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In-Season Unsupervised Mapping and Planted Area Estimation of Major Crops in War-Affected Ukraine

https://doi.org/10.2139/ssrn.4779347
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inferredcites →https://doi.org/10.1002/9780470665480openalex-extracted
Agricultural Survey Methods
inferredcites →https://doi.org/10.1002/agg2.20125openalex-extracted
Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds
inferredcites →https://doi.org/10.1007/978-90-481-9618-0_9openalex-extracted
Satellite-Derived Information on Snow Cover for Agriculture Applications in Ukraine
inferredcites →https://doi.org/10.1007/s10462-015-9433-yopenalex-extracted
Dealing with the evaluation of supervised classification algorithms
inferredcites →https://doi.org/10.1007/s41064-018-0050-7openalex-extracted
A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information
inferredcites →https://doi.org/10.1016/0034-4257(79)90013-0openalex-extracted
Red and photographic infrared linear combinations for monitoring vegetation
inferredcites →https://doi.org/10.1016/j.agsy.2022.103462openalex-extracted
Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data
inferredcites →https://doi.org/10.1016/j.compag.2020.105978openalex-extracted
Spatial domain bridge transfer : An automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area
inferredcites →https://doi.org/10.1016/j.compenvurbsys.2012.01.003openalex-extracted
Knowledge-based region labeling for remote sensing image interpretation
inferredcites →https://doi.org/10.1016/j.eja.2008.12.002openalex-extracted
The impact of soil structure on the establishment of winter wheat (Triticum aestivum)
inferredcites →https://doi.org/10.1016/j.eja.2022.126566openalex-extracted
The impact of crop establishment systems in combination with applied nitrogen management on the establishment, growth and yield of winter oilseed rape in a mild Atlantic climate
inferredcites →https://doi.org/10.1016/j.gfs.2022.100661openalex-extracted
The impact of Russia-Ukraine conflict on global food security
inferredcites →https://doi.org/10.1016/j.isprsjprs.2010.04.004openalex-extracted
Early-season crop area estimates for winter crops in NE Australia using MODIS satellite imagery
inferredcites →https://doi.org/10.1016/j.isprsjprs.2021.04.015openalex-extracted
Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil
inferredcites →https://doi.org/10.1016/j.jag.2013.12.007openalex-extracted
Combined use of multi-seasonal high and medium resolution satellite imagery for parcel-related mapping of cropland and grassland
inferredcites →https://doi.org/10.1016/j.jag.2013.12.013openalex-extracted
Efficiency assessment of using satellite data for crop area estimation in Ukraine
inferredcites →https://doi.org/10.1016/j.jag.2013.12.015openalex-extracted
Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2
inferredcites →https://doi.org/10.1016/j.jag.2018.11.012openalex-extracted
Remote sensing based yield monitoring: Application to winter wheat in United States and Ukraine
inferredcites →https://doi.org/10.1016/j.jag.2022.102937openalex-extracted
Large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data
inferredcites →https://doi.org/10.1016/j.molp.2022.06.012openalex-extracted
Speed vernalization to accelerate generation advance in winter cereal crops
inferredcites →https://doi.org/10.1016/j.neucom.2022.04.002openalex-extracted
Analysis of clustering methods for crop type mapping using satellite imagery
inferredcites →https://doi.org/10.1016/j.patrec.2015.02.001openalex-extracted
Semi-automatic ground truth generation using unsupervised clustering and limited manual labeling: Application to handwritten character recognition
inferredcites →https://doi.org/10.1016/j.rse.2004.12.009openalex-extracted
Mapping paddy rice agriculture in southern China using multi-temporal MODIS images
inferredcites →https://doi.org/10.1016/j.rse.2006.11.021openalex-extracted
Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains
inferredcites →https://doi.org/10.1016/j.rse.2007.07.019openalex-extracted
Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains
inferredcites →https://doi.org/10.1016/j.rse.2007.09.007openalex-extracted
The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI
inferredcites →https://doi.org/10.1016/j.rse.2010.01.010openalex-extracted
A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data
inferredcites →https://doi.org/10.1016/j.rse.2011.10.011openalex-extracted
Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index
inferredcites →https://doi.org/10.1016/j.rse.2014.02.015openalex-extracted
Good practices for estimating area and assessing accuracy of land change
inferredcites →https://doi.org/10.1016/j.rse.2014.10.009openalex-extracted
Cloud cover throughout the agricultural growing season: Impacts on passive optical earth observations
inferredcites →https://doi.org/10.1016/j.rse.2015.02.014openalex-extracted
Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information
inferredcites →https://doi.org/10.1016/j.rse.2017.03.047openalex-extracted
A multi-resolution approach to national-scale cultivated area estimation of soybean
inferredcites →https://doi.org/10.1016/j.rse.2017.04.026openalex-extracted
Early season large-area winter crop mapping using MODIS NDVI data, growing degree days information and a Gaussian mixture model
inferredcites →https://doi.org/10.1016/j.rse.2017.04.031openalex-extracted
A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems
inferredcites →https://doi.org/10.1016/j.rse.2017.06.031openalex-extracted
