Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops-Reference-Cited by-同舟云学术

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Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops

Published:2021-03 Issue: Volume:173 Page:278-296
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Danner Martin,Berger Katja,Wocher Matthias,Mauser Wolfram,Hank Tobias

Funder

Deutsches Zentrum für Luft- und Raumfahrt

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference104 articles.

1. Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring;Amin;Remote Sens. Environ.,2020

2. Kernel multivariate analysis framework for supervised subspace learning: A tutorial on linear and kernel multivariate methods;Arenas-Garcia;IEEE Signal Process Mag.,2013

3. Biophysical and Biochemical Sources of Variability in Canopy Reflectance;Asner;Remote Sens. Environ.,1998

4. Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data: Principles and validation;Bacour;Remote Sens. Environ.,2006

5. Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring;Belda;Agronomy,2020

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