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Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm

Yuanhuizi He, Changlin Wang, Fang Chen, Huicong Jia, Dong Liang, Aqiang Yang
2019 Remote Sensing  
In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm  ...  Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion.  ...  Conclusions In this study, Landsat-8 OLI and Sentinel-2 MSI data were combined for wheat extraction and mapping using the random forest machine learning algorithm.  ... 
doi:10.3390/rs11050535 fatcat:jcedakw5ozaqzgb2oymhatdh3u

Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

Dongyan Zhang, 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China, Mengru Zhang, Fenfang Lin, Zhenggao Pan, Fei Jiang, Liang He, Hang Yang, Ning Jin, 2. Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, Henan, China, 3. School of Informatics and Engineering, Suzhou University, Suzhou 234000, Anhui, China, 4. National Meteorological Center, Beijing 100081, China (+1 others)
2021 International Journal of Agricultural and Biological Engineering  
The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020, and Sentinel-2 imagery was used to verify the results.  ...  In this study, high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China.  ...  Random Forest Algorithm for selecting feature and identifying winter wheat Random Forest (RF) is a machine learning algorithm.  ... 
doi:10.25165/j.ijabe.20221501.6917 fatcat:y4beksenbvc57dbw6vqpa4rtdm

Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region

Chuanliang Sun, Yan Bian, Tao Zhou, Jianjun Pan
2019 Sensors  
This paper uses multi-source (Sentinel-1, Sentinel-2, and Landsat-8) and multi-temporal data to identify crop types.  ...  The combination of Sentinel-1, Sentinel-2, and Landsat-8 data provided the best overall accuracy (0.93) and Kappa coefficient (0.91).  ...  Acknowledgments: We thank the China Scholar Council (CSC) for its support. We also thank Genping Zhao's professional advice. We also thank the two peer reviewers' valuable comments.  ... 
doi:10.3390/s19102401 fatcat:pecwyhxnxvbc7pzwueu3qnmvhi

Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)

Wenmin Zhang, Martin Brandt, Alexander V. Prishchepov, Zhaofu Li, Chunguang Lyu, Rasmus Fensholt
2021 Remote Sensing  
Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery.  ...  Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in  ...  Data Availability Statement: Not applicable. Acknowledgments: We thank Huan Zhang, Wangxin Yang, Lei Han for taking winter wheat sample from the field work.  ... 
doi:10.3390/rs13061170 fatcat:tmfd732x6faovouwmpix6ogryq

Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

Abdelhakim Amazirh, El Houssaine Bouras, Luis Enrique Olivera-Guerra, Salah Er-Raki, Abdelghani Chehbouni
2021 Remote Sensing  
Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input.  ...  The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field  ...  Acknowledgments: The authors acknowledge the Joint International Laboratory TREMA (https: // (accessed on 7 August 2021) (IRD, UCAM, DMN, CNESTEN, ABHT, and ORMVAH) for the in situ data  ... 
doi:10.3390/rs13163181 fatcat:pchkjn3f7nf45hwbporiozqqcq

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-ichiro Mochizuki
2017 GIScience & Remote Sensing  
for band 2, 3, 4, 5, 11 and 12, potato-beetroot 280 for band 11and potato-grass for band 6, 7, 8 and 11 (p < 0.05, based on Tukey-Kramer 281 test).  ...  TerraSAR-X Random forests Japan Beans, Beet, Grass, Maize, Potato, Winter late early m id late early m id late early m id late early m id late early m id late early m id late 7 June 19 June  ...  Application of polarization 508 signature to land cover scattering mechanism analysis and classification using 509 multi-temporal C-band polarimetric RADARSAT-2 imagery.  ... 
doi:10.1080/15481603.2017.1351149 fatcat:bjrzxq63abbineqjluufdvyxkq

Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy

Vasileios Sitokonstantinou, Ioannis Papoutsis, Charalampos Kontoes, Alberto Arnal, Ana Pilar Andrés, José Angel Zurbano
2018 Remote Sensing  
The scheme makes use of supervised classifiers Support Vector Machines (SVMs) and Random Forest (RF) to discriminate among the various crop types, based on a large variable space of Sentinel-2 imagery  ...  However, low (e.g., MODIS 250 m) and moderate (e.g., Landsat 30 m) spatial resolution data, with pixel sizes comparable to the parcel area, provide suboptimal thematic accuracy [7] .  ...  free access to Sentinel-2 MSI and Landsat-8 OLI images, correspondingly.  ... 
doi:10.3390/rs10060911 fatcat:qzmaueoxe5h2rntlwsxxir5mxa

AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine

Gaoxiang Yang, Weiguo Yu, Xia Yao, Hengbiao Zheng, Qiang Cao, Yan Zhu, Weixing Cao, Tao Cheng
2021 International Journal of Applied Earth Observation and Geoinformation  
After extracting spatial objects from Sentinel-2 imagery in the season of 2017-2018, this method performed recognition of winter wheat objects based on the unique phenology and spectral features of winter  ...  When compared with agricultural census data, the winter wheat area accounted for 99% and 90% of the variability at the municipal and county levels.  ...  Supplementary material Supplementary data to this article can be found online at https://doi. org/10.1016/j.jag.2021.102446.  ... 
doi:10.1016/j.jag.2021.102446 fatcat:wd7n53ql6fbk7jusva4wcdu6h4

Early-season mapping of winter wheat in China based on Landsat and Sentinel images

Jie Dong, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, Wenping Yuan
2020 Earth System Science Data  
Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces  ...  The 30 m winter wheat maps in China are available via an open-data repository (DOI:, Dong et al., 2020a).  ...  This paper was edited by David Carlson and reviewed by Sergii Skakun and one anonymous referee.  ... 
doi:10.5194/essd-12-3081-2020 fatcat:dkxkfj4bvfhfjlixb3ne2jhkw4

Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

Lingbo Yang, Lamin Mansaray, Jingfeng Huang, Limin Wang
2019 Remote Sensing  
It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource  ...  Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification.  ...  Acknowledgments: We thank Weiwei Liu and Huaiyue Peng of the Key Laboratory of Agricultural Remote Sensing and Information Systems of Zhejiang University, for their help in ground data collection.  ... 
doi:10.3390/rs11050514 fatcat:h2kd5e2shvcudjwsskecvpluym

Combining ASNARO-2 XSAR HH and Sentinel-1 C-SAR VH/VV Polarization Data for Improved Crop Mapping

Rei Sonobe
2019 Remote Sensing  
Comparisons between ASNARO-2 XSAR and Sentinel-1 C-SAR using data obtained in June and August 2018 were conducted to identify five crop types (beans, beetroot, maize, potato, and winter wheat), and the  ...  The Advanced Satellite with New system ARchitecture for Observation-2 (ASNARO-2), which carries the X-band Synthetic Aperture Radar (XSAR), was launched on 17 January 2018 and is expected to be used to  ...  Acknowledgments: The author would like to thank Hiroshi Tani from Hokkaido University, Tokachi Nosai for providing the field data, and the Japan Space Imaging Corporation and Nippon Electric Company for  ... 
doi:10.3390/rs11161920 fatcat:mnryatjvlnb77idscozh3dm5iq

Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping

Andrii Shelestov, Mykola Lavreniuk, Nataliia Kussul, Alexei Novikov, Sergii Skakun
2017 Frontiers in Earth Science  
scale (e.g., country level) and multiple sensors (e.g., Landsat-8 and Sentinel-2).  ...  In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (∼28,100 km 2 and 1.0 M  ...  ACKNOWLEDGMENTS This research was conducted in the framework of the "Large scale crop mapping in Ukraine using SAR and optical data fusion" Google Earth Engine Research Award funded by the Google Inc.  ... 
doi:10.3389/feart.2017.00017 fatcat:ghufyqrfgjdfzgblwxthctnk7m

The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

Shengwei Liu, Dailiang Peng, Bing Zhang, Zhengchao Chen, Le Yu, Junjie Chen, Yuhao Pan, Shijun Zheng, Jinkang Hu, Zihang Lou, Yue Chen, Songlin Yang
2022 Remote Sensing  
Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively.  ...  A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established.  ...  Analysis of the J-M Distance Results Figure 2 . 2 Figure 2. Results for the accuracy achieved using the random forest algorithm.  ... 
doi:10.3390/rs14040893 fatcat:y6gzetekj5crde2x7shqurckzu

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

Kristof Van Tricht, Anne Gobin, Sven Gilliams, Isabelle Piccard
2018 Remote Sensing  
Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium.  ...  An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77.  ...  Acknowledgments: We would like to thank the BELCAM team for their constructive remarks. We are also grateful to the four anonymous reviewers that have provided helpful feedback on this work.  ... 
doi:10.3390/rs10101642 fatcat:m6muqtpzxjcsfadeygh6wsncoi

The Classification Performance and Mechanism of Machine Learning Algorithms in Winter Wheat Mapping Using Sentinel-2 10 m Resolution Imagery

Peng Fang, Xiwang Zhang, Panpan Wei, Yuanzheng Wang, Huiyi Zhang, Feng Liu, Jun Zhao
2020 Applied Sciences  
In this paper, based on Google Earth Engine (GEE), Sentinel-2 10 m resolution images during a specific phenological period of winter wheat were obtained.  ...  Then, support vector machine (SVM), random forest (RF), and classification and regression tree (CART) machine learning algorithms were employed to identify and map winter wheat in a large-scale area.  ...  Major Research Projects of the Ministry of Education, and the cooperation base open fund of the Key Laboratory of Geospatial Technology for the middle and lower Yellow River regions and the CPGIS.  ... 
doi:10.3390/app10155075 fatcat:e3tm3rg4qnd27ojywloylupjbe
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