Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
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Title
Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
Authors
Keywords
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Journal
Remote Sensing
Volume 12, Issue 9, Pages 1357
Publisher
MDPI AG
Online
2020-04-28
DOI
10.3390/rs12091357
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- (2017) Carly Stanton et al. Journal of Applied Remote Sensing
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- Relationship of cotton nitrogen and yield with Normalized Difference Vegetation Index and plant height
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- (2014) Jakob Geipel et al. Remote Sensing
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- A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems
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- Radiometric Correction of Terrestrial LiDAR Point Cloud Data for Individual Maize Plant Detection
- (2013) Bernhard Hofle IEEE Geoscience and Remote Sensing Letters
- Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2
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- A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances
- (2013) Rafael Muñoz-Huerta et al. SENSORS
- Field high-throughput phenotyping: the new crop breeding frontier
- (2013) José Luis Araus et al. TRENDS IN PLANT SCIENCE
- The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning
- (2013) Joanne White et al. Forests
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- (2013) Graham Pope et al. Remote Sensing
- Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat
- (2012) Wei Wang et al. FIELD CROPS RESEARCH
- High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm
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- Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3
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- Forest biomass estimation from airborne LiDAR data using machine learning approaches
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- Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons
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- Extreme Learning Machine for Regression and Multiclass Classification
- (2011) Guang-Bin Huang et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
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- Evaluation of Sentinel-2 Red-Edge Bands for Empirical Estimation of Green LAI and Chlorophyll Content
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- Discrete return lidar-based prediction of leaf area index in two conifer forests
- (2008) J JENSEN et al. REMOTE SENSING OF ENVIRONMENT
- Development of a two-band enhanced vegetation index without a blue band
- (2008) Z JIANG et al. REMOTE SENSING OF ENVIRONMENT
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