Results 21 to 30 of about 21,657,058 (394)

DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification [PDF]

arXiv
Recent advancements in remote sensing (RS) technologies have shown their potential in accurately classifying local climate zones (LCZs). However, traditional scene-level methods using convolutional neural networks (CNNs) often struggle to integrate prior
Ma, Xianping, Pun, Man-On, Sui, Jialu, Wu, Qianqian   +3 more
core   +1 more source

Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping

Applied Sciences, 2021
Crop identification is key to global food security. Due to the large scale of crop estimation, the science of remote sensing was able to do well in this field.
Shilan Felegari, Alireza Sharifi, Kamran Moravej, Muhammad Amin, Ahmad Golchin, Anselme Muzirafuti, Aqil Tariq, Na Zhao   +7 more
doaj   +1 more source

Crop growth monitoring through Sentinel and Landsat data based NDVI time-series [PDF]

Компьютерная оптика, 2020
Crop growth monitoring is an important phenomenon for agriculture classification, yield estimation, agriculture field management, improve productivity, irrigation, fertilizer management, sustainable agricultural development, food security and to ...
M.S. Boori , K. Choudhary, A.V. Kupriyanov   +2 more
doaj   +1 more source

Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

Remote Sensing, 2022
Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy.
Md Yeasin, Dipanwita Haldar, Suresh Kumar, Ranjit Kumar Paul, Sonaka Ghosh   +4 more
doaj   +1 more source

A Sentinel-2 Dataset for Uganda [PDF]

Data, 2021
Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda.
openaire   +3 more sources

Assessing Combinations of Landsat, Sentinel-2 and Sentinel-1 Time series for Detecting Bark Beetle Infestations

GIScience & Remote Sensing, 2023
Bark beetle infestations are among the most substantial forest disturbance agents worldwide. Moreover, as a consequence of global climate change, they have increased in frequency and in the size and number of affected areas.
S. König, Frank Thonfeld, M. Förster, O. Dubovyk, M. Heurich   +4 more
semanticscholar   +1 more source

Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars

Geomatics, Natural Hazards & Risk, 2023
This research compares the use of the SAR (Sentinel-1) and Optical (Sentinel-2) sensors in identifying and mapping burnt and unburnt scars are rising during a bushfire in southeastern Australia and Margalla Hills, Islamabad, Pakistan, in 2019 and 2020 ...
A. Tariq, Y. Jiango, Linlin Lu, Ahsan Jamil, I. Al-ashkar, Muhammad Kamran, Ayman El Sabagh   +6 more
semanticscholar   +1 more source

Fusion of Sentinel-2 images [PDF]

Remote Sensing of Environment, 2016
Sentinel-2 is a very new programme of the European Space Agency (ESA) that is designed for fine spatial resolution global monitoring. Sentinel-2 images provide four 10 m bands and six 20 m bands. To provide more explicit spatial information, this paper aims to downscale the six 20 m bands to 10 m spatial resolution using the four directly observed 10 m
Peter M. Atkinson, Peter M. Atkinson, Peter M. Atkinson, Qunming Wang, Wenzhong Shi, Zhongbin Li   +5 more
openaire   +4 more sources

LEARNING HARMONISED PLEIADES AND SENTINEL-2 SURFACE REFLECTANCES [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021
Abstract. In this paper, we investigate the use of machine-learning techniques in order to produce harmonised surface reflectances between Sentinel-2 and Pleiades images, and reduce the impact of the differences in sensors, view conditions, and atmospheric correction differences between them.
Michel, J., Inglada, J.
openaire   +5 more sources

Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Remote Sensing, 2021
Sentinel-2 images have been widely used in studying land surface phenomena and processes, but they inevitably suffer from cloud contamination. To solve this critical optical data availability issue, it is ideal to fuse Sentinel-1 and Sentinel-2 images to
Quan Xiong, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, Xuli Zan, Lin Zhang, Dehai Zhu, Zhe Liu, Xiaochuang Yao, Xiaodong Zhang   +10 more
doaj   +1 more source