Global biodiversity data

The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues * Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU). Fish species are based on Froese, R. and Pauly, D. (eds). 2008.

The Leuser Ecosystem spans the provinces of Aceh and North Sumatra on the island of Sumatra in Indonesia. Over 35 times the size of Singapore, this majestic and ancient ecosystem covers more than 2.6 million hectares of lowland rainforests, peat swamps, montane and coastal forests and alpine meadows. Globally recognized as one of the richest expanses of tropical rainforest found anywhere in Southeast Asia, the Leuser Ecosystem is also one of Asia’s largest carbon sinks. The Leuser Ecosystem is the last place on earth where orangutans, rhinos, elephants, and tigers co-exist in the wild. All four of these species are now classified by the International Union for Conservation of Nature (IUCN) as Critically Endangered. The Leuser Ecosystem is the only remaining habitat left in Sumatra large enough to sustain viable populations of these species. A publication in leading international journal Science listed the Leuser Ecosystem as “one of the world’s foremost irreplaceable areas.” The Leuser Ecosystem is an essential asset for the economic development of Aceh, providing a total economic value of at least 350 million US dollars per year. The Leuser Ecosystem acts as a life-support system for approximately four million people in Aceh. The primary ecosystem services are fresh water provision and disaster mitigation. The forests of the Leuser Ecosystem act as a sponge, soaking up the downpours of the rainy season and spreading out the release of water downstream more evenly across the months. Deforestation of this environmentally sensitive area is having a dramatic impact by increasing the damage caused by flooding and landslides, and causing economic damage to communities and downstream industry. Locally and globally, the Leuser Ecosystem also has immense environmental value due to its role in climate regulation and carbon storage. Efforts to conserve the Leuser Ecosystem date as far back as the early 19th century, when the traditional leaders of Aceh lobbied the colonial government to protect their natural heritage, ranging from the mountains all the way down to the coast. More recent laws have served to strengthen the protection of the Leuser Ecosystem and placed the responsibility for managing its protection and restoration with the Aceh Provincial Government (Article 150 of National Law on Governing Aceh No. 11/2006). Furthermore, the Leuser Ecosystem in Aceh has special legal status as a National Strategic Area for its Environmental Protection Function (26 of 2007 juncto 26/2008), prohibiting any activities that reduce that function, including cultivation and infrastructure development. Displays the boundary of the Leuser Ecosystem, a National Strategic Area for its Environmental Protection Function.

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). Data users are encouraged to transition to the equivalent VJ213A3 (primary) or VJ113A3 (secondary) data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. This product includes a total of 15 variables, including three Vegetation Indices layers, NIR reflectance layers; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A3 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

The Intact Forest Landscapes (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat satellite imagery for the year 2000.  Changes in the extent of IFLs were identified within year 2000 IFL boundary using  Landsat Analysis Ready Data (ARD), the global forest cover loss dataset (Hansen et al., 2013), Sentinel-2 imagery, and high resolution data from Google Earth and Planet. Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2025. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.  This data can be used to assess forest intactness, alteration, and degradation at global and regional scales. Identifies the world’s last remaining unfragmented forest landscapes, large enough to retain all native biodiversity and showing no signs of human alteration as of the year 2020. This layer also shows the reduction in the extent of Intact Forest Landscapes from 2000 to 2025.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP30 VI data product is provided monthly at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP30 VI product contains 12 Science Datasets (SDS), which include the calculated VIs (NDVI and EVI2) as well as quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. The VIP30 dataset consists of 12 monthly composites annually representing each calendar month of the year. Known Issues * The Relative Azimuth Angle (RAA) for the input MODIS data is computed based on absolute values of the finer resolution pixels resulting in positive values and has minor usefulness. * The RAA for the input AVHRR data contain values in the -360° to 360° range. The routine to restrict the values in the -180° to 180° range was accidentally missed and can be corrected using the following routine described in Section 4.2.1 of the User Guide and Algorithm Theoretical Basis Document: * SinRelativeAz=sin(RAA) * CosRelativeAz=cos(RAA) * Correct-RAA = atan2(SinRelativeAz,CosRelativeAz)

