The Cropland Data Layer (CDL), hosted on CropScape, provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. The data is created annually using moderate resolution satellite imagery and extensive agricultural ground truth. Users can select a geographic area of interest or import one, then access acreage statistics for a specific year or view the change from one year to another. The data can be exported or added to the CDL. The information is useful for issues related to agricultural sustainability, biodiversity, and land cover monitoring, especially due to extreme weather events. Resources in this dataset:Resource Title: CropScape and Cropland Data Layer - National Download. File Name: Web Page, url: https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php Downloads available as zipped files at https://www.nass.usda.gov/Research_and_Science/Cropland/Release/index.php --

National CDL's -- by year, 2008-2020. Cropland Data Layer provides a raster, geo-referenced, crop-specific land cover map for the continental United States. The CDL also includes a crop mask layer and planting frequency layers, as well as boundary, water and road layers. The Boundary Layer options provided are County, Agricultural Statistics Districts (ASD), State, and Region. National Cultivated Layer -- based on the most recent five years (2013-2020). National Frequency Layer -- the 2017 Crop Frequency Layer identifies crop specific planting frequency and are based on land cover information derived from the 2008 through 2020CDL's. There are currently four individual crop frequency data layers that represent four major crops: corn, cotton, soybeans, and wheat. National Confidence Layer -- the Confidence Layer spatially represents the predicted confidence that is associated with that output pixel, based upon the rule(s) that were used to classify it. Western/Eastern/Central U.S.

Visit https://nassgeodata.gmu.edu/CropScape/ for the interactive map including tutorials and basic instructions. These options include a "Demo Video", "Help", "Developer Guide", and "FAQ".

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data.The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012.Known Issues* See Section 3.0 of the GFSAD 1 km User Guide.

