Large-scale mapping of environmental variables by combining ground observations, remote sensing, and machine learning
Co-organized by ESSI4
Convener:
Hanna Meyer
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Co-conveners:
Benjamin Dechant,
Alvaro Moreno,
Jacob Nelson,
Madlene Nussbaum
Orals
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Thu, 18 Apr, 08:30–12:30 (CEST) Room 2.23
Posters on site
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Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00 Hall X1
Posters virtual
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Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00 vHall X1
In the upscaling, machine learning algorithms that can account for complex and nonlinear relationships are increasingly used to link remote sensing datasets to reference measurements. The resulting models are then applied to provide spatially explicit predictions of the target variable, often even on a global scale.
Due to easy access to user-friendly software, model training and spatial prediction using machine learning algorithms is nowadays straightforward at first sight. However, considerable challenges remain: dealing with reference data that are not independent and identically distributed, accounting for spatial heterogeneity when scaling reference measurements to the grid cell scale, appropriately evaluating the resulting maps and quantifying their uncertainties, generating robust maps that do not suffer from extrapolation artifacts as well as the strategies for model interpretation and understanding. This session invites contributions on the methodology and application of large-scale mapping strategies in different disciplines, including vegetation characteristics such as foliar or canopy traits and photosynthesis, soil characteristics such as soil organic carbon, or atmospheric parameters such as pollutant concentration. Methodological contributions can focus on individual aspects of the upscaling approach, such as the design of measurement campaigns or networks to increase representativeness, novel algorithms or validation strategies as well as uncertainty assessment.
08:30–08:35
From field data to vegetation and soil maps
08:45–08:55
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EGU24-8898
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ECS
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On-site presentation
08:55–09:05
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EGU24-10647
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ECS
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Virtual presentation
09:05–09:15
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EGU24-20365
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Virtual presentation
09:15–09:25
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EGU24-312
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ECS
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On-site presentation
Mapping the multidecadal trends of terrestrial plant nitrogen stable isotope ratios globally
(withdrawn)
09:25–09:35
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EGU24-17559
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ECS
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On-site presentation
09:35–09:45
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EGU24-4977
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On-site presentation
Development of the fine resolution fractional vegetation cover product and its application in urban area
(withdrawn)
09:45–09:55
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EGU24-15542
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ECS
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On-site presentation
09:55–10:05
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EGU24-17268
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On-site presentation
10:05–10:15
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EGU24-17704
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On-site presentation
Coffee break
Chairpersons: Benjamin Dechant, Alvaro Moreno, Madlene Nussbaum
10:45–10:50
Machine learning for large-scale mapping of carbon and water fluxes
10:50–11:00
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EGU24-20044
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ECS
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Virtual presentation
Soil property, carbon stock and peat extent mapping at 10m resolution in Scotland using digital soil mapping techniques
(withdrawn)
11:00–11:10
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EGU24-10504
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ECS
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On-site presentation
11:10–11:20
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EGU24-19789
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ECS
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On-site presentation
11:20–11:30
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EGU24-19664
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ECS
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Virtual presentation
11:30–11:40
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EGU24-5488
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ECS
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On-site presentation
11:50–12:00
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EGU24-4823
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ECS
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On-site presentation
12:00–12:10
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EGU24-6067
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ECS
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On-site presentation
12:10–12:20
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EGU24-19181
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ECS
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On-site presentation
12:20–12:30
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EGU24-13485
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ECS
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On-site presentation
X1.103
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EGU24-4638
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ECS
Diagnosis of Nitrogen Nutrition in Summer Maize based on Simulated Multispectral Data
(withdrawn after no-show)
X1.107
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EGU24-10962
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ECS
vX1.11
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EGU24-9374
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ECS
Upscaling terrestrial Evapotranspiration: A framework based on a spatial heterogeneitymodel and machine learning algorithms
(withdrawn after no-show)
vX1.12
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EGU24-16460
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ECS