This layer combines multiple forest roads datasets covering six Congo Basin countries: Cameroon, Central African Republic, Democratic Republic of Congo, Equatorial Guinea, Gabon, and Republic of Congo. The pre-2019 roads data are from Kleinschroth et al (2019) which combines crowd-sourced data from Open Street Map (OSM), manually digitized roads from the Logging roads initiative led by Moabi, the Joint Research Center of the European Commission and Global Nature Watch, data from Laporte et al. (2007), who manually digitized Landsat images from 1970-2003, and earlier efforts from Kleinschroth et al. (2017) for certain regions. Roads after 2019 are mapped by Slagter et al. 2024, with an advanced detection method based on Sentinel-1 and Sentinel-2 satellite imagery and a deep learning model. This integration serves to offer a more comprehensive forest roads dataset for the Congo Basin. While the machine learning techniques enhance our ability to detect and map roads going forward, the older data holds value as it captures signals from roads that may no longer be visible or detectable today. This information can still provide context for users and insight into forest intactness, past logging activities, and the human footprint. The automated road detection algorithm from 2019 onwards implements both the Sentinel-2 optical sensors, which provide detailed imagery during clear weather conditions, as well as the Sentinel-1 radar sensors which can ‘see through’ clouds during persistent rainy seasons in the tropics. Therefore, the opening of commonly narrow and transient road segments can be precisely located on a monthly basis. Some old, overgrown roads—often 20–30 years old and present in the Laporte/Kleinschroth dataset—are detected again by WUR when they are reopened for a new logging cycle. This accounts for about one-third of WUR’s road detections. For some users, this distinction is valuable, as reopening old logging roads generally has less environmental impact than constructing entirely new ones. In addition, it provides an indication where new harvest cycles are occurring in previously logged forests. Intersections between new and old roads in the data layer on GNW may indicate such reopenings. However, due to differences in detection/collection methodology, these roads may not perfectly align spatially, even when they represent the same underlying feature. To download recent forest roads from WUR (>2019): Forest roads (Congo Basin) (zenodo.org) To download historical forest roads (<2019): Data from: Road expansion and persistence in forests of the Congo Basin - Research Collection (ethz.ch) Read the blog here: https://www.globalnaturewatch.org/blog/data-and-tools/congo-basin-forest-road-mapping/ Shows the development of forest roads in the Congo Basin

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Forest Cover Change Multi-Year Global dataset provides estimates of changes in forest cover from 1990 to 2000 and from 2000 to 2005 at 30 meter spatial resolution. The GFCC30FCC product represents a global record of fine-scale changes in forest dynamics between observation periods. The forest cover change product was generated from the GFCC Tree Cover (GFCC30TC) product which is based on Global Land Survey (GLS) data acquired by the Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensors. Each forest cover product has two GeoTIFF files associated with it; a change map file and a change probability file. Data follow the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

The tropical tree cover data maps tree extent at the ten-meter scale and tree cover at the half hectare scale to enable accurate monitoring of trees in urban areas, agricultural lands, and in open canopy and dry forest ecosystems. The data extends over 4.3 billion hectares of the global tropics. The data is derived from multi-temporal convolutional neural network models applied to Sentinel optical and radar imagery. The 10-meter dataset is a binary tree extent layer that is similar to a land cover map, while the tree cover data represents fractional cover at a half-hectare scale. More details on the methodology and analyses can be found on the GitHub page. Displays tree extent at the ten-meter scale and tree cover at the half hectare scale to enable accurate monitoring of trees in urban areas, agricultural lands, and in open canopy and dry forest ecosystems

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting. The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024. The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters. The GEDI02_B data product contains 198 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary. Known Issues * Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8). * Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers. * Known Issues: Section 8 of the User Guide provides additional information on known issues.