Google Earth Engine: Planetary-scale geospatial analysis for everyone
inferredcites →https://doi.org/10.1016/j.rse.2017.06.033openalex-extracted
MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types
inferredcites →https://doi.org/10.1016/j.rse.2017.10.005openalex-extracted
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
inferredcites →https://doi.org/10.1016/j.rse.2018.12.026openalex-extracted
Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques
inferredcites →https://doi.org/10.1016/j.rse.2020.111660openalex-extracted
Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series
inferredcites →https://doi.org/10.1016/j.rse.2021.112365openalex-extracted
Integrating coarse-resolution images and agricultural statistics to generate sub-pixel crop type maps and reconciled area estimates
inferredcites →https://doi.org/10.1016/j.rse.2021.112586openalex-extracted
A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery
inferredcites →https://doi.org/10.1016/j.rse.2022.112990openalex-extracted
Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2
inferredcites →https://doi.org/10.1016/j.rse.2022.112994openalex-extracted
Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach
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Development of a 10-m resolution maize and soybean map over China: Matching satellite-based crop classification with sample-based area estimation
inferredcites →https://doi.org/10.1016/j.rse.2023.113689openalex-extracted
Sentinel-1 time series data for sunflower (Helianthus annuus) phenology monitoring
inferredcites →https://doi.org/10.1016/s0065-2113(08)60672-0openalex-extracted
Reflectance Properties of Soils
inferredcites →https://doi.org/10.1021/ac60214a047openalex-extracted
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
inferredcites →https://doi.org/10.1038/d41586-022-00789-xopenalex-extracted
Broken bread — avert global wheat crisis caused by invasion of Ukraine
inferredcites →https://doi.org/10.1038/s41592-019-0369-zopenalex-extracted
Analyzing outliers: robust methods to the rescue
inferredcites →https://doi.org/10.1038/s43016-023-00841-7openalex-extracted
Dynamic global-scale crop and irrigation monitoring
inferredcites →https://doi.org/10.1080/01431160310001619607openalex-extracted
Remote sensing and land cover area estimation
inferredcites →https://doi.org/10.1080/01431161.2015.1035412openalex-extracted
Remote sensing of violent conflict: eyes from above
inferredcites →https://doi.org/10.1080/01431161.2015.1047994openalex-extracted
Spectral indices for yellow canola flowers
inferredcites →https://doi.org/10.1080/15481603.2018.1482855openalex-extracted
Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system
inferredcites →https://doi.org/10.1080/17538947.2019.1572799openalex-extracted
Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass
inferredcites →https://doi.org/10.1088/1757-899x/336/1/012017openalex-extracted
Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster
inferredcites →https://doi.org/10.1109/cvprw53098.2021.00122openalex-extracted
Learning to predict crop type from heterogeneous sparse labels using meta-learning
inferredcites →https://doi.org/10.1109/icpr.2014.756openalex-extracted
Systematic Labeling Bias: De-biasing Where Everyone is Wrong
inferredcites →https://doi.org/10.1109/jstars.2015.2454297openalex-extracted
Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine
inferredcites →https://doi.org/10.14358/pers.78.8.815openalex-extracted
Mapping Crop Types, Irrigated Areas, and Cropping Intensities in Heterogeneous Landscapes of Southern India Using Multi-Temporal Medium-Resolution Imagery
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Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security
inferredcites →https://doi.org/10.2134/agronj1998.00021962009000050002xopenalex-extracted
Winter Wheat Seedling Emergence from Deep Sowing Depths
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The Russia-Ukraine Conflict: Its Implications for the Global Food Supply Chains
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How the War in Ukraine Affects Food Security
inferredcites →https://doi.org/10.3390/ijgi9110648openalex-extracted
An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning
inferredcites →https://doi.org/10.3390/rs10071058openalex-extracted
Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping
inferredcites →https://doi.org/10.3390/rs13173523openalex-extracted
Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery
inferredcites →https://doi.org/10.3390/rs13193930openalex-extracted
A Technical Review of Planet Smallsat Data: Practical Considerations for Processing and Using PlanetScope Imagery
inferredcites →https://doi.org/10.3390/rs14030541openalex-extracted
Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data
inferredcites →https://doi.org/10.3390/rs6032473openalex-extracted
Mapping Crop Cycles in China Using MODIS-EVI Time Series
inferredcites →https://doi.org/10.3390/s18030868openalex-extracted
New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery
inferredcites →https://doi.org/10.4141/cjps96-007openalex-extracted
Relationship between low-temperature tolerance and vernalization response in wheat and rye
inferredcites →https://doi.org/10.5194/essd-15-5491-2023openalex-extracted
WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping
inferredcites →https://doi.org/10.5281/zenodo.8091862openalex-snapshot
Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data
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