Field-level crop yield estimation with PRISMA and Sentinel-2

M. Marshall*, M. Belgiu, Mirco Boschetti, Monica Pepe, A. Stein, A. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)
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Satellite image data deliver consistent and frequent information for crop yield estimation over large areas. Hyperspectral narrowbands are more sensitive spectrally to changes in crop growth than multispectral broadbands but few studies quantified the gains in the former over the later. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission offers narrow (≤10 nm) band capability across the full optical range. The multispectral broadband Sentinel-2 mission carries four experimental red-edge and near infrared (NIR) hyperspectral narrow (≤20 nm) bands. We compared the performance of PRISMA and Sentinel-2 spectral bands at important phases of crop development (vegetative, reproductive, maturity) in estimating field-level biomass and yield for corn, rice, soybean, and wheat. We selected three data-driven methods: two-band vegetation indices (TBVIs), partial least squares regression (PLSR), and random forest (RF). The PRISMA and Sentinel-2 models on average explained approximately 20% more variability in biomass and yield with RF than TBVIs and PLSR. The mean RMSE of the PRISMA RF models was 0.42 and 0.17 kg m−2, which was lower than the Sentinel-2 RF models (0.48 and 0.18 kg m−2). Multidate image (seasonal) model performance was generally higher than single-date image model performance. PRISMA shortwave infrared narrowbands and Sentinel-2 red-edge and near infrared bands were among the top-performing spectral regions. The results highlight potential complementarity between the PRISMA and Sentinel-2 missions for predicting crop biomass and yield. The results also show the benefits, limitations, and pitfalls of hyperspectral imaging in agricultural monitoring, which is important for upcoming operational hyperspectral missions, such as ESA CHIME and NASA Surface Biology and Geology (formerly HysPIRI).
Original languageEnglish
Pages (from-to)191-210
Number of pages20
JournalISPRS journal of photogrammetry and remote sensing
Early online date18 Mar 2022
Publication statusPublished - May 2022


  • Agriculture
  • Imaging spectroscopy
  • Hyperspectral
  • Remote sensing
  • Machine learning
  • Random forest
  • UT-Hybrid-D


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