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113C1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 0.05 degree resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. This product contains a total of 18 variables. Along with the three Vegetation Indices variables, this product also includes variables for the standard deviations of each Vegetation Index; NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; number of pixels, number of pixels used; pixel reliability; average sun angle, and a quality variable. Two low resolution browse images are also available for each VJ113C1 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) Gap-Filled (VJ217A2GF) Version 2 product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ217A2GF is an 8-day composite at 500 meters (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VJ217A2GF will be generated at the end of each year when the entire yearly 8-day VJ215A2H is available. Hence, the gap-filled VJ217A2GF is the improved VJ217A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VJ217A2GF in near-real time because it will be generated only at the end of a given year. Provided in the VJ217A2GF product are variables for GPP, PSN, along with a quality control variable. A low resolution browse image for GPP is also available for each VJ217A2GF granule. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.

The VCD19A3CMG Version 2 data product is a combined NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Index and Snow Products Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The VCD9A3CMG product is corrected for atmospheric gases and aerosols using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that is based on a time series analysis and a combination of pixel- and image-based processing. The VCD19A3CMG Vegetation Index and Snow product contains variables for NDVI (Normalized Difference Vegetation Index), NDVI normalized to a fixed geometry, gap_filled daily NDVI for forward and backward scattering, daily Enhanced Vegetation Index (EVI) normalized to a fixed geometry, snow fraction, and snow grain size. Known Issues * MAIAC Lookup Tables (LUTs) are built assuming pseudo-spherical correction in single scattering which has a reduced accuracy for high sun/view zenith angles. A reduced MAIAC performance is expected at solar zenith angles > 78°. * MAIAC may be missing bright salt pans in several world deserts. In such cases, it may generate a persistent high Aerosol Optical Depth (AOD) resulting in missing surface retrievals. * Because of inherent uncertainties of gridding on the coastline, the area of 1-3 pixels from the coastline may contain frequent artifacts in cloud mask (usually over-detection), AOD (higher values) and surface BRF. Users should exercise caution near the coastline as represented by the AOD QA bit 1010 (See Table 5.4 of the User Guide bits 8-11, 1010 -- AOD within +-2 km from the coastline is replaced by nearby AOD). * Atmospheric Correction (AC) over detected snow: as MAIAC does not retrieve AOD over snow, it assumes a low climatology AOD=0.05 globally and 0.02 at high elevations (H>4.2 km). Over north-central China, which is often heavily polluted and low AOD assumption can lead to a significant bias, we use AOD averaged over mesoscale area of 150 km using reliable AOD retrievals over snow-free pixels. Such approach does improve quality of AC as compared to low-AOD assumption as judged by the reduced boundaries and color artifacts, but it does not account for the aerosol variability inside 150 km area. * Ice mask is currently unreliable

Dates of Images: May 5, 2022 Date of Next Image: None Summary: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/2B satellites Resolution: 10 meters Credits: NASA/MSFC, USGS, ESA Copernicus REST Endpoint: See URL on the right. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2603_202205_FLOOD_MN/ex2603_sentinel2_ndvi/ImageServer/WMSServer

The MCD19A3CMG Version 6.1 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-Angle Implementation of Atmospheric Correction (MAIAC) Vegetation Index Level 3 product produced daily in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). The MCD19A3CMG product provides Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at ground level in the absence of atmospheric scattering or absorption. The MCD19A3CMG Vegetation Index data product contains the following Science Dataset (SDS) layers: NDVI, NDVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view, gap-filled NDVI, EVI, and EVI normalized to a fixed geometry of solar zenith angle at 45° and nadir view. A low-resolution browse image is also included showing NDVI created using a composite of all available orbits. Known Issues * Known issues are described in Section 6 of the User Guide. * An artifact was discovered in the EVI, NDVI, and NDVI_gapfill layers over the entire swath of southern polar region. The affected region shows false retrieval of data during the summer months. The artifacts occurred over permanently snow-covered areas where no meaningful NDVI retrieval is expected. A software fix was implemented for forward processing in June 2023. Existing data products will not be reprocessed. Affected dates are listed on the Land Data Operational Products Evaluation Quality Assessment note. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). Data users are encouraged to transition to the equivalent VJ213A1 (primary) or VJ113A1 (secondary) data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VNP13A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VNP13 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VNP13 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. This product includes a total of 16 variables, including three Vegetation Indices layers, layers for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality layer. Two low resolution browse images are also available for each VNP13A1 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A3) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a monthly acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. This product contains a total of fifteen variables. Along with the three Vegetation Indices variables, this product also includes variables for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; pixel reliability; pixel reliability; relative azimuth, view, and sun angles; and a quality variable. Two low resolution browse images are also available for each VJ113A3 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meters (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data.

