Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2

Paul Naethe, Andrea De Sanctis, Andreas Burkart, Petya K.E. Campbell, Roberto Colombo, Biagio Di Mauro, Alexander Damm, Tarek El-Madany, Francesco Fava, John A. Gamon, Karl F. Huemmrich, Mirco Migliavacca, Eugenie Paul-Limoges, Uwe Rascher, Micol Rossini, Dirk Schüttemeyer, Giulia Tagliabue, Yongguang Zhang, Tommaso Julitta

Research output: Contribution to journalArticlepeer-review


Sentinel-2 satellite data enables multispectral monitoring of the earth at a high temporal revisit rate. Combining this information with a network of optical ground measurements enables a more detailed and a more complete understanding of terrestrial ecosystems. However, independent optical ground measurements often lack consistency, especially when comparing different sites in geographically remote locations. Using the very high temporal and spectral resolution offered by the automated field spectrometer systems FloX and RoX (Fluorescence Box and Reflectance Box, respectively, JB-Hyperspectral Devices GmbH, Duesseldorf, Germany), we investigated continuous time series ranging over three years and in ten different locations across Europe, Africa, America and Asia. The continuous records of ground-measured reflectance were first validated against Sentinel-2 top of canopy (TOC) reflectance to evaluate the consistency of the in-situ network. Our results suggest a good agreement of ground-measured reflectance with Sentinel-2 TOC reflectance in vegetation and snow with R2 around 0.79 in the 833 nm band and R2 up to 0.94 in the bands around 559 nm and 492 nm, demonstrating good consistency across the network. Spatial misalignment of Sentinel-2 pixel-sizes with respect to the different footprint sizes of the ten automated spectrometers on the ground, atmospheric uncertainties, sub-optimal instrument setup and spatial-temporal variable landscape heterogeneity were identified as the most relevant sources of uncertainties in the network. Comparing the Normalized Difference Vegetation Index (NDVI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI) and Enhanced Vegetation Index (EVI) between ground and satellite revealed a decreasing agreement with increasing complexity of index formulation. The best agreement between satellite and ground was exhibited by NDVI with R2 around 0.96 and relative error of 4.3% investigating vegetation and snow across all ten sites. Furthermore, we identified a seasonal pattern in residuals of NDVI between ground and satellite in an alpine ecosystem in northern Italy, which was associated with increased spatial heterogeneity due to the effects of diverse vegetation phenology and snowfall. In contrast, a random distribution of residuals was recognized in a rather uniform oak forest canopy in southern France. Clustering Sentinel-2 pixels with respect to their temporal patterns in NDVI identified similar areas seen as homogenous in the canopy of Torgnon, Italy, and Observatoire de Haute-Provence (OHP), France, each. The very high temporal resolution of NDVI measured on the ground confirmed overlap with matched homogenous areas, but must consider seasonal landscape heterogeneity. Using well-standardized and globally homogenous Sentinel-2 TOC reflectance enabled the assessment of uncertainties in ten field spectrometer sites around the world. The standardization of the automated field spectrometers, their data products and data annotation were essential prerequisites that enabled joint validation against Sentinel-2. Harmonizing optical ground measurements with respect to a satellite is promising for future research to ensure the valid intercomparison and transfer of data products across different sites in a network worldwide.

Original languageEnglish (US)
Article number114013
JournalRemote Sensing of Environment
StatePublished - Mar 15 2024


  • Cloud filtering
  • Data fusion
  • Field spectrometer
  • FloX
  • RoX
  • Sentinel-2
  • Time series

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences


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