MI3MLSNF_2 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 3 FIRSTLOOK Global Land product in netCDF format covering a month version 2 data product. It contains a monthly statistical summary of directional hemispherical reflectance (DHR), photosynthetically active spectral region (DHR-PAR), DHR for near-infrared band (DHR-NIR), fractional absorbed photosynthetically active radiation (FPAR), DHR-based normalized difference vegetation index (NDVI) and land surface bidirectional reflectance factor (BRF) model parameters. It is classified into six vegetated and one non-vegetated types. This data product is a global summary of the Level 2 land/surface parameters of interest averaged over a month and reported on a geographic grid, with resolution of 0.5 degree by 0.5 degree. Data collection for this product is ongoing. This collection contains Leaf Area Index (LAI).

FIRSTLOOK processing uses the new time dependence of the Atmospheric and Surface Climatology (TASC) from the same month/previous year. The TASC data set now contains snow-ice and ocean surface wind speed values that are updated on a monthly basis. Therefore, these data sets cannot be generated until the end of the month. Products generated are distinguished by the presence of FIRSTLOOK in the file names. The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward, and four cameras pointing aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.

MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.

This Level 2 FIRSTLOOK Land Surface product contains directional reflectance properties, albedo(spectral and PAR integrated), FPAR, radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period.
MIL2ASLF_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 2 FIRSTLOOK Surface parameters version 2. It contains directional reflectance properties, albedo (spectral and photosynthetically active radiation (PAR) integrated), a fraction of photosynthetically active radiation absorbed by vegetation (FPAR), radiation parameters, and terrain-referenced geometric parameters produced using ancillary input from the previous time period. Data collection for this product is ongoing. This collection contains the Leaf Area Index (LAI).
Multi-angle Imaging SpectroRadiometer (MISR) Level 2 Aerosol data products contain information on the Earth's atmosphere. The aerosol data include tropospheric aerosol optical depth on 17. 6-km centers archived with a compositional model identifier and retrieval residuals, ancillary data including relative humidity, ozone optical depth, stratospheric aerosol optical depth, and retrieval flags. MISR multi-angle imagery will be used to monitor global and regional trends radiatively significant to optical properties (optical depth, single scattering albedo, and size distribution) and amounts (mass loading) of natural and anthropogenic aerosols, including those arising from industrial and volcanic emissions, slash-and-burn agriculture, and desertification. Coupled with MISR's determinations of top-of-atmosphere and surface albedos, these data will measure the global aerosol forcing of the shortwave planetary radiation budget. The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the exact surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.
MISR is designed to view Earth with cameras in 9 different directions. As the instrument flies overhead, all nine cameras successfully imaged each piece of Earth's surface below in 4 wavelengths (blue, green, red, and near-infrared). MISR aims to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.

This Level 2 Land Surface product contains directional reflectance properties,albedo(spectral & PAR integrated),FPAR, radiation parameters & terrain-referenced geometric parameters.

This dataset provides estimates of vegetation resilience in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at annual time steps for 2000-2019 and at 300-m spatial resolution. Vegetation resilience is defined as the recovery rate from deviations, due to climate perturbations or disturbances, to the equilibrium state. It is quantified as the negative temporal lag-1 autocorrelation of Enhanced Vegetation Index (EVI). Using a time series of MODIS EVI, the vegetation resilience was estimated using a Bayesian dynamic linear model. The mapped vegetation resilience was derived from Terra EVI products (MOD13Q1v061) across 175 ABoVE B grid tiles over the ABoVE core domain. The estimated mean resilience, upper boundary, and lower boundary results are provided for each tile in cloud optimized GeoTIFF (COG) format.

This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.

This dataset provides maps of vegetation types for the Atchafalaya and Terrebonne basins in coastal Louisiana, U.S., derived from NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) imagery acquired during spring and fall of 2021 for the Delta-X campaign. Vegetation types were classified from Level-2B BRDF-adjusted surface reflectance. Local pixel reflectance spectra coincident with herbaceous vegetation field samples and vegetation plot data from Louisiana's Coastwide Reference Monitoring System were used to generate a machine learning-based model to classify vegetation types. This model was then applied to the AVIRIS-NG mosaic imagery to map vegetation types across the Atchafalaya and Terrebonne Basins. The data are provided in cloud optimized GeoTIFF (COG) format.

This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.

The Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) activity originated in support of the NASA Earth System Observatory's Surface Biology and Geology (SBG) mission to develop prototype workflows with community algorithms and generate prototype data products envisioned for SBG. SISTER focused on developing a data system that is open, portable, scalable, standards-compliant, and reproducible. This collection contains EXPERIMENTAL workflows and sample data products, including (a) the Common Workflow Language (CWL) process file and a Jupyter Notebook that run the entire SISTER workflow capable of generating experimental sample data products spanning terrestrial ecosystems, inland and coastal aquatic ecosystems, and snow, (b) the archived algorithm steps (as OGC Application Packages) used to generate products at each step of the workflow, (c) a small number of experimental sample data products produced by the workflow which are based on the Airborne Visible/Infrared Imaging Spectrometer-Classic (AVIRIS or AVIRIS-CL) instrument, and (d) instructions for reproducing the sample products included in this dataset. DISCLAIMER: This collection contains experimental workflows, experimental community algorithms, and experimental sample data products to demonstrate the capabilities of an end-to-end processing system. The experimental sample data products provided have not been fully validated and are not intended for scientific use. The community algorithms provided are placeholders which can be replaced by any user's algorithms for their own science and application interests. These algorithms should not in any capacity be considered the algorithms that will be implemented in the upcoming Surface Biology and Geology mission.

This dataset provides a presentation that highlights the role NASA research and researchers played in developing a wide range of significant, quantitative ecological applications of satellite data. The presentation by Dr Diane E. Wickland, former NASA Terrestrial Ecology Program Manager and Lead for NASA Carbon Cycle and Ecosystems Focus Area, provides a top-level overview from her perspective of the development and evolution of the program. Dr Wickland joined NASA in 1985 to manage a newly formed Terrestrial Ecosystems Program. Along with other NASA program managers, she was charged with reorienting the program to be less empirical and have a greater focus on first principles, and to prepare for a next generation of earth-observing satellites. As an ecologist, she thought that focusing on important ecological questions and recruiting practicing ecologists to the program would facilitate such a change in directions. The presentation emphasizes the early years of U.S. satellite remote sensing and covers a few highlights after 2005.

This dataset provides classes of global forests delineated by status/condition in 2020 at approximately 30-m resolution. The data support generating Tier 1 estimates for Aboveground dry woody Biomass Density (AGBD) in natural forests in the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Forest classes include primary, young secondary (<=20 years), and old secondary forests (>20 years). Classification was based on a Boolean combination of a suite of existing Earth Observation (EO) products of forest tree cover, height, age, and land use classification layers representing years 2000 to 2020. This forest status/condition classification prioritizes the reduction of potential errors of commission in the delineations by minimizing the inclusion of ambiguous pixels. Hence, it provides a conservative estimate of global forest area, identifying approximately 3.26 billion ha of forests worldwide. The classification was created on the collaborative open-science cloud-computing system, the ESA-NASA Multi-mission Analysis and Algorithm Platform (MAAP). The data are provided in cloud-optimized GeoTIFF format.