RAdar for Detecting Deforestation (RADD) is a deforestation alert product that uses data from the European Space Agency’s Sentinel-1 satellites to detect forest disturbances in near-real-time and to confirm alerts within weeks. The RADD alerts use a detection methodology produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing. These alerts are particularly advantageous in monitoring tropical forests, as Sentinel-1’s cloud-penetrating radar and frequent revisit times (6-12 days) allow for more consistent monitoring than alert products based on optical satellite images. Alerts are available for the primary humid tropical forest areas of South America, sub-Saharan Africa and Southeast Asia at 10 m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America, Central America, and Southeast Asia. Pre-processed Sentinel-1 images are collected from Google Earth Engine, then quality controlled and normalized using historical time-series metrics. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image, and then marked as high confidence with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. Unconfirmed alerts are provided for forest disturbance probabilities above 85%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. Alerts are detected within areas of primary humid tropical forest, defined by  Turubanova et al (2018) and with 2001-2018 forest loss (Hansen et al. (2013)) and mangrove (Bunting et al. 2018)  removed. For more information on methodology and validation, please refer to Reiche et. al. (2021). The version presented here (v1) has been updated from that described in the paper (v0), with changes to the forest mask and a reduction of the minimum mapping unit.   The RADD alerts were made possible thanks to the support of a coalition of ten major palm oil producers and buyers. Under the project, Wageningen University and Research (WUR) developed the detection method and Satelligence first scaled the system in Indonesia and Malaysia and provided additional prioritization of alerts for on-the-ground follow up. Additional support was provided by the US Forest Service and Norway’s International Climate and Forest Initiative. The alerts are currently operated by WUR using Google Earth Engine.   The RADD alerts are available on \*\*Google Earth Engine\*\* with asset ID: projects/radar-wur/raddalert/v1  Monitor primary forest disturbance in near-real-time using Sentinel-1’s cloud-penetrating radar sensors

