Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images

Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel, Matthieu Puigt

Abstract

This dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait retrieval, and uncertainty quantification. Each hyperspectral cube contains 211 bands spanning 400--2500 nm at 10 nm resolution and a fixed spatial layout of 64 \times 64 pixels, offering continuous simulated surface reflectance spectra suitable for emulator development and machine-learning tasks requiring high spectral detail. Vegetation traits were derived by inverting Sentinel-2 Level-2A surface reflectance using a PROSAIL-based lookup-table approach, followed by forward PROSAIL simulations to generate hyperspectral reflectance under physically consistent canopy and illumination conditions. The dataset covers four ecologically diverse regions -- East Africa, Northern France, Eastern India, and Southern Spain -- and includes 5th and 95th percentile uncertainty maps as well as Sentinel-2 scene classification layers. This resource enables benchmarking of inversion methods, development of fast radiative transfer emulators, and studies of spectral--biophysical relationships under controlled yet realistic environmental variability.

SVH-BD : Synthetic Vegetation Hyperspectral Benchmark Dataset for Emulation of Remote Sensing Images

Abstract

This dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait retrieval, and uncertainty quantification. Each hyperspectral cube contains 211 bands spanning 400--2500 nm at 10 nm resolution and a fixed spatial layout of 64 \times 64 pixels, offering continuous simulated surface reflectance spectra suitable for emulator development and machine-learning tasks requiring high spectral detail. Vegetation traits were derived by inverting Sentinel-2 Level-2A surface reflectance using a PROSAIL-based lookup-table approach, followed by forward PROSAIL simulations to generate hyperspectral reflectance under physically consistent canopy and illumination conditions. The dataset covers four ecologically diverse regions -- East Africa, Northern France, Eastern India, and Southern Spain -- and includes 5th and 95th percentile uncertainty maps as well as Sentinel-2 scene classification layers. This resource enables benchmarking of inversion methods, development of fast radiative transfer emulators, and studies of spectral--biophysical relationships under controlled yet realistic environmental variability.

Paper Structure

This paper contains 3 sections, 2 equations, 4 figures, 2 tables.

Table of Contents

  1. Leaf-Level Simulation: PROSPECT-D
  2. Canopy-Level Simulation: SAIL
  3. RTM inversion

Figures (4)

  • Figure 1: Folder hierarchy of the dataset.
  • Figure 2: Geographic distribution of the four study regions used for synthetic hyperspectral dataset generation. The regions span diverse climate zones and vegetation types across Europe (France, Spain), Africa (Tanzania), and Asia (India).
  • Figure 3: Representative examples of emulated hyperspectral image cubes and their corresponding chlorophyll a+b (Cab) maps from the four study regions. For each row, the left column shows the Cab map and the right column shows the associated HSI scene. All scenes are displayed using an RGB composite.
  • Figure 4: Simulated hyperspectral reflectance spectra from the four study regions, color-coded by chlorophyll a+b content ($C_{ab}$).