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Canada AAFC Annual Crop Inventory
Starting in 2009, the Earth Observation Team of the Science and Technology Branch (STB) at Agriculture and Agri-Food Canada (AAFC) began the process of generating annual crop type digital maps. Focusing on the Prairie Provinces in 2009 and 2010, a Decision Tree (DT) based methodology … canada crop landcover -
Global Map of Oil Palm Plantations
The dataset is a 10m global industrial and smallholder oil palm map for 2019. It covers areas where oil palm plantations were detected. The classified images are the output of a convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. See article for additional … biodiversity conservation crop global landuse oilpalm -
ESA WorldCereal Active Cropland 10 m v100
The European Space Agency (ESA) WorldCereal Active Cropland 10 m 2021 product suite contains global-scale seasonal active cropland markers. They were generated as part of the ESA-WorldCereal project. The active cropland products indicate whether or not a pixel identified as temporary crops has been actively … agriculture copernicus crop esa global landcover -
ESA WorldCereal 10 m v100
The European Space Agency (ESA) WorldCereal 10 m 2021 product suite consists of global-scale annual and seasonal crop maps and their related confidence. They were generated as part of the ESA-WorldCereal project. More information on the content of these products and the methodology used to … agriculture copernicus crop esa global landcover -
ESA WorldCereal AEZ v100
The European Space Agency (ESA) WorldCereal classification system aims for product generation within one month after the end of a particular growing season. Due to the dynamic nature of these growing seasons across the globe, a global stratification into Agro-Ecological Zones (AEZ) was performed based … agriculture boundaries crop esa global table -
GRIDMET DROUGHT: CONUS Drought Indices
This dataset contains drought indices derived from the 4-km daily Gridded Surface Meteorological (GRIDMET) dataset. The drought indices provided include the standardized precipitation index (SPI), the evaporative drought demand index (EDDI), the standardized precipitation evapotranspiration index (SPEI), the Palmer Drought Severity Index (PDSI) and Palmer … climate conus crop drought evapotranspiration geophysical -
EUCROPMAP
European crop type map based on Sentinel-1 and LUCAS Copernicus 2018 in-situ observations for 2018; and one based on Sentinel-2 and LUCAS Copernicus 2022 for 2022. Capitalizing on the unique LUCAS 2018 Copernicus in-situ survey, the 2018 dataset is the first continental crop type map … crop eu jrc lucas sentinel1-derived -
USDA NASS Cropland Data Layers
The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, … crop landcover usda -
GFSAD1000: Cropland Extent 1km Multi-Study Crop Mask, Global Food-Support Analysis Data
The GFSAD is a NASA-funded project to provide high-resolution global cropland data and their water use that contributes towards global food security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat, MODIS, AVHRR), secondary data, and field-plot data … crop landcover usgs -
Oil Palm Plantation Probability v20240312
This image collection provides per-pixel probability that the underlying area is in oil palm cultivation. These probability estimates are provided at 10 meter resolution, and have been generated by a machine learning model. Labeled examples of oil palm plantations were supplied by community contributors to … biodiversity conservation crop deforestation eudr landuse
[null,null,[],[[["This webpage showcases a variety of datasets related to global cropland and oil palm plantations, including information on crop types, extent, and probabilities."],["The datasets are derived from various sources, such as satellite imagery (Sentinel-1, Sentinel-2, Landsat, MODIS), ground truth data, and machine learning models."],["Organizations like ESA, USDA, AAFC, and Biopama contribute to these datasets, offering valuable resources for agricultural monitoring and research."],["Many of the datasets provide global or continental coverage, while others focus on specific regions like the US or Canada."],["Users can access information on crop-specific land cover, active cropland markers, drought indices related to crops, and oil palm plantation distribution."]]],[]]