This dataset enhances pantropical near-real-time forest disturbance monitoring systems by using a deep learning model to classify direct drivers of forest disturbances on a monthly basis. Produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing, with support from Global Nature Watch (GNW), the dataset is built upon GNW's integrated disturbance alerts, which contains WUR's RADD (Radar for Detecting Deforestation) alerts and University of Maryland's GLAD (Global Land Analysis & Discovery) alerts, harnessing post-disturbance imagery from Sentinel-1 and Sentinel-2 satellites. Only high and highest confidence alerts are classified. This approach enables the identification of these direct causes driving deforestation: small-scale agriculture, large-scale agriculture, road development, selective logging, mining, wildfire, flooding, and other natural disturbances. The alert drivers offer more targeted enforcement of laws and regulations, improved estimations of ecological impacts, and better understanding of carbon emissions related to forest disturbances. Read the paper here: Rapid satellite monitoring reveals complexity of tropical tree cover loss drivers at fine scale | Research Square **Alert Driver class definitions** **Non-natural drivers (human-caused)** **Small-scale agriculture**: Clearings smaller than 2 ha, commonly related to shifting cultivation (temporary clearing for cultivation with later regrowth) or smallholder farming. In smallholder landscapes with mixed disturbances, this may include artisanal logging and fuelwood collection (typically burned for cooking).  **Small-scale agriculture with fire**: Clearings for small-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio **Large-scale agriculture**: Clearings larger than 2 ha for the establishment of crops or pastures, commonly related to industrial agriculture (e.g. production of soy, palm oil, beef, etc.), clearcuts, and large-scale clearings for land speculation. **Large-scale agriculture with fire**: Clearings for large-scale agriculture (see above) where fire was likely used for the clearing, observed by a coinciding VIIRS fire alert and low post-disturbance Sentinel-2 Normalized Burn Ratio **Road development**: Clearings for the establishment of roads, commonly related to facilitating industrial timber harvests, but can include roads for any purpose.  **Selective logging**: Small-scale disturbances caused by selective tree felling and skidding (paths where felled logs are dragged or transported), commonly related to industrial timber harvests.  **Mining**: Forest clearing to facilitate artisanal and industrial mineral extraction. **Natural drivers** **Flooding**: Disturbances or clearings caused by floodings and meandering rivers. This includes both natural and human-induced flooding. **Other natural disturbances**: Disturbances or clearings without visible human-induced cause. This includes windthrows, droughts, landslides and naturally dying trees. **Other drivers** **Wildfire**: Large-scale disturbances due to fire, without immediate land clearing for agricultural activity. This includes both human-induced and naturally induced fires. This class excludes controlled fires used for agricultural clearing, but includes escaped wildfires caused by controlled fires. **Unlabeled**: Pixels where a confidence threshold for a prediction is not reached. The dataset covers the primary humid tropical forest (Turubanova et al. 2018) of the Amazon (Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname and Venezuela, restricted to the biogeographic outlines of the Amazon biome as defined by RAISG), Congo Basin (Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Republic of the Congo) and Southeast Asia (Brunei, East- Timor, Indonesia, Malaysia and Papua New Guinea). The model was trained on a dataset of labeled disturbance alerts, interpreted with high-resolution Planet imagery across the Amazon, Congo Basin, and Southeast Asia. The model integrates the complementary strengths of radar (Sentinel-1) and optical (Sentinel-2) imagery, leveraging convolutional neural networks (CNNs) to classify disturbances. Even though the dataset initially displayed historical alert data (2022-2025), the classifications were applied in a simulated near-real-time monitoring scenario to classify monthly disturbance alerts into one of the specified classes, achieving an overall accuracy of 0.89 Macro-F1. Accuracy varies by region, with the Congo Basin performing best. After the addition of the DIST-ALERT product, there are more alerts and therefore, areas of misclassification may be larger. Most misclassifications stem from confusion between agriculture and mining, which often co-occur, and between flooding and wildfires. Historical drivers (2022-2025) were classified up to three times within the three months following each alert, with the most confident result retained. These 2022-2025 classifications were postprocessed by selecting per disturbance patch the most occurring driver class, leading to some initial classifications being overwritten by another class in postprocessing. The current method, running from January 2026 onwards, selects the most occurring driver class only for disturbance patches within the three-month windows, As a result, an alert can be updated up to 3 times, and the latest classification is retained. Classifications may therefore change for up to 4 months after the initial alert and do not change after that. . A class ‘unconfident’ is built from pixels that do not exceed a 0.5 class probability from the classification output. When the most occurring class for a disturbance patch is ‘unconfident’, this alerts remains unlabeled. Post-processing steps identified where fire coincided with the clearing of agricultural land, based on the presence of VIIRS fire alerts (within a 500 m buffer around the alert and a 6-month window) and a low post-disturbance normalized-burn ratio (lower than 0.0) in the following month’s Sentinen-2 composite. Smoothing was applied to all classes except road development and selective logging, which occur at finer scales. For patches smaller than 5 km, the dominant driver was assigned. For larger patches in the 2022-2025 data, pixel-level classifications were retained but smoothed with a 5 × 5-pixel majority filter. This step was applied to the whole time series; therefore, the historical product has an advantage in differentiation between small- and large-scale agriculture compared to the operational product. In a near-real-time setting, large clearings may initially be labeled as small-scale until they exceed the 2 ha threshold. In the future, monthly driver attributions for disturbance alerts will expand to new regions. The driver attributions will be continuously mapped and openly distributed via Global Nature Watch, as an extension to the integrated disturbance alerts. We also provide an annual version of this dataset, which incorporates some post processing – specifically the differentiation between large-scale and small-scale agriculture is made at the end of the year, meaning that agricultural clearings which are identified incrementally are identified based on their size at the end of the year. See the integrated disturbance alert layer metadata for additional details about the disturbance alerts. The alert drivers are available on Google Earth Engine with asset IDs: **Latest data** (ee.ImageCollection containing data from January 1, 2026 – end of last month): - projects/wurnrt-drivers/assets/operational_postprocessed_sa_collection - projects/wurnrt-drivers/assets/operational_postprocessed_afr_collection - projects/wurnrt-drivers/assets/operational_postprocessed_sea_collection *Note: this collection updates monthly, so new images will be added over time, but the collection path itself will stay the same.* **Historical archive** (ee.Image containing data from 2022–2025): - projects/wurnrt-drivers/assets/distribution/driverclassification_afr_202201_202512 - projects/wurnrt-drivers/assets/distribution/driverclassification_sa_202201_202512 - projects/wurnrt-drivers/assets/distribution/driverclassification_sea_202201_202512 Assigns probable drivers to the integrated disturbance alerts using machine learning and Sentinel imagery