USA Cropland is a time-enabled imagery layer of the USDA Cropland Data Layer dataset from the National Agricultural Statistics Service (NASS). The time series shows the crop grown during every growing season in the conterminous US since 2008. Use the time slider to select only one year to view, or press play to see every growing season displayed sequentially in an animated map.The USDA is now serving the Cropland Data Layer in their own application called CropCros which allows selection and display of a single product or growing season. This application will eventually replace their popular CropScape application.This dataset is GDA compliant. Compliancy information can be found here.Why USA Cropland masks out NLCD land cover in its default templateUSDA Cropland Data Layer, by default as downloaded from USDA, fills in the non-cultivated areas of the conterminous USA with land cover classes from the MRLC National Land Cover Dataset (NLCD). The default behavior for Esri's USA Cropland layer is a little bit different. By default the Esri USA Cropland layer uses the analytic renderer, which masks out this NLCD data. Why did we choose to mask out the NLCD land cover classes by default?While crops are updated every year from USDA NASS, the NLCD data changes every several years, and it can be quite a bit older than the crop data beside it. If analysis is conducted to quantify landscape change, the NLCD-derived pixels will skew the results of the analysis because NLCD land cover in a yearly time series may appear to remain the same class for several years in a row. This can be problematic because conclusions drawn from this dataset may underrepresent the amount of change happening to the landscape.Since the 2018 Cropland Data Layer was posted (early 2019), MRLC issued an update to the NLCD Land Cover dataset. The 2019 and 2020 cropland frames have this more current NLCD data, but the years before that contain NLCD land cover data from 2011 or older.To display the most current land cover available from both sources, add both the USA NLCD Land Cover service and USA Cropland time series to your map. Use the analytical template with the USA Cropland service, and draw it on top of the USA NLCD Land Cover service. When a time slider is used with these datasets together, the map user will see the most current land cover from both services in any given year.Variable mapped: Crop grown in each pixel since 2008.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scales are visibleSource: USDA NASSPublication Date: 2/2/2022This layer and the data making up the layer are in the Albers map projection. Albers is an equal area projection, and this allows users of this layer to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web Mercator, if that is the destination projection of the layer.Processing templates available with this layerTo help filter out and display just the crops and land use categories you are interested in showing, choose one of the thirteen processing templates that will help you tailor the symbols in the time series to suit your map application. The following are the processing templates that are available with this layer:Analytic RendererUSDA Analytic RendererThe analytic renderer is the default template. NLCD codes are masked when using analytic renderer processing templates. There is a default esri analytic renderer, but also an analytic renderer that uses the original USDA color scheme that was developed for the CropScape layers. This is useful if you have already built maps with the USDA color scheme or otherwise prefer the USDA color scheme.Cartographic RendererUSDA Cartographic RendererThese templates fill in with NLCD land cover types where crops are not cultivated, thereby filling the map with color from coast to coast. There is also a template using the USDA color scheme, which is identical to the datasets as downloaded from USDA NASS.In addition to different ways to display the whole dataset, some processing templates are included which help display the top 10 agricultural products in the United States. If these templates seem to overinclude crops in their category (for example, tomatoes are included in both the fruit and vegetables templates), this is because it's easier for a map user to remove a symbol from a template than it is to add one.Corn - Corn, sweet corn, popcorn or ornamental corn, plus double crops with corn and another crop.Cotton - Cotton and double crops, includes double crops with cotton and another crop.Fruit - Symbolized fruit crops include not only things like melons, apricots, and strawberries, but also olives, avocados, and tomatoes. Nuts - Peanuts, tree nuts, sunflower, etc.Oil Crops - Oil crops include rapeseed and canola, soybeans, avocado, peanut, corn, safflower, sunflower, also cotton and grapes.Rice - Rice crops.Sugar - Crops grown to make sugars. Sugar beets and cane are displayed of course, but so are corn and grapes.Soybeans - Soybean crops. Includes double crops where soybeans are grown at some time during the growing season.Vegetables - Vegetable crops, and yes this includes tomatoes. Wheat - Winter and spring wheat, durum wheat, triticale, spelt, and wheat double crops.In many places, two crops were grown in one growing season. Keep in mind that a double crop of corn and soybeans will display in both the corn and soybeans processing templates.Index to raster values in USA Cropland:0,Background (not a cultivated crop or no data)1,Corn2,Cotton3,Rice4,Sorghum5,Soybeans6,Sunflower10,Peanuts11,Tobacco12,Sweet Corn13,Popcorn or Ornamental Corn14,Mint21,Barley22,Durum Wheat23,Spring Wheat24,Winter Wheat25,Other Small Grains26,Double Crop Winter Wheat/Soybeans27,Rye28,Oats29,Millet30,Speltz31,Canola32,Flaxseed33,Safflower34,Rape Seed35,Mustard36,Alfalfa37,Other Hay/Non Alfalfa38,Camelina39,Buckwheat41,Sugarbeets42,Dry Beans43,Potatoes44,Other Crops45,Sugarcane46,Sweet Potatoes47,Miscellaneous Vegetables and Fruits48,Watermelons49,Onions50,Cucumbers51,Chick Peas52,Lentils53,Peas54,Tomatoes55,Caneberries56,Hops57,Herbs58,Clover/Wildflowers59,Sod/Grass Seed60,Switchgrass61,Fallow/Idle Cropland62,Pasture/Grass63,Forest64,Shrubland65,Barren66,Cherries67,Peaches68,Apples69,Grapes70,Christmas Trees71,Other Tree Crops72,Citrus74,Pecans75,Almonds76,Walnuts77,Pears81,Clouds/No Data82,Developed83,Water87,Wetlands88,Nonagricultural/Undefined92,Aquaculture111,Open Water112,Perennial Ice/Snow121,Developed/Open Space122,Developed/Low Intensity123,Developed/Med Intensity124,Developed/High Intensity131,Barren141,Deciduous Forest142,Evergreen Forest143,Mixed Forest152,Shrubland176,Grassland/Pasture190,Woody Wetlands195,Herbaceous Wetlands204,Pistachios205,Triticale206,Carrots207,Asparagus208,Garlic209,Cantaloupes210,Prunes211,Olives212,Oranges213,Honeydew Melons214,Broccoli215,Avocados216,Peppers217,Pomegranates218,Nectarines219,Greens220,Plums221,Strawberries222,Squash223,Apricots224,Vetch225,Double Crop Winter Wheat/Corn226,Double Crop Oats/Corn227,Lettuce228,Double Crop Triticale/Corn229,Pumpkins230,Double Crop Lettuce/Durum Wheat231,Double Crop Lettuce/Cantaloupe232,Double Crop Lettuce/Cotton233,Double Crop Lettuce/Barley234,Double Crop Durum Wheat/Sorghum235,Double Crop Barley/Sorghum236,Double Crop Winter Wheat/Sorghum237,Double Crop Barley/Corn238,Double Crop Winter Wheat/Cotton239,Double Crop Soybeans/Cotton240,Double Crop Soybeans/Oats241,Double Crop Corn/Soybeans242,Blueberries243,Cabbage244,Cauliflower245,Celery246,Radishes247,Turnips248,Eggplants249,Gourds250,Cranberries254,Double Crop Barley/Soybeans

