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@ARTICLE{Adriko:1047371,
      author       = {Adriko, Kennedy and Sedona, Rocco and Seguini, Lorenzo and
                      Riedel, Morris and Cavallaro, Gabriele and Paris, Claudia},
      title        = {{F}rom {MODIS} to {S}entinel-2: {A} {R}egional
                      {C}omparative {A}nalysis of {C}rop-{Y}ield {P}rediction with
                      {M}atched {S}patiotemporal {D}ata},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {18},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-04261},
      pages        = {27663 - 27683},
      year         = {2025},
      abstract     = {Large-scale crop yield mapping has long relied on the
                      Moderate Resolution Imaging Spectrometer (MODIS) due to its
                      high temporal resolution and consistent atmospheric
                      correction. The Sentinel-2 constellation, with its finer
                      spatial resolution and vegetation-sensitive spectral bands,
                      now offers new opportunities for regional- and field-scale
                      yield prediction—especially as MODIS nears the end of its
                      operational life. However, it remains unclear whether
                      Sentinel-2 can ensure continuity of MODIS-based estimates
                      across diverse agricultural regions. We present a regional
                      sensor-to-sensor comparison of MODIS and Sentinel-2 for crop
                      yield prediction using matched spatiotemporal inputs across
                      two agro-ecological zones. Using reproducible regression
                      workflows, we demonstrate that Sentinel-2 captures finer
                      spatial variation in crop phenology and consistently
                      outperforms MODIS in terms of predictive accuracy. For
                      cotton, Sentinel-2 achieved an RMSE of 123.52 lb/acre and R2
                      of 0.76, versus MODIS with 129.20 lb/acre and R2 of 0.74.
                      For corn, Sentinel-2 achieved 8.40 Bu/acre and 0.79,
                      outperforming MODIS at 8.69 and 0.66, respectively. SHAP
                      analysis identifies Enhanced Vegetation Index (EVI),
                      Fraction of Photosynthetically Active Radiation (FPAR), and
                      Leaf Area Index (LAI) as key predictors across both sensors.
                      Despite its lower temporal frequency, Sentinel-2 delivers
                      robust, regionally consistent estimates, supporting its
                      suitability as a successor to MODIS for operational crop
                      monitoring.},
      cin          = {JSC},
      ddc          = {520},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Embed2Scale - Earth
                      Observation $\&$ Weather Data Federation with AI Embeddings
                      (101131841)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)101131841},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1109/JSTARS.2025.3624046},
      url          = {https://juser.fz-juelich.de/record/1047371},
}