Net primary production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: gC/m² Scale Factor : 0.001 Map code : L2-NPP-D Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily: Incoming solar radiation and temperature data (Tmin/Tmax); - Monthly: fAPAR and soil moisture stress; - Seasonal: Land Cover. Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

This dataset was generated within the framework of the Global Mangrove Watch (GMW), an initiative convened by Aberystwyth University, soloEO, The Nature Conservancy and Wetlands International. The larger dataset (v3.0) depicts the global extent of mangrove forests for the years 1996, 2007-2010, and 2015-2020, derived by L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA), thus developing a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. The classification was confined using a mangrove habitat mask, which defined regions where mangrove ecosystems can be expected to exist. The mangrove habitat definition was based on geographical parameters such as latitude, elevation and distance from ocean water. The habitat mask was initially developed in GMW v2.0 and has been revised in subsequent versions. Global coverage of mangroves for 2020

The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ213A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VJ213 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ213 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices variables, this product also includes variables for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality variable. Two low resolution browse images are also available for each VJ213A2 product: EVI and NDVI. Known Issues: • For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). The daily NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Burned Area (VNP64A1) Version 2 data product is a monthly, global gridded 500-meter (m) product containing per-pixel burned area and quality information. The VNP64 burned area mapping approach employs 750 m VIIRS imagery coupled with 750 m VIIRS active fire observations. The hybrid algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive Vegetation Index (VI) derived from VIIRS shortwave infrared channels M8 and M11, and a measure of temporal texture. VIIRS bands that are both sensitive and insensitive to biomass burning are used to detect changes caused by fire and to differentiate them from other types of change. The mapping algorithm ultimately identifies the date of burn, to the nearest day, for 500 m grid cells within the individual sinusoidal tile being processed. The date is encoded in a single data layer of the output product as the ordinal day of the calendar year on which the burn occurred (range 1–366), with a value of 0 for unburned land pixels and additional values reserved for missing data and water grid cells. The VNP64A1 data product is designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined burned area product to promote the continuity of the Earth Observation System (EOS) mission. The data layers provided in the VNP64A1 product include Burn Date, Burn Date Uncertainty, and Quality Assurance (QA), along with First Day and Last Day of reliable change detection of the year. A low resolution browse is also provided showing the burned date layer with a color map applied in JPEG format. Known Issues * Additional information on known issues is provided in Section 8 of the User Guide and on the MODIS/VIIRS Land Quality Assessment website.

The NOAA-20 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ113A2) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 1 kilometer (km) resolution. The VJ113 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ113 algorithm process produces three vegetation indices: The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices variables, this product also includes variables for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles, and a quality variable. Two low resolution browse images are also available for each VJ113A2 product: EVI and NDVI. Known Issues * Due to missing critical inputs, this product lacks coverage for tiles h33v07 and h18v14, which are located over water. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

This data set shows several different types of protected areas in Mexico. National protected areas are managed by CONANP, while state and municipal protected areas are managed by state and municipal agencies. Voluntary conservation areas are lands that private property owners have voluntarily certified as protected for a minimum of 15 years. Shows the location of Mexico’s national, state, and municipal protected areas as well as voluntary conservation areas