Dataset Abstract:

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download on Zenodo.

Summary of Methods:

Using Google Earth Engine, we trained a random forest classifier to classify each pixel of the study area into corn, soybean, and an aggregated "other crops" class. CDL 2008-2018 data were used as labels. The features input to the model were harmonic regression coefficients fit to the NIR, SWIR1, SWIR2, and GCVI bands/indices of time series from Landsat 5, 7, and 8 Surface Reflectance observations. Cloudy pixels were masked out using the pixel_qa band provided with Landsat Surface Reflectance products.

Map Legend:

0 = outside study area

1 = corn

5 = soy

9 = other crop

255 = non-crop (masked by NLCD)

Values were chosen to be consistent with CDL values when possible.

Usage Notes:

We recommend that users consider metrics such as (1) user's and producer's accuracy with CDL and (2) R2 with NASS statistics across space and time to determine in which states/counties and years CSDL is of high quality. This can be done with the CSV file of user's and producer's accuracies included in this Zenodo, and annual county-level statistics and example code we have included in our repo at https://github.com/LobellLab/csdl.

Updates:

March 1, 2021: Fixed an issue where 255 (non-crop) values were represented as NAs instead. CSDL now contains the 255 values representing non-crop.

October 20, 2020: Fixed projection issues in the previous version. The CSDL projection now matches that of CDL.

July 13, 2020: We revised how we used NLCD to mask out non-crop pixels from our maps. Instead of using one cropland mask (the union of cropland across all NLCD maps) for all years of CSDL, we used a different cropland mask (the last available NLCD) for each year of CSDL. We also reprojected the CSDL maps to the same projection as CDL to make it easier for users to transition between or combine the two datasets.

This raster dataset provides information on crop types and their spatial extent in Afghanistan. 3 classes are considered: single crop harvest in the first part of year, single crop harvest in the second part of the year, double cropping

The Crop Masks used in the European Drought Observatory (EDO) and Global Drought Observatory (GDO) consist of 36 binary spatial layers (one for each 10-day period of the year) used to depict the location and extent of crop in its growing season. The input data for calculating the crop masks is the cropland and rangeland mask available through ASAP (Anomaly hot Spots of Agricultural Production, https://agricultural-production-hotspots.ec.europa.eu/). Data values are 0: Non-active, 1: Active.

