R Tutorials
R Tutorials
Nineteen rendered R vignettes, grouped by theme: the models themselves, trait inversion (classical ML and deep learning), sensitivity analysis, and satellite/spatial applications. Every number and figure is real output from an actual run.
Getting Started with ToolsRTM
A first simulation, start to finish.
From Leaf to Canopy Reflectance
Chaining a leaf model into a canopy model.
SPART: Soil-Plant-Atmosphere RT
Top-of-canopy vs. top-of-atmosphere reflectance.
Comparing Radiative Transfer Models
All 15 leaf×canopy combinations, side by side.
Building Look-Up Tables
Parameter sampling for a simulation LUT.
Large-Scale and Parallel RTM Simulation
Running a LUT across many cores.
Sensor Convolution
Native spectrum → real sensor bands.
Hyperspectral and VNIR Sensor Convolution
PRISMA and other hyperspectral sensors.
Vegetation Indices and Spectral Features
NDVI, red-edge and friends, from simulated bands.
MARMIT + fourSAIL + SPART
Realistic, moisture-dependent soil reflectance inside a full canopy run.
From Physics to Vegetation Traits: Hybrid Inversion
LUT-trained models applied to real and synthetic spectra.
Comparing ML Algorithms for RTM Inversion
Random Forest, SVM, PLSR and ensembles, head-to-head.
Deep Learning for RTM Inversion
Dense networks and a 1D-CNN on hyperspectral bands, vs. Random Forest.
End-to-End RTM Inversion Pipeline
Simulate → convolve → invert, as one pipeline.
From Satellite Reflectance to Traits: Real EO Application
Inverting a real, not simulated, scene.
Monitoring a Forest Site Through Time
A real forest site, tracked across a time series.
Data-Driven Spatial Index Mapping
Trait maps from real Sentinel-2 imagery.
FLEX Cal/Val: ESU Heterogeneity Mapping
Scaling up to FLEX's coarser footprint with sub-pixel heterogeneity.