Regional scale crop mapping using multi-temporal satellite imagery

41 citations as recorded by crossref.

1. An auto-encoder based classifier for crop mapping from multitemporal multispectral imagery M. Hamidi et al. 10.1080/01431161.2020.1820619
2. Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds K. Bakuła et al. 10.3390/ijgi9040248
3. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data N. Kussul et al. 10.1109/LGRS.2017.2681128
4. Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices X. Chen et al. 10.3390/rs12193202
5. Land-cover classification with high-resolution remote sensing images using transferable deep models X. Tong et al. 10.1016/j.rse.2019.111322
6. A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach A. Htitiou et al. 10.1080/10106049.2020.1768593
7. The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region A. Htitiou et al. 10.1007/s41976-019-00023-9
8. Crop-specific phenomapping by fusing Landsat and Sentinel data with MODIS time series J. Schreier et al. 10.1080/22797254.2020.1831969
9. Applicability and Analysis of the Results of Non-Contact Methods in Determining the Vertical Displacements of Timber Beams B. Kovačič et al. 10.3390/app11198936
10. Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery N. Kussul et al. 10.1080/22797254.2018.1454265
11. Object-based machine learning approach for soybean mapping using temporal sentinel-1/sentinel-2 data M. Kumari et al. 10.1080/10106049.2021.1952314
12. Field validation of NDVI to identify crop phenological signatures M. Bhatti et al. 10.1007/s11119-024-10165-6
13. Biophysical parameters mapping within the SPOT-5 Take 5 initiative A. Shelestov et al. 10.1080/22797254.2017.1324743
14. Crop Type Mapping and Winter Wheat Yield Prediction Utilizing Sentinel-2: A Case Study from Upper Thracian Lowland, Bulgaria I. Kamenova et al. 10.3390/rs16071144
15. A Spatial–Temporal Depth-Wise Residual Network for Crop Sub-Pixel Mapping from MODIS Images Y. Wang et al. 10.3390/rs14215605
16. Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine J. Xiong et al. 10.3390/rs9101065
17. Methods of essential variables determination for the Earth’s surface state assessing B. YAILYMOV et al. 10.15407/knit2018.04.026
18. Remote Sensing and Cropping Practices: A Review A. Bégué et al. 10.3390/rs10010099
19. Estimating winter wheat straw amount and spatial distribution in Qihe County, China, using GF-1 satellite images A. Huawei Mou et al. 10.1063/5.0033639
20. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity F. Waldner et al. 10.1080/01431161.2016.1194545
21. Deep learning based crop-type mapping using SAR and optical data fusion M. Hamidi et al. 10.1016/j.jag.2024.103860
22. Detection of seasonal land use pattern and irrigated crops in drylands using multi-temporal sentinel images Z. Makhamreh et al. 10.1007/s12665-022-10249-4
23. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data N. Kussul et al. 10.1109/JSTARS.2016.2560141
24. Automatic Detection of Dominant Crop Types in Poland Based on Satellite Images J. Pluto-Kossakowska et al. 10.2478/arsa-2020-0013
25. On the target complex program of NAS of Ukraine «Aerospace environmental surveillance for sus-tainable development and security as a national segment of Horizon-2020 ERA-PLANET project (ERA-PLANET/UA) O. Fedorov 10.15407/visn2017.12.035
26. Classification of multi-temporal spectral indices for crop type mapping: a case study in Coalville, UK Y. Palchowdhuri et al. 10.1017/S0021859617000879
27. Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps C. Zhang et al. 10.1016/j.compag.2019.104989
28. Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models S. Skakun et al. 10.3390/rs11151768
29. SATELLITE AGROMONITORING IN UKRAINE: RESULTS OF SENTINEL-2 FOR AGRICULTURE PROJECT AND FURTHER PROSPECTS N. Kussul et al. 10.15407/visn2016.12.099
30. Crop Mapping Using Sentinel Full-Year Dual-Polarized SAR Data and a CPU-Optimized Convolutional Neural Network With Two Sampling Strategies S. Liu et al. 10.1109/JSTARS.2021.3094973
31. Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region C. Sun et al. 10.3390/s19102401
32. Satellite agromonitoring in Ukraine N. Kussul et al. 10.15407/visn2016.02.096
33. Efficient hyper-parameter determination for regularised linear BRDF parameter retrieval J. Zobitz et al. 10.1080/01431161.2019.1667552
34. Water requirements for irrigated crops in semi-arid region in Jordan using sentinel satellite images A. Al-Azeez Hdoush 10.1016/j.pce.2020.102949
35. Enhancing adaptation to climate change through groundwater-based irrigation O. Fynn et al. 10.1007/s40899-023-00814-5
36. Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium K. Van Tricht et al. 10.3390/rs10101642
37. Crop cover identification based on different vegetation indices by using machine learning algorithms S. Pargaien et al. 10.35208/ert.1446909
38. Monitoring land cover change detection with NOAA-AVHRR and MODIS remotely sensed data in the North and West of Africa from 1982 to 2015 M. Henchiri et al. 10.1007/s11356-019-07216-1
39. Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition J. Pluto-Kossakowska 10.3390/agriculture11100999
40. Classification of remote sensing time series and similarity metrics for crop type verification D. Dimov 10.1117/1.JRS.16.024519
41. Sentinel SAR-optical fusion for improving in-season wheat crop mapping at a large scale using machine learning and the Google Earth engine platform L. Zoungrana et al. 10.1007/s12518-023-00545-4