VegScape https://nassgeodata.gmu.edu/VegScape/ delivers interactive vegetation indices so that web users can explore, visualize, query, and disseminate current vegetative cover maps and data without the need for specialized expertise, software, or high end computers. New satellite-based data are loaded on a weekly basis during the growing season. One can compare year-to-year change since the year 2000, compare conditions at a given times to mean, median and ratio vegetative cover, and can overlay a crop mask to help identify crop land versus non-crop land, among many functions. Vegetation indices, such as the NDVI (Normalized Difference Vegetation Index), and mean, median, and ratio comparisons to prior years have proven useful for assessing crop condition and identifying the land area impacted by floods, drought, major weather anomalies, and vulnerabilities of early/late season crops. The National Aeronautics Space Administration's MODIS satellite is used for this project and provides imaging at 250 meter (15 acres) per pixel resolution. Additionally, the data can be directly exported to Google Earth for mashups or delivered to other applications via web services. NASS developed both the CropScape and VegScape web services in cooperation with the Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA. For more information about this dataset, contact: Rick Mueller: rick.mueller@nass.usda.gov USDA, NASS, Spatial Analysis Research Section staff: HQ_RDD_GIB@nass.usda.gov Resources in this dataset:Resource Title: VegScape - Vegetation Condition Explorer web site. File Name: Web Page, url: https://nassgeodata.gmu.edu/VegScape/ Web interface supporting data query by layers (Global Cover, Cropland Data Layer, Boundaries, Water Layers, Road Layers, Data layers), Products (Type, Period, Year, Date). Toolbar buttons help define a wide range of map and query operations, data display, and download options.

The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is an Earth Observation (EO) based flood crop loss assessment cyber-service system operated by the Center for Spatial Information Science and Systems (CSISS), George Mason University. RF-CLASS supports flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration (NASA) Earth Observing System Data and Information System (EOSDIS), for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototype system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Resources in this dataset:Resource Title: Website Pointer to RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System. File Name: Web Page, url: https://dss.csiss.gmu.edu/RFCLASS/ Basic Layers: Global Cover: CDL: CDL 2012, Crop Mask; Boundaries,:Counties, States, ASD; Water Layers: Rivers, Lakes; Road Layers: Freeway System (National), Major Highways (Regional); Flood Data Layers: flood_frequency. Products: Type: Flood; Crop Fraction; Crop Loss; Flood Events; NDVI; VCI; RVCI; RMVCI; MVCI. (Period/Year/Date)

This dataset is a ready-to-use stack of Sentinel-2 satellite images. It contains the time series of the composite cloud-free NDVI images in the province of Overijssel (Netherlands) during the year 2017-2018

The WorldCereal active cropland products provide binary maps for all growing seasons as defined by the WorldCereal global crop calendars, showing where active cropland has been detected. Seasonal active cropland is defined by the WorldCereal system as actively cultivated cropland during a specific growing season. In order for a pixel to be labeled as active during a particular growing season, a full crop growth cycle (sowing, growing, senescence and harvesting) needs to take place within the designated time period. Note that this active marker is not crop-type specific and only consider specific seasonality. This also means in practice that any crop grown (slightly) outside the predefined growing seasons will not be flagged as active cropland in any of the seasons covered by these products. The WorldCereal active cropland products were generated within the respective annual temporary crops mask.

This dataset has been created as part of the deliverables for ESA’s SD4EO project. It consists of textures generated using a multispectral variant of a still unpublished high-order statistical constraint synthesis method for each of the following crop types:

Barley.

Wheat.

Other grain leguminous.

Peas.

Fallow & Bare soil.

Vetch.

Alfalfa.

Sunflower.

Oats.

The initial data was sampled from satellite images, specifically from Copernicus’ Sentinel-1 and Sentinel-2 satellites. The images were acquired over a period from October 2017 to September 2018 on the central-east region of northern Spain (Castile and León and Catalonia). From these images, the corresponding crops were extracted and used as samples for assembling large puzzles that have been applied as input reference images to generate the synthetic images that make up this dataset.

