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Purpose <p>Sentinel-2 enables precise wheat monitoring, but cloud cover can disrupt observations. PlanetScope’s near-daily, 3-m resolution can compensate, but cross-calibration of its different sensors poses challenges. Synergy between Sentinel-2 and PlanetScope can leverage their complementary strengths. Prior to this, consistency analysis is crucial. This study aims to evaluate their consistency and determine optimal time intervals and product levels.</p> Methods <p>The wheat Leaf Area Index (LAI) was measured in France and China (2019–2022), alongside collecting Sentinel-2 and PlanetScope (SuperDove, Dove-R) imagery. Surface reflectance, vegetation indices (VIs), and LAI were analyzed for consistency, with satellite LAI retrieved using a PROSAIL-based neural network.</p> Results <p>Surface reflectance exhibited strong overall consistency (R<sup>2</sup> = 0.89 for Sentinel-2 and SuperDove, and R<sup>2</sup> = 0.98 for Sentinel-2 and Dove-R), enabling overall band conversion regression equations. VIs showed high consistency within a 2-day interval: Dove-R and Sentinel-2 achieved R<sup>2</sup> = 0.96 and RMSE = 0.06 for Normalized Difference Vegetation Index (NDVI), SuperDove and Sentinel-2 exhibited the strongest agreement in Normalized Difference Red-Edge Index (NDRE) (R<sup>2</sup> = 0.79, RMSE = 0.09), highlighting the potential of red-edge-band-based VIs in satellite synergy. LAI consistency demonstrated robust performance across 0–5&#xa0;day intervals (R<sup>2</sup> = 0.89–0.97 for Dove-R and Sentinel-2, and R<sup>2</sup> = 0.89–0.97 for SuperDove and Sentinel-2), with an optimal interval of 2 days.</p> Conclusion <p>The synergy of Sentinel-2 and PlanetScope for continuous crop monitoring is feasible, with LAI being the most robust choice. Future studies should include more high-resolution satellites and other products (e.g., top-of-atmosphere reflectance) to enhance crop monitoring.</p>

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Evaluating the consistency between Sentinel-2 and Planet constellations at field scale: illustration over winter wheat

  • 期刊论文

  • 发表日期：

    2025年2月12日

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  • Yuman Ma,
  • Wenjuan Li,
  • Jingwen Wang,
  • Shouyang Liu,
  • Mingxia Dong,
  • Zhongchao Shi

摘要

Purpose

Sentinel-2 enables precise wheat monitoring, but cloud cover can disrupt observations. PlanetScope’s near-daily, 3-m resolution can compensate, but cross-calibration of its different sensors poses challenges. Synergy between Sentinel-2 and PlanetScope can leverage their complementary strengths. Prior to this, consistency analysis is crucial. This study aims to evaluate their consistency and determine optimal time intervals and product levels.

Methods

The wheat Leaf Area Index (LAI) was measured in France and China (2019–2022), alongside collecting Sentinel-2 and PlanetScope (SuperDove, Dove-R) imagery. Surface reflectance, vegetation indices (VIs), and LAI were analyzed for consistency, with satellite LAI retrieved using a PROSAIL-based neural network.

Results

Surface reflectance exhibited strong overall consistency (R2 = 0.89 for Sentinel-2 and SuperDove, and R2 = 0.98 for Sentinel-2 and Dove-R), enabling overall band conversion regression equations. VIs showed high consistency within a 2-day interval: Dove-R and Sentinel-2 achieved R2 = 0.96 and RMSE = 0.06 for Normalized Difference Vegetation Index (NDVI), SuperDove and Sentinel-2 exhibited the strongest agreement in Normalized Difference Red-Edge Index (NDRE) (R2 = 0.79, RMSE = 0.09), highlighting the potential of red-edge-band-based VIs in satellite synergy. LAI consistency demonstrated robust performance across 0–5 day intervals (R2 = 0.89–0.97 for Dove-R and Sentinel-2, and R2 = 0.89–0.97 for SuperDove and Sentinel-2), with an optimal interval of 2 days.

Conclusion

The synergy of Sentinel-2 and PlanetScope for continuous crop monitoring is feasible, with LAI being the most robust choice. Future studies should include more high-resolution satellites and other products (e.g., top-of-atmosphere reflectance) to enhance crop monitoring.