This product shows the dominant driver of tree cover loss from 2001-2025. A driver is defined as the direct cause of tree cover loss, and can include both temporary disturbances (natural or anthropogenic) or permanent loss of tree cover due to a change to a non-forest land use (e.g., deforestation). The dominant driver is defined as the direct driver that caused the majority of tree cover loss within each 1 km cell over the time period. Classes are defined as follows:    - Permanent agriculture: Long-term, permanent tree cover loss for small- to large-scale agriculture.   - Hard commodities: Loss due to the establishment or expansion of mining or energy infrastructure.  - Shifting cultivation: Tree cover loss due to small- to medium-scale clearing for temporary cultivation that is later abandoned and followed by subsequent regrowth of secondary forest or vegetation.  - Logging: Forest management and logging activities occurring within managed, natural or semi-natural forests and plantations, often with evidence of forest regrowth or planting in subsequent years.   - Wildfire: Tree cover loss due to fire with no visible human conversion or agricultural activity afterward. Fires may be started by natural causes (e.g. lightning) or may be related to human activities (accidental or deliberate).  - Settlements and infrastructure: Tree cover loss due to expansion and intensification of roads, settlements, urban areas, or built infrastructure (not associated with other classes).  - Other natural disturbances: Tree cover loss due to other non-fire natural disturbances (e.g., landslides, insect outbreaks, river meandering). If loss due to natural causes is followed by salvage or sanitation logging, it is classified as logging.    These data were produced in a collaboration between the World Resources Institute and Google DeepMind. The data were developed using a global neural network model (ResNet) trained on a set of samples collected through visual interpretation of very high-resolution satellite imagery. The model used satellite imagery (Landsat 7 & 8, Sentinel-2) and ancillary data to classify the seven driver categories. Overall accuracy of the model is 90.5%, with regional accuracies varying from 82.8% in Southeast Asia to 94.1% in Asia. Global per class producer’s and user’s accuracy are highest for the permanent agriculture, logging, and wildfire classes (over 90%), and generally lower for rarer classes, such as hard commodities, settlements and infrastructure, and other natural disturbances. A full description of the methods and accuracy statistics are available in the publication.     The data is also available on Google Earth Engine.  Shows the dominant driver of tree cover loss within each 1 km grid cell and the intensity of loss over the time period

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A1) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The delivery of all Suomi National Polar-orbiting Partnership (Suomi NPP) VIIRS science data products will cease on November 2, 2026 (2026/306). Data users are encouraged to transition to the equivalent VJ217A3GF (primary) or VJ117A3GF (secondary) data products. The NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Gross and Net Primary Production (NPP) Gap-Filled (VNP17A3GF) Version 2 product is a cumulative composite of Gross Primary Productivity (GPP) values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VNP17A3GF is a yearly composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. The VNP17A3GF will be generated at the end of each year when the entire yearly 8-day VNP15A2H is available. Hence, the gap-filled VNP17A3GF is the improved VNP17A2, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get VNP17A3GF in near-real time because it will be generated only at the end of a given year. Provided in the VNP17A3GF product are three variables for GPP, NPP, along with a quality control variable. A low resolution browse image for GPP is also available for each VNP17A3GF granule. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

The Land Processes Distributed Active Archive Center (LP DAAC) archives and distributes Global Forest Cover Change (GFCC) data products through the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. The GFCC Water Cover 2000 Global dataset provides surface-water information at 30 meter spatial resolution. This dataset was derived from waterbodies in the GFCC Tree Cover (GFCC30TC) and Forest Cover Change (GFCC30FCC) products based on a classification-tree model. Data are available for selected dates between June 1999 and January 2003. GFCC30WC follows the Worldwide Reference System-2 tiling scheme. Additional details regarding the methodology used to create the data are available in the Algorithm Theoretical Basis Document (ATBD).