The datasets of assembled crop field "puzzles" used as reference images combine the largest crop areas to create a square multispectral texture of the largest possible size that is a power of 2 (or nearly a power of 2). Each base image combines data from all available Sentinel-2 satellite passes for the same month and a previous monthly composition from Sentinel-1. Due to cloud masks influence, the shape and number of crops vary for each time sample, preventing the reuse of element disposition in the “puzzles” across different months. Therefore, we have a base image (puzzle) for each month and crop type, with a size dependent on the number and area of crops not covered by clouds. These base image sizes range between 256, 384, 512, 768, 1024, 1536, and 2048 pixels per side, influenced by weather conditions and crop type each year season.

In this dataset, the synthetic texture sizes match the corresponding base image sizes to facilitate debugging the method implementation and enable subsequent comparisons. For crops with a base image size of 1536 pixels or larger, the generated synthetic images have been reduced to half their size to reduce computational costs and RAM requirements, thereby completing the synthesis faster. Consequently, there remains some diversity in file sizes, generally smaller for crop types with less cultivated area.

Additionally, to increase the amount of available data, six variants have been synthesized from each base multispectral image. This number can be arbitrarily increased, as initialization with noise (random numbers) ensures the distinction among the generated data.

File names are structured as follows:

Prefix "HO" indicating the synthesis method

The crop type name:

Barley

Wheat

OtherGrainLeguminous

Peas

FallowAndBareSoil

Vetch

Alfalfa

Sunflower

Oats

Year/Month/01 (representing the start of the month period)

Side length of the multispectral texture in pixels (based on the highest precision instrument of Sentinel-2: 10m x 10m)

Number of the synthesis variant

The generation parameters for all images include:

Normalized and weighted bands (VH band influence increased by a factor of 3 compared to others)

4 levels of depth in the Steerable pyramid

6 orientations in the Steerable pyramid

14 joint statistics of the wavelet coefficients corresponding to basis functions at adjacent spatial locations, orientations, and scales. This parameter is crucial for capturing local dependencies between wavelet coefficients, essential for the visual perception of texture.

30 iterations

A significant effort has been made to stabilize the algorithm, and to eliminate artifacts in the generated textures, resulting in much more robust outcomes. However, in rare cases, the initial white noise distribution can be statistically unfavorable, leading to instabilities. Files have been left as generated, without correcting these effects, to make them visible despite their low frequency. Specifically, among the 657 generated multispectral textures, this phenomenon has occurred prominently in only two and is relatively noticeable in another two, leaving the rest free of this effect (affecting less than 1% of the syntheses).

Thus, the following files can be considered partially failed syntheses:

HO_Alfalfa_20180801_768_1.nc

HO_FallowAndBareSoil_20180101_768_3.nc

HO_OtherGrainLeguminous_20171201_256_4.nc

HO_Vetch_20180301_384_3.nc

Files are encoded in the standardized net4CDF format [link], each containing a single xarray with metadata corresponding to a 3D array with the synthesized texture of the indicated crop type and satellite passes for the regions of Castilla y León and Catalonia for the corresponding monthly period.

The most important data structure is the 3D array, where the first two dimensions correspond to the pixel extent indicated in the file name as square textures ('x' and 'y' labels in the xarray). The third dimension denotes the spectral band of the satellite, ordered by constellation and pixel size:

'B02' 10m (Sentinel-2)

'B03' 10m (Sentinel-2)

'B04' 10m (Sentinel-2)

'B08' 10m (Sentinel-2)

'B05' originally 20m, resampled to 10m (Sentinel-2)

'B06' originally 20m, resampled to 10m (Sentinel-2)

'B07' originally 20m, resampled to 10m (Sentinel-2)

'B11' originally 20m, resampled to 10m (Sentinel-2)

'B12' originally 20m, resampled to 10m (Sentinel-2)

'B8A' originally 20m, resampled to 10m (Sentinel-2)

'VH' also resampled to 10m (Sentinel-1)

The original dynamic range is preserved in all bands, and they have been synthesized together using our multispectral algorithm variant. The new band combination may result in slightly unusual values in vegetation indices since restrictions were not considered in their transformed space, but in the latent space of the decorrelated Steerable pyramid.

