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Spatial Mapping of Rice Using Time Series Geospatial Big Data and Machine Learning Algorithms

https://doi.org/10.2139/ssrn.4677851
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inferredcites →https://doi.org/10.1016/j.asr.2019.08.042openalex-extracted
Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data
inferredcites →https://doi.org/10.1016/j.isprsjprs.2005.05.001openalex-extracted
Rice crop parameter retrieval using multi-temporal, multi-incidence angle Radarsat SAR data
inferredcites →https://doi.org/10.1016/j.isprsjprs.2015.05.011openalex-extracted
Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.03.008openalex-extracted
Optical remotely sensed time series data for land cover classification: A review
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.05.010openalex-extracted
Evolution of regional to global paddy rice mapping methods: A review
inferredcites →https://doi.org/10.1016/j.isprsjprs.2017.01.019openalex-extracted
Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
inferredcites →https://doi.org/10.1016/j.jag.2015.11.001openalex-extracted
Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images
inferredcites →https://doi.org/10.1016/j.jag.2017.10.013openalex-extracted
Mapping cropping patterns in irrigated rice fields in West Java: Towards mapping vulnerability to flooding using time-series MODIS imageries
inferredcites →https://doi.org/10.1016/j.rse.2005.09.007openalex-extracted
Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers
inferredcites →https://doi.org/10.1016/j.rse.2005.10.004openalex-extracted
Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images
inferredcites →https://doi.org/10.1016/j.rse.2015.08.004openalex-extracted
Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data
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.2017.10.005openalex-extracted
Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
inferredcites →https://doi.org/10.1016/j.scitotenv.2017.03.221openalex-extracted
Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China
inferredcites →https://doi.org/10.1080/01431160500114698openalex-extracted
Deriving cropping system performance indices using remote sensing data and GIS
inferredcites →https://doi.org/10.1080/01431161.2010.532826openalex-extracted
Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis
inferredcites →https://doi.org/10.1080/01431161.2012.738946openalex-extracted
Remote Sensing of Rice Crop Areas – A Review
inferredcites →https://doi.org/10.1080/01431161.2017.1395969openalex-extracted
Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data
inferredcites →https://doi.org/10.1080/01431161.2020.1779378openalex-extracted
Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning
inferredcites →https://doi.org/10.1080/014311698214073openalex-extracted
Rice crop area estimation of an administrative division in China using remote sensing data
inferredcites →https://doi.org/10.1080/014311698216134openalex-extracted
Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year
inferredcites →https://doi.org/10.1080/10106048709354084openalex-extracted
Introductory digital image processing: A remote sensing perspective
inferredcites →https://doi.org/10.1080/10106049.2017.1289555openalex-extracted
Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines
inferredcites →https://doi.org/10.1080/17538947.2016.1168489openalex-extracted
Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250 m time-series data
inferredcites →https://doi.org/10.1080/2150704x.2016.1225172openalex-extracted
Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data
inferredcites →https://doi.org/10.1080/2150704x.2017.1331472openalex-extracted
Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales
inferredcites →https://doi.org/10.1109/jstars.2014.2387214openalex-extracted
Capability of Rice Mapping Using Hybrid Polarimetric SAR Data
inferredcites →https://doi.org/10.1109/jstars.2016.2586102openalex-extracted
Rice Mapping Using RADARSAT-2 Dual- and Quad-Pol Data in a Complex Land-Use Watershed: Cau River Basin (Vietnam)
inferredcites →https://doi.org/10.1109/lgrs.2013.2261049openalex-extracted
Rice-Planted Area Mapping Using Small Sets of Multi-Temporal SAR Data
inferredcites →https://doi.org/10.1371/journal.pone.0070816openalex-extracted
Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR
inferredcites →https://doi.org/10.2307/2529310openalex-extracted
The Measurement of Observer Agreement for Categorical Data
inferredcites →https://doi.org/10.3390/agriculture10100433openalex-extracted
Large-Scale and High-Resolution Crop Mapping in China Using Sentinel-2 Satellite Imagery
inferredcites →https://doi.org/10.3390/rs11070887openalex-extracted
Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France
inferredcites →https://doi.org/10.3390/rs11141666openalex-extracted
Automated Near-Real-Time Mapping and Monitoring of Rice Extent, Cropping Patterns, and Growth Stages in Southeast Asia Using Sentinel-1 Time Series on a Google Earth Engine Platform
inferredcites →https://doi.org/10.3390/rs71215805openalex-extracted
Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices
inferredcites →https://doi.org/10.3390/s150100769openalex-extracted
Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review
inferredcites →https://doi.org/10.3390/s18010185openalex-extracted
Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China
inferredcites →https://doi.org/10.3390/s18010316openalex-extracted
Mapping of Rice Varieties and Sowing Date Using X-Band SAR Data
inferredcites →https://doi.org/10.3390/s18061901openalex-extracted
Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets
inferredcites →https://doi.org/10.5589/m12-012openalex-extracted
Towards operational radar-only crop type classification: comparison of a traditional decision tree with a random forest classifier
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