NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY

13 citations as recorded by crossref.

1. A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems L. G. Poley & G. J. McDermid
2. Quantifying Aboveground Biomass of Shrubs Using Spectral and Structural Metrics Derived from UAS Imagery L. Poley et al.
3. Very high resolution aerial data to support multi-temporal precision agriculture information management L. Pádua et al.
4. Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time “Out of the Lab” Data Processing Z. Varga et al.
5. From Application-Driven Growth to Paradigm Shift: Scientific Evolution and Core Bottleneck Analysis in the Field of UAV Remote Sensing D. Huang et al.
6. Urban Growth Patterns and Forest Carbon Dynamics in the Metropolitan Twin Cities of Islamabad and Rawalpindi, Pakistan A. Mannan et al.
7. Flight Altitude and Sensor Angle Affect Unmanned Aerial System Cotton Plant Height Assessments O. Adedeji et al.
8. Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry R. Nuijten et al.
9. Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages Y. Dai et al.
10. Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data C. Chen et al.
11. A Review of Estimation Methods for Aboveground Biomass in Grasslands Using UAV C. Bazzo et al.
12. Spatio-Temporal Estimation of Biomass Growth in Rice Using Canopy Surface Model from Unmanned Aerial Vehicle Images C. Peprah et al.
13. Estimation of Agronomic Characters of Wheat Based on Variable Selection and  Machine Learning Algorithms D. Wang et al.