Additionally, the following metadata are stored as xarray attributes:

"long_name": corresponding to the crop type name

"date": the period of the original data used as the base image for synthesis

"dataset": denotes the combination of the initial Castilla y León dataset and the extended 6 Tiles from Catalonia

"synthetic_method": corresponds to the high-order constrained method

"max_visible_value": a reference value to maintain the same dynamic range when comparing with base images, avoiding distortions in color space and contrast

A total of:

       9 types of crops x 12 months x 6 variants = 648 synthetized multispectral textures

occupying 34.5GB, have been organized and uploaded into 9 ZIP files (one per crop type) on the Zenodo website for distribution under Creative Commons Attribution 4.0 International license.

The SD4EO Project is funded by the ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and supervised by ESA Φ-lab.

Crop land map for United Republic of Tanzania at 90 meters resolution (reference year: 2019). Each pixel represents the crop land area in square meters. These data derive from ASAP crop mask (Version 03, Anomaly Hotspots of Agricultural Production, JRC)

Agricultural Stress Index System (ASIS) is a global agricultural drought monitoring system developed and operated by FAO which enables to monitor agricultural areas affected by dry spells, or severe drought in extreme cases, using satellite data. It provides a collective quick-look indicators that facilitate the early identification of cropland/grassland with a high likelihood of water stress (drought). ASIS related products (maps, zonal statistics) are processed by FAO GIEWS (Global Information and Early Warning System on Food and Agriculture) every 10 days. Pre-processed, published-ready maps, zonal statistics of ASIS are published FAO GIEWS Earth Observation website at: https://www.fao.org/giews/earthobservation/index.jsp?lang=en. All ASIS raster datasets are accessible through the FAO Hand-in-Hand Geospatial Portal, Web Map Service (WMS) and Google Earth Engine (GEE). More information, please visit ASIS Data Access page: https://www.fao.org/giews/earthobservation/access.jsp?lang=en Agricultural Stress Index System is composed of two type of indicators: seasonal indicators such as Agricultural Stress Index (ASI) to detect the severe agricultural drought, Drought Intensity to classify the severity of the drought and no-seasonal indicators, such as vegetation indicators (NDVI anomaly, VCI and VHI). The seasonal indicators are designed to allow easy identification of areas of cropped land with a high likelihood of water stress (drought). The indices are based on remote sensing data of vegetation and land surface temperature combined with information on agricultural cropping cycles derived from historical data and a global crop mask. The final maps highlight anomalous vegetation growth and potential drought in crop zones during the growing season. In ASIS, two cropping cycles (major season /minor season) and crop/grassland zones are applied. Some countries have three or four crop seasons within a crop year. For these countries, Global ASIS cannot properly capture the agricultural drought occurred between the first and the last season (e.g. for a country has four crop seasons, the drought occurred during the 2nd and 3rd season). The satellite data used in the calculation of the mean VHI and the ASI is the 10-day (dekadal) vegetation data from the METOP-AVHRR sensor at 1 km resolution (2007 and after). Data at 1 km resolution for the period 1984-2006 was derived from the NOAA-AVHRR dataset at 16 km resolution. The crop/grass mask is FAO GLC-SHARE. Pixel with at least 5% covered by the class is defined as a cropland/grassland pixel. Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed] For more information, please visit GIEWS Earth Observation website.

The WorldCereal active irrigation products provide binary maps for all growing seasons as defined by the WorldCereal global crop calendars, showing where active irrigation has been detected. Seasonally actively irrigated cropland is defined by the WorldCereal system as a piece of land that is extensively irrigated during a specific growing season where, without irrigation applied at regular intervals, crop growth would be significantly reduced or impossible. Incidental irrigation, such as irrigation that has been applied only during the sowing period of a crop, is not translated to actively irrigated cropland. The WorldCereal active irrigation products were generated within the respective annual temporary crops mask.

