Yang Liu
Orcid: 0000-0003-3862-441XAffiliations:
- Beijing Academy of Agriculture and Forestry Sciences, Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, China
According to our database1,
Yang Liu
authored at least 22 papers
between 2022 and 2024.
Collaborative distances:
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Bibliography
2024
Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation.
Remote. Sens., March, 2024
Analyzing winter-wheat biochemical traits using hyperspectral remote sensing and deep learning.
Comput. Electron. Agric., 2024
A novel vegetation-water resistant soil moisture index for remotely assessing soil surface moisture content under the low-moderate wheat cover.
Comput. Electron. Agric., 2024
Exploring multi-features in UAV based optical and thermal infrared images to estimate disease severity of wheat powdery mildew.
Comput. Electron. Agric., 2024
Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data.
Comput. Electron. Agric., 2024
Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques.
Comput. Electron. Agric., 2024
Estimating potato above-ground biomass based on vegetation indices and texture features constructed from sensitive bands of UAV hyperspectral imagery.
Comput. Electron. Agric., 2024
Estimation of potato yield using a semi-mechanistic model developed by proximal remote sensing and environmental variables.
Comput. Electron. Agric., 2024
A model suitable for estimating above-ground biomass of potatoes at different regional levels.
Comput. Electron. Agric., 2024
2023
Mapping cropland rice residue cover using a radiative transfer model and deep learning.
Comput. Electron. Agric., December, 2023
Comput. Electron. Agric., November, 2023
Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images.
Remote. Sens., October, 2023
Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements.
Comput. Electron. Agric., October, 2023
Leaf area index estimation under wheat powdery mildew stress by integrating UAV‑based spectral, textural and structural features.
Comput. Electron. Agric., October, 2023
Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages.
Comput. Electron. Agric., September, 2023
Land-Use Mapping with Multi-Temporal Sentinel Images Based on Google Earth Engine in Southern Xinjiang Uygur Autonomous Region, China.
Remote. Sens., August, 2023
Comparison of Different Dimensional Spectral Indices for Estimating Nitrogen Content of Potato Plants over Multiple Growth Periods.
Remote. Sens., February, 2023
2022
Estimation of Aboveground Biomass of Potatoes Based on Characteristic Variables Extracted from UAV Hyperspectral Imagery.
Remote. Sens., 2022
Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral images and Machine-Learning Regression.
Remote. Sens., 2022
Estimation of Potato Above-Ground Biomass Based on Vegetation Indices and Green-Edge Parameters Obtained from UAVs.
Remote. Sens., 2022
Comparison of Winter Wheat Yield Estimation Based on Near-Surface Hyperspectral and UAV Hyperspectral Remote Sensing Data.
Remote. Sens., 2022
Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images.
Comput. Electron. Agric., 2022