Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data-Reference-Cited by-同舟云学术

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Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data

Published:2021-10 Issue: Volume:264 Page:112558
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Okujeni Akpona,Jänicke Clemens,Cooper Sam,Frantz David,Hostert Patrick,Clark Matthew,Segl Karl,van der Linden Sebastian

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference95 articles.

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2. Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring;Azzari;Remote Sens. Environ.,2017

3. Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data;Baumann;Remote Sens. Environ.,2018

4. Imaging spectrometry-derived estimates of regional ecosystem composition for the Sierra Nevada, California;Bogan;Remote Sens. Environ.,2019

5. The concept of essential climate variables in support of climate research, applications, and policy;Bojinski;Bull. Am. Meteorol. Soc.,2014

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