scientists seeking readily available geospatial data for various applications and projects, streamlining access to critical information.

Pytroll is an easy-to-use, modular, free, and open-source Python framework designed for the processing of Earth Observation (EO) satellite data. Its packages are suitable for both research and development (R&D) environments and 24/7 operational production. The framework focuses on atmospheric applications and imaging sensors, supporting a wide range of Earth sciences.

NASA's Land Processes Distributed Active Archive Center (LP DAAC) offers a vast array of Earth science data. This center is essential for researchers and scientists investigating, characterizing, and monitoring biological, geological, hydrological, and ecological conditions and processes. It serves as a key resource within the NASA Earthdata ecosystem for land science.