The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Vegetation Indices (VJ213A1) Version 2 data product provides vegetation indices by a process of selecting the best available pixel over a 16-day acquisition period at 500 meter (m) resolution. The VJ213 data products are designed after the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Vegetation Indices product suite to promote the continuity of the Earth Observation System (EOS) mission. The VJ213 algorithm process produces three vegetation indices: Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Enhanced Vegetation Index-2 (EVI2). NDVI is one of the longest continual remotely sensed time series observations, using both the red and near-infrared (NIR) bands. EVI is a slightly different vegetation index that is more sensitive to canopy cover, while NDVI is more sensitive to chlorophyll. EVI2 is a reformation of the standard 3-band EVI, using the red band and NIR band. This reformation addresses arising issues when comparing VIIRS EVI to other EVI models that do not include a blue band. EVI2 will eventually become the standard EVI. Along with the three Vegetation Indices variables, this product also includes variables for NIR reflectance; three shortwave infrared (SWIR) reflectance; red, blue, and green reflectance; composite day of year; pixel reliability; relative azimuth, view, and sun angles; and a quality variable. Two low resolution browse images are also available for each VJ213A1 product: EVI and NDVI. Known Issues: * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website and the User Guide and ATBD.

Net Primary Production (NPP) is a fundamental characteristic of an ecosystem, expressing the conversion of carbon dioxide into biomass driven by photosynthesis. The pixel value represents the mean daily NPP for that specific dekad. The data is provided from January 2009 to 2023. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: g/m² Map code: L1-NPP-D Scale Factor: 0.001 Conversion factor: the pixel value in the downloaded data must be multiplied by 0.001 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 4.0 IGO **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** The core of the methodology for deriving NPP is detailed in Veroustraete et al. (2002), whilst the practical implementation, as developed for the MARS Crop Yield Forecasting System, is described in Eerens et al. (2004). These methodologies were improved within the framework of the Copernicus Global Land Component, the most important change being the incorporation of biome-specific light-use efficiencies (LUEs). The FRAME project applies this updated methodology, adding improvements which include the addition of a reduction factor to account for reduced water availability (i.e. soil moisture stress). The following data is used to calculate NPP: - Daily:incoming solar radiation and temperature data (Tmin/Tmax); - Dekadal:fAPAR and soil moisture stress; - Seasonal:Land Cover. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

The long-term NDVI quality layer is produced as a summary of the quality for the entire time period (2009 to 2022). Pixel values indicate the percentage of good NDVI observations over the entire time period. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: Percentage (%) Scale Factor: 1.0 Map code: L1-QUAL-NDVI-LT **Citation:** FAO 2018. WaPOR Database Methodology: Level 1. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC BY 43.0 IGO **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** While the dekadal NDVI Quality layer shows the quality of the data for a specific time of year, the long-term NDVI quality layer has been added to give users a quick overview of the areas that tend to suffer more cloud cover. **Resource constraints:** Creative Commons Attribution 4.0 International (CC BY 4.0) **Online resources:** Download the data from Google Cloud Storage

The NOAA-21 Visible Infrared Imaging Radiometer Suite (VIIRS) Gross Primary Productivity (GPP) and Net Photosynthesis (PSN) (VJ217A2) Version 2 data product is a cumulative composite of GPP values based on the radiation use efficiency concept that is potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. VJ217A2 is an 8-day composite at 500 meter (m) spatial resolution delivered as a gridded Level 4 (L4) product in Sinusoidal projection. Provided in the VJ217A2 product are layers for GPP, PSN, along with a quality control layer. A low resolution browse image for GPP is also available for each VJ217A2 granule. Known Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

This map shows the rate at which forests could capture carbon from the atmosphere and store it in aboveground live biomass over the first 30 years of natural forest regrowth. It was created by combining ground-based measurements at thousands of locations around the world with 66 co-located environmental covariate layers in a machine learning model to produce a wall-to-wall map. Forest plot data used to train the model are sourced from published literature, which can be found in the Forest Carbon database (ForC, maintained by the Smithsonian Institute (https://github.com/forc-db), as well as georeferenced data from publicly available national forest inventories. Although rates were estimated over all forest and savanna biomes globally, they are filtered here by “reforestable” area, as defined in Griscom et al. 2017 (PNAS). Reforestable areas exclude areas of native grasslands and croplands to safeguard the production of food and fiber and habitat for biological diversity.  Estimates the rate at which carbon could be sequestered in aboveground live biomass during the first thirty years of natural forest regrowth in potentially reforestable areas (Mg carbon/ha/yr).