A5108062794- Remote Sensing in Agriculture
- Water Quality Monitoring and Analysis
- Spectroscopy and Chemometric Analyses
- Remote Sensing and LiDAR Applications
- Smart Agriculture and AI
- Leaf Properties and Growth Measurement
- Coastal wetland ecosystem dynamics
- Constructed Wetlands for Wastewater Treatment
- Aquatic Ecosystems and Phytoplankton Dynamics
- Potato Plant Research
- Horticultural and Viticultural Research
Inner Mongolia Agricultural University
2021-2025
Technical University of Munich
2025
Nanjing University
2008
Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types spectral bands indices (SIs) coupled with ensemble learning models (ELMs) retrieving plant concentration (PNC) aboveground biomass (AGB) potato from Sentinel-2 images. Cloud-free imagery was acquired during tuber-formation to starch-accumulation stages 2020...
Many empirical models based on hyperspectral indices (HIs) have been developed to estimate nitrogen (N) status of crops. However, most the researches by far focused identification sensitive bands HIs, and not identified importance formula formats achieve their best performance. The current study aimed investigate response band optimization canopy N concentration (CNC) potato (Solanum tuberosum L.) plants, verify performance HIs through optimized algorithms a multi-site -year study. Three...
Abstract Spectral indices based on unmanned aerial vehicle (UAV) multispectral images combined with machine learning algorithms can more effectively assess chlorophyll content in plants, which plays a crucial role plant nutrition diagnosis, yield estimation and better understanding of environment interactions. Therefore, the aim this study was to use UAV-based spectral deriving from as inputs different models predict canopy potato crops. The relative obtained using SPAD meter. Random Forest...