Fikriyah, 2018 - Google Patents
Detecting rice crop establisment methods using Sentinel-1 multi temporal imagery in Nueva Ecija, PhillipinesFikriyah, 2018
View PDF- Document ID
- 3338877444598502742
- Author
- Fikriyah V
- Publication year
External Links
Snippet
Rice is a major staple food, and monitoring its production and management requires detailed spatial and temporal information. However, conventional methods for obtaining this information are often timeconsuming and labour intensive. Remote sensing data have been …
- 235000007164 Oryza sativa 0 title abstract description 307
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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Clauss et al. | Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data | |
You et al. | Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine | |
Abu et al. | Detecting cocoa plantations in Côte d’Ivoire and Ghana and their implications on protected areas | |
Thieme et al. | Using NASA Earth observations and Google Earth Engine to map winter cover crop conservation performance in the Chesapeake Bay watershed | |
Güler et al. | Using landsat data to determine land use/land cover changes in Samsun, Turkey | |
Poulin et al. | Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes | |
US20180349520A1 (en) | Methods for agricultural land improvement | |
Redowan et al. | Analysis of forest cover change at Khadimnagar National Park, Sylhet, Bangladesh, using Landsat TM and GIS data | |
Fikriyah et al. | Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data | |
Schmitt-Harsh | Landscape change in Guatemala: Driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010 | |
Pazhanivelan et al. | Rice crop monitoring and yield estimation through COSMO Skymed and TerraSAR-X: A SAR-based experience in India | |
Nguyen et al. | Rice-planted area extraction by time series analysis of ENVISAT ASAR WS data using a phenology-based classification approach: A case study for Red River Delta, Vietnam | |
Ghosh et al. | Kharif rice growth and area monitoring in Gosaba CD block of Indian Sundarbans region using multi-temporal dual-pol SAR data | |
Lam-Dao et al. | Effects of changing rice cultural practices on C-band synthetic aperture radar backscatter using Envisat advanced synthetic aperture radar data in the Mekong River Delta | |
Jain et al. | Rice (kharif) production estimation using SAR data of different satellites and yield models: A comparative analysis of the estimates generated under FASAL project | |
Fikriyah | Detecting rice crop establisment methods using Sentinel-1 multi temporal imagery in Nueva Ecija, Phillipines | |
Burchfield et al. | Application of machine learning to prediction of vegetation health | |
Fan et al. | Sent2Agri System Based Crop Type Mapping in Yellow River Irrigation Area | |
Liou | Rapid identification of evapotranspiration features using normalized difference latent heat index (NDLI) | |
Gaikwad et al. | Estimation of area sown and sowing dates of inseason rabi crops using sentinel-2 time series data | |
Choudhury et al. | Estimation of rice growth parameter and crop phenology with conjunctive use of RADARSAT and ENVISAT | |
Villano et al. | Separability of Transplanted and Direct Seeded Rice Using Multi-Temporal SENTINEL-1A Data | |
Mortel | Remote sensing of crop inventories and crop model simulations for irrigation management along the Guyana coastal plains | |
Niraimathi et al. | Integrating Sentinel-1 data and machine learning for effective paddy field monitoring in Cauvery Delta Zone, Tamil Nadu, India | |
Maeda et al. | Monte Carlo simulation and remote sensing applied to agricultural survey sampling strategy in Taita Hills, Kenya |