Crop land map for Eritrea at 90 meters resolution (reference year: 2019). Each pixel represents the crop land area in square meters. These data derive from ASAP crop mask (Version 03, Anomaly Hotspots of Agricultural Production, JRC)

The WorldCereal maize products provide binary maps for the maize growing seasons as defined by the WorldCereal global crop calendars, showing where maize is grown. The WorldCereal maize products were generated within the respective annual temporary crops mask.

The WorldCereal spring cereals products provide binary maps for the spring cereals growing season as defined by the WorldCereal global crop calendars, showing where spring cereals are grown. This season describes the spring cereals season in northern latitudes. Spring cereals include wheat, barley and rye, which belong to the Triticeae tribe. These crops were grouped together because their spectral signatures and growing seasons were too similar to reliably distinguish them at a global scale. The WorldCereal spring cereals products were generated within the respective annual temporary crops mask.

The WorldCereal winter cereals products provide binary maps for the winter cereals growing season as defined by the WorldCereal global crop calendars, showing where cereals are grown. This season describes the main cereals season in a region. Cereals include wheat, barley and rye, which belong to the Triticeae tribe. These crops were grouped together because their spectral signatures and growing seasons were too similar to reliably distinguish them at a global scale. The WorldCereal winter cereals products were generated within the respective annual temporary crops mask.

This data set provides two products that were derived from the recently published North American Carbon Program (NACP) Regional Synthesis 1-degree terrestrial biosphere model (TBM) and inverse model (IM) outputs (Gridded 1-deg Observation Data and Biosphere and Inverse Model Outputs, Wei et al., 2013).

The first product is the aggregation of the standardized gridded 1-degree TBM and IM outputs to the Greenhouse Gas (GHG) inventory zones as defined for North America (United States, Canada, and Mexico). Depending on the data availability, the monthly/yearly Net Ecosystem Exchange (NEE), Net Primary Production (NPP), Total Vegetation Carbon (VegC), Heterotrophic Respiration (Rh), and Fire Emissions (FE) outputs from the 22 TBM and 7 IM models were aggregated from the 1-degree resolution gridded format to the inventory zones and then, further divided into Forest Lands, Crop Lands, and Other Lands sectors within each inventory zone based on the 1-km resolution GLC2000 land cover map (GLC2000, 2003).

The second product is the North American national GHG inventories on the scale of inventory zones which contain estimated land-atmosphere exchange of CO2 (NEE) in forest lands, crop lands, and other lands sectors. NEE estimates were synthesized from inventory-based data on productivity, ecosystem carbon stock change, and harvested product stock change, and additional information from national-level GHG inventories of the United States, Canada, and Mexico including EPA (2011) and Environment Canada (2011).

An additional summary file of annual mean NEE (2000-2006)is provided for both land sectors and reporting zones in North America and was created by combining the aggregated model output and the national GHG database and is provided.

The aggregated monthly and yearly model output data and the national GHG inventories data are available in comma separated value (*.csv) format files.

Also provided are detailed inventory zone spatial data as an ESRI Shapefile. Included are zone names, boundaries, and zone and land cover type area attributes. For mapping convenience, the inventory zones shapefile was merged with 1-km forest, crop, and other lands masks to create a 1-km resolution reference data file that was converted to GeoTIFF format. The GeoTIFF defines to which inventory zone and land cover type each 1-km grid cell belongs.

This document provides detailed information about the content, format, and processing procedures of these two data products. Detailed descriptions of the TBMs and IMs can be found in a separate companion document: NACP Regional Synthesis - Description of Observations and Models.

Crop land map for Burundi at 90 meters resolution (reference year: 2019). Each pixel represents the crop land area in square meters. These data derive from ASAP crop mask (Version 03, Anomaly Hotspots of Agricultural Production, JRC)