Zhong et al., 2016 - Google Patents
Rapid corn and soybean mapping in US Corn Belt and neighboring areasZhong et al., 2016
View HTML- Document ID
- 11258307291041807732
- Author
- Zhong L
- Yu L
- Li X
- Hu L
- Gong P
- Publication year
- Publication venue
- Scientific reports
External Links
Snippet
The goal of this study was to promptly map the extent of corn and soybeans early in the growing season. A classification experiment was conducted for the US Corn Belt and neighboring states, which is the most important production area of corn and soybeans in the …
- 235000002017 Zea mays subsp mays 0 title abstract description 79
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/06—Investment, e.g. financial instruments, portfolio management or fund management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0201—Market data gathering, market analysis or market modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhong et al. | Rapid corn and soybean mapping in US Corn Belt and neighboring areas | |
You et al. | The 10-m crop type maps in Northeast China during 2017–2019 | |
Gao et al. | Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities | |
Wang et al. | Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive | |
Johnson et al. | Pre-and within-season crop type classification trained with archival land cover information | |
Wardlow et al. | A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas | |
Zhong et al. | A phenology-based approach to map crop types in the San Joaquin Valley, California | |
Brown et al. | Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data | |
Arvor et al. | Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil | |
Zhong et al. | Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery | |
Vintrou et al. | Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products | |
Kassawmar et al. | Reducing landscape heterogeneity for improved land use and land cover (LULC) classification across the large and complex Ethiopian highlands | |
Bala et al. | Correlation between potato yield and MODIS‐derived vegetation indices | |
Feyisa et al. | Characterizing and mapping cropping patterns in a complex agro-ecosystem: An iterative participatory mapping procedure using machine learning algorithms and MODIS vegetation indices | |
Htitiou et al. | A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: A machine learning approach | |
Cao et al. | Mapping paddy rice using Landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988− 2017 | |
Tang et al. | Mapping forest disturbance across the China–Laos border using annual Landsat time series | |
Dhakar et al. | Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques | |
Zhou et al. | Integration of maximum crop response with machine learning regression model to timely estimate crop yield | |
Chaves et al. | CBERS data cubes for land use and land cover mapping in the Brazilian Cerrado agricultural belt | |
Liu et al. | Identifying major crop types in Eastern Canada using a fuzzy decision tree classifier and phenological indicators derived from time series MODIS data | |
Nieto et al. | An integrated approach of field, weather, and satellite data for monitoring maize phenology | |
Sankey et al. | Post‐socialist cropland changes and abandonment in Mongolia | |
Upadhyay et al. | Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach | |
Deines et al. | Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020 |