1,720,994 research outputs found
Satellite Data for Corn and Soybean Fields + Other Land Cover: Illinois, US, 2017-2019
No full textThese are the datasets associated with the paper:
Hannah Kerner, Ritvik Sahajpal, Sergii Skakun, Inbal Becker-Reshef, Brian Barker, Mehdi Hosseini, Estefania Puricelli, and Patrick Gray. 2020. Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. In KDD ’20: ACM Special Interest Group (SIG) on Knowledge Discovery and Data Mining Conference Workshops, August 23–27, 2020, San Diego, CA.
The code that uses these datasets can be found at: https://github.com/nasaharvest/croptype-mapping-gs
Hand Labelled Crop / Non Crop datasets
No full textThis dataset provides the hand-labelled crop / non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. Data is available for Ethiopia, Sudan, Togo and Kenya.
Code used to process these points is available in the following github repository: https://github.com/nasaharvest/crop-maml
For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2021. Learning to predict crop type from heterogeneous sparse labels using meta-learning. GeoVision Workshop at CVPR ’21: June 19th, 202
GEOGLAM (GEO Global Agricultural Monitoring) Crop Assessment Tool
No full textThe Group on Earth Observations, a partnership of governments and international organizations, developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to the growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community’s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth Observations (EO), which include satellite and ground-based observations. This initiative is designed to build on existing agricultural monitoring programs and initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing.
The GEOGLAM Crop Monitor provides the Agricultural Market Information System (AMIS) with an international and transparent multi-source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soy) within the main agricultural producing regions of the AMIS countries. These assessments have been produced operationally since September 2013 and are published in the AMIS Market Monitor Bulletin. The Crop Monitor reports provide cartographic and textual summaries of crop conditions as of the 28th of each month, according to crop type.
Sources and Disclaimers: The Crop Monitor assessment is conducted by GEOGLAM with coordination from the University of Maryland. Inputs are from the following partners (in alphabetical order): Argentina (Buenos Aires Grains Exchange, INTA), Asia Rice Countries (AFSIS, ASEAN+3 & Asia RiCE), Australia (ABARES & CSIRO), Brazil (CONAB & INPE), Canada (AAFC), China (CAS), EU (EC JRC MARS), Indonesia (LAPAN & MOA), International (CIMMYT, FAO, IFPRI & IRRI), Japan (JAXA ), Mexico (SIAP), Russian Federation (IKI), South Africa (ARC & GeoTerraImage & SANSA), Thailand (GISTDA & OAE), Ukraine (NASU-NSAU & UHMC), USA (NASA, UMD, USGS – FEWS NET, USDA (FAS, NASS)), Viet nam (VAST & VIMHE-MARD). The findings and conclusions in this joint multi-agency report are consensual statements from the GEOGLAM experts, and do not necessarily reflect those of the individual agencies represented by these experts. Map data sources: Major crop type areas based on the IFPRI/IIASA SPAM 2005 beta release (2013), USDA/NASS 2013 CDL, 2013 AAFC Annual Crop Inventory Map, GLAM/UMD, GLAD/UMD, Australian Land Use and Management Classification (Version 7), SIAP, ARC, and JRC. The GEOGLAM crop calendars are compiled with information from AAFC, ABARES, ARC, Asia RiCE, Bolsa de cereales, CONAB, INPE, JRC, FAO, FEWS NET, IKI, INTA, SIAP, UHMC, USDA FAS, and USDA NASS. Resources in this dataset:Resource Title: GEOGLAM Crop Assessment Tool (web site) . File Name: Web Page, url: https://earthobservations.org/geoglam.php GEOGLAM contains a web-based dashboard to manipulate data, including satellite data, integrating them with ground-based and other in situ measurements. "The initiative will contribute to generating reliable, accurate, timely and sustained crop monitoring information and yield forecasts."
GEOGLAM Crop Assessment Tool Requirements:
•Users must be registered in order to use the commenting features
•A modern browser is recommended (IE10+, Firefox22+, Chrome27+)
•Popup blockers should be disabled if using the table-based Crop Impact Assessment feature
•Data is provided by Crop Monitor Group partners
Crop Monitor tool can be found at https://cropmonitor.org/index.php/eodatatools/cmet/</p
Togo Cropland Map and Labeled Dataset
No full textThis dataset provides a 10 m resolution map of cropland in Togo (togo_cropland_2019.zip). Each pixel represents a posterior probability (ranging 0 to 1) that the pixel contains crops, predicted using an LSTM classifier and multi-spectral time series of Sentinel-2 satellite observations. For more details on the method, please see Kerner and Tseng, et al. (full reference below).
This dataset also provides the hand-labeled polygons used for training (crop_merged_v2.zi, noncrop_merged_v2.zip) and testing (togo_test_majority.zip) the model, which were created by experts based on photointerpretation of high-resolution imagery (primarily SkySat and PlanetScope) in QGIS and Google Earth Pro.
If you use any part of this dataset, please cite the following paper: Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe, Brian Barker, Blake Munshell, Madhava Paliyam, and Mehdi Hosseini. 2020. Rapid Response Crop Maps in Data Sparse Regions. In review for KDD ’20: ACMSIGKDD Conference on Knowledge Discovery and Data Mining Workshops, August 22–27, 2020, San Diego, CA
Evaluating the impacts of drought on crop production from satellite observations: A case study in Kansas
No full textEvaluation of the ASCAT surface soil moisture product for agricultural drought monitoring in USA
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