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GIScience & Remote Sensing Volume 58, 2021 - Issue 7
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Research Article

Aboveground biomass estimates over Brazilian savannas using hyperspectral metrics and machine learning models: experiences with Hyperion/EO-1

Aline Daniele Jacona Divisão de Observação da Terra e Geoinformática (DIOTG), Instituto Nacional de Pesquisas Espaciais (Inpe), São José dos Campos, SP, BrazilORCID Iconhttps://orcid.org/0000-0003-2585-5198View further author information
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Lênio Soares Galvãoa Divisão de Observação da Terra e Geoinformática (DIOTG), Instituto Nacional de Pesquisas Espaciais (Inpe), São José dos Campos, SP, BrazilCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-8313-0497View further author information
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Ricardo Dalagnola Divisão de Observação da Terra e Geoinformática (DIOTG), Instituto Nacional de Pesquisas Espaciais (Inpe), São José dos Campos, SP, Brazil;b Department of Geography, School of Environment Education and Development, University of Manchester, Manchester, UKORCID Iconhttps://orcid.org/0000-0002-7151-8697View further author information
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João Roberto dos Santosa Divisão de Observação da Terra e Geoinformática (DIOTG), Instituto Nacional de Pesquisas Espaciais (Inpe), São José dos Campos, SP, BrazilORCID Iconhttps://orcid.org/0000-0002-1139-9577View further author information
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Pages 1112-1129 | Received 14 Apr 2021, Accepted 10 Aug 2021, Published online: 26 Aug 2021

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Juliana de Abreu Araújo, Lênio Soares Galvão & Ricardo Dalagnol. (2023) Sensitivity of hyperspectral vegetation indices to rainfall seasonality in the Brazilian savannahs: an analysis using PRISMA data. Remote Sensing Letters 14:3, pages 277-287.
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Articles from other publishers (7)

Sobia Bhatti, Sajid Rashid Ahmad, Muhammad Asif & Iftikhar ul Hassan Farooqi. (2023) Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan. Forestry: An International Journal of Forest Research 96:1, pages 104-120.
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Jaese Lee, Sumin Park, Jungho Im, Cheolhee Yoo & Eunkyo Seo. (2022) Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning. Journal of Hydrology 609, pages 127749.
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Isadora Haddad, Lênio Soares Galvão, Fábio Marcelo Breunig, Ricardo Dalagnol, Vandoir Bourscheidt & Aline Daniele Jacon. (2022) On the combined use of phenological metrics derived from different PlanetScope vegetation indices for classifying savannas in Brazil. Remote Sensing Applications: Society and Environment 26, pages 100764.
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Yinghui Zhao, Ye Ma, Lindi J. Quackenbush & Zhen Zhen. (2022) Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery. Remote Sensing 14:2, pages 271.
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Abdulhakim M. Abdi, Martin Brandt, Christin Abel & Rasmus Fensholt. (2022) Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities. Journal of Remote Sensing 2022.
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Alana Almeida de Souza, Lênio Soares Galvão, Thales Sehn Korting & Cláudio Aparecido Almeida. (2021) On a Data-Driven Approach for Detecting Disturbance in the Brazilian Savannas Using Time Series of Vegetation Indices. Remote Sensing 13:24, pages 4959.
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Itiya Aneece & Prasad S. Thenkabail. (2021) Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud. Remote Sensing 13:22, pages 4704.
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