Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data-Reference-Cited by-同舟云学术

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

Published:2021-01-01 Issue:1 Volume:54 Page:209-225
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Chiarito Eugenia¹²^ORCID,Cigna Francesca³,Cuozzo Giovanni¹,Fontanelli Giacomo²,Mejia Aguilar Abraham¹,Paloscia Simonetta²,Rossi Mattia¹²,Santi Emanuele²,Tapete Deodato³,Notarnicola Claudia¹

Affiliation:

1. Institute for Earth Observation, EURAC Research, Bolzano, Italy

2. Earth Observation, Institute of Applied Physics - National Research Council of Italy (IFAC–CNR), Sesto Fiorentino, Italy

3. Italian Space Agency (ASI), Rome, Italy

Funder

Italian Space Agency

“Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone”

European Regional Development Fund (ERDF), Operational Program Investment for growth and jobs

Data Platform and Sensing Technology for Environmental Sensing

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2021.1901063

Reference41 articles.

1. Estimating standing biomass in papyrus (Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression

2. Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

3. The COSMO-SkyMed Dual Use Earth Observation Program: Development, Qualification, and Results of the Commissioning of the Overall Constellation

4. Mediterranean shrublands biomass estimation using Sentinel-1 and Sentinel-2

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning models for dry matter and biomass estimates on cattle grazing systems;Computers and Electronics in Agriculture;2024-01

2. Linking Grassland Yield and Nitrogen Nutrition Status to Management Practices: Towards Large-Scale Grassland Use Intensity Assessment;2024

3. Application of hyperspectral and radar data for a land cover classification;Mongolian Journal of Geography and Geoecology;2023-12-28

4. Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass;Ecological Informatics;2023-12

5. Prediction of pasture yield using machine learning-based optical sensing: a systematic review;Precision Agriculture;2023-09-28