Chen Zhang
George Mason University
45 Papers
140 Citations
Chen Zhang is an academic researcher from George Mason University. The author has contributed to research in topics: Web service & Computer science. The author has an hindex of 11, co-authored 44 publications. Previous affiliations of Chen Zhang include United States Department of Agriculture.
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Papers
Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples.
TL;DR: A transfer learning (TL) workflow is proposed to use the classification model trained in contiguous U.S.A. (CONUS) to identify crop types in other regions to provide new options for crop classification in regions of training samples shortage.
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Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps
TL;DR: This paper is the first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps using Cropland Data Layer time series as reference data and multi-layer artificial neural network as prediction model.
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A review of remote sensing in flood assessment
Li Lin,Liping Di,Eugene Genong Yu,Lingjun Kang,Ranjay Shrestha,Md. Shahinoor Rahman,Junmei Tang,Meixia Deng,Ziheng Sun,Chen Zhang,Lei Hu +10 more
- 18 Jul 2016
TL;DR: In this article, a brief review and comparison of major optical and radar satellite sensors which are currently adopted in flood assessment is provided. But, the authors do not consider the use of satellite data in flood prediction.
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Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer
TL;DR: A new perspective and detailed guidance for rapid in-season mapping of corn and soybeans fields without ground truth data for the current year is provided, which can be potentially applied to identify more diverse crop types and scaled up to the entire United States.
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Rapid Flood Progress Monitoring in Cropland with NASA SMAP
TL;DR: This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information and indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation.
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