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Pengyu Hao
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2020 – today
- 2022
- [j12]Mingquan Wu
, Huichun Ye
Operation Status Comparison Monitoring of China's Southeast Asian Industrial Parks before and after COVID-19 Using Nighttime Lights Data. ISPRS Int. J. Geo Inf. 11(2): 122 (2022) - [j11]Qiang Zhao
An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote. Sens. 14(8): 1863 (2022) - 2021
- [j10]Chen Zhang
Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer. Int. J. Appl. Earth Obs. Geoinformation 102: 102374 (2021) - [c5]Pengyu Hao, Liping Di, Eugene Yu, Liying Guo, Ziheng Sun, Haoteng Zhao
Using machine learning and trapezoidal model to derive All-weather ET from Remote sensing Images and Meteorological Data. Agro-Geoinformatics 2021: 1-4 - [c4]Haoteng Zhao
Impacts of Soil Moisture on Crop Health: A Remote Sensing Perspective. Agro-Geoinformatics 2021: 1-4 - [c3]Chen Zhang
Applying Machine Learning to Cropland Data Layer for Agro-Geoinformation Discovery. IGARSS 2021: 1149-1152 - 2020
- [j9]Chen Zhang
AgKit4EE: A toolkit for agricultural land use modeling of the conterminous United States based on Google Earth Engine. Environ. Model. Softw. 129: 104694 (2020)
2010 – 2019
- 2019
- [j8]Pengyu Hao
New Workflow of Plastic-Mulched Farmland Mapping using Multi-Temporal Sentinel-2 data. Remote. Sens. 11(11): 1353 (2019) - 2018
- [j7]Pengyu Hao
Annual Cropland Mapping Using Reference Landsat Time Series - A Case Study in Central Asia. Remote. Sens. 10(12): 2057 (2018) - [c2]Chang-An Liu, Zhongxin Chen, Pengyu Hao, Kun Li, Xiaochen Wang:
LAI Retrieval of Winter Wheat using Simulated Compact SAR Data through GA-PLS Modeling. IGARSS 2018: 3840-3843 - 2017
- [j6]Mingquan Wu, Wenjiang Huang, Zheng Niu, Yu Wang, Changyao Wang, Wang Li, Pengyu Hao, Bo Yu:
Fine crop mapping by combining high spectral and high spatial resolution remote sensing data in complex heterogeneous areas. Comput. Electron. Agric. 139: 1-9 (2017) - 2016
- [j5]Pengyu Hao
Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China. ISPRS Int. J. Geo Inf. 5(5): 67 (2016) - [j4]Mingquan Wu, Chaoyang Wu, Wenjiang Huang, Zheng Niu, Changyao Wang, Wang Li, Pengyu Hao:
An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Inf. Fusion 31: 14-25 (2016) - [c1]Pengyu Hao, Li Wang
Using historical NDVI time series to classify crops at 30m spatial resolution: A case in Southeast Kansas. IGARSS 2016: 6316-6319 - 2015
- [j3]Pengyu Hao
Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA. Remote. Sens. 7(5): 5347-5369 (2015) - [j2]Mingquan Wu, Xiaoyang Zhang
Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring. Remote. Sens. 7(12): 16293-16314 (2015) - 2014
- [j1]Pengyu Hao
The Potential of Time Series Merged from Landsat-5 TM and HJ-1 CCD for Crop Classification: A Case Study for Bole and Manas Counties in Xinjiang, China. Remote. Sens. 6(8): 7610-7631 (2014)
Coauthor Index
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