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Ichsani Wheeler

Showing results (1-10 of 7) with videos related to

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Peerj|August 30, 2018
Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potentialTomislav Hengl, Markus G Walsh, Jonathan Sanderman, et al.
Peerj|March 18, 2024
Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolutionJulia Hackländer, Leandro Parente, Yu-Feng Ho, et al.
Nutrient Cycling in Agroecosystems|January 18, 2021
Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learningTomislav Hengl, Johan G B Leenaars, Keith D Shepherd, et al.
Scientific Data|December 11, 2024
Annual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine LearningLeandro Parente, Lindsey Sloat, Vinicius Mesquita, et al.
Plos One|February 17, 2017
SoilGrids250m: Global gridded soil information based on machine learningTomislav Hengl, Jorge Mendes de Jesus, Gerard B M Heuvelink, et al.
Scientific Reports|March 18, 2021
African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learningTomislav Hengl, Matthew A E Miller, Josip Križan, et al.
Data in Brief|August 28, 2023
Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolutionPanos Panagos, Tomislav Hengl, Ichsani Wheeler, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
Peerj|August 30, 2018
Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potentialTomislav Hengl, Markus G Walsh, Jonathan Sanderman, et al.
Peerj|March 18, 2024
Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolutionJulia Hackländer, Leandro Parente, Yu-Feng Ho, et al.
Nutrient Cycling in Agroecosystems|January 18, 2021
Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learningTomislav Hengl, Johan G B Leenaars, Keith D Shepherd, et al.
Scientific Data|December 11, 2024
Annual 30-m maps of global grassland class and extent (2000-2022) based on spatiotemporal Machine LearningLeandro Parente, Lindsey Sloat, Vinicius Mesquita, et al.
Plos One|February 17, 2017
SoilGrids250m: Global gridded soil information based on machine learningTomislav Hengl, Jorge Mendes de Jesus, Gerard B M Heuvelink, et al.
Scientific Reports|March 18, 2021
African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learningTomislav Hengl, Matthew A E Miller, Josip Križan, et al.
Data in Brief|August 28, 2023
Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolutionPanos Panagos, Tomislav Hengl, Ichsani Wheeler, et al.
Pageof 1