Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China

32 citations as recorded by crossref.

1. Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management C. Yang et al. https://doi.org/10.3390/s20185354
2. Independent estimates of net carbon uptake in croplands: UAV-LiDAR and machine learning vs. eddy covariance J. Revenga et al. https://doi.org/10.1016/j.agrformet.2024.110106
3. Creation of a Virtual Tour .Exe Utilizing Very High-Resolution RGB UAV Data U. Sefercik et al. https://doi.org/10.30897/ijegeo.1102575
4. Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass S. Cooper et al. https://doi.org/10.3390/rs9060531
5. A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping L. Feng et al. https://doi.org/10.1016/j.compag.2021.106033
6. Fast non-rigid image feature matching for agricultural UAV via probabilistic inference with regularization techniques Z. Yu et al. https://doi.org/10.1016/j.compag.2017.10.002
7. Sparse Covariance Matrix Reconstruction‐Based Nulling Broadening for UAV 2D Antenna Arrays C. Han et al. https://doi.org/10.1155/2022/4987990
8. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data L. Han et al. https://doi.org/10.1186/s13007-019-0394-z
9. Predicting Zea mays Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images G. Wu et al. https://doi.org/10.3389/fpls.2019.01251
10. Prediction of morpho-physiological traits in sugarcane using aerial imagery and machine learning C. Poudyal et al. https://doi.org/10.1016/j.atech.2022.100104
11. An Efficient Approach Based on UAV Orthographic Imagery to Map Paddy With Support of Field-Level Canopy Height From Point Cloud Data H. Liu et al. https://doi.org/10.1109/JSTARS.2018.2829218
12. Merging Unmanned Aerial Systems (UAS) Imagery and Echo Soundings with an Adaptive Sampling Technique for Bathymetric Surveys L. Alvarez et al. https://doi.org/10.3390/rs10091362
13. UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial C. Hütt et al. https://doi.org/10.1007/s41064-022-00228-6
14. Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution A. Burkart et al. https://doi.org/10.1007/s11119-017-9504-y
15. High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing F. Holman et al. https://doi.org/10.3390/rs8121031
16. Reproducibility of UAV-based earth topography reconstructions based on Structure-from-Motion algorithms F. Clapuyt et al. https://doi.org/10.1016/j.geomorph.2015.05.011
17. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm G. Nguyen & S. Norton https://doi.org/10.3390/plants9070817
18. Farklı insansız hava araçlarından (İHA) elde edilen veriler ile buğday bitkisinin boyunun belirlenmesi N. SÖNMEZ et al. https://doi.org/10.29136/mediterranean.823440
19. Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models M. Aboutalebi et al. https://doi.org/10.3390/rs12010050
20. Use of remotely piloted aircraft in precision agriculture: a review L. Dos Santos et al. https://doi.org/10.15446/dyna.v86n210.74701
21. Literature Review on Multi-Spectral Imaging for Fruits and Vegetable S. Gaikwad https://doi.org/10.2139/ssrn.3905180
22. Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua I. Luna & A. Lobo https://doi.org/10.3390/rs8060500
23. Utilization of UAV Remote Sensing in Plant-Based Field Experiments: A Case Study of the Evaluation of LAI in a Small-Scale Sweetcorn Experiment H. Jung et al. https://doi.org/10.3390/agriculture13030561
24. Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques S. Varela et al. https://doi.org/10.3390/rs10020343
25. Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey J. Liu et al. https://doi.org/10.3390/rs13214387
26. Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging A. Matese et al. https://doi.org/10.1080/01431161.2016.1226002
27. Engaging with the Canopy—Multi-Dimensional Vegetation Mark Visualisation Using Archived Aerial Images G. Verhoeven & F. Vermeulen https://doi.org/10.3390/rs8090752
28. Assessing the Temporal and Spatial Variability of Coffee Plantation Using RPA-Based RGB Imaging M. Martello et al. https://doi.org/10.3390/drones6100267
29. High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems A. Patrick & C. Li https://doi.org/10.3390/rs9121250
30. Development and Validation of Methodology for Estimating Potato Canopy Structure for Field Crop Phenotyping and Improved Breeding F. de Jesus Colwell et al. https://doi.org/10.3389/fpls.2021.612843
31. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System J. Geipel et al. https://doi.org/10.3390/rs61110335
32. UAV-derived data for mapping change on a swidden agriculture plot: preliminary results from a pilot study A. Cummings et al. https://doi.org/10.1080/01431161.2017.1295487