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September 2, 2022International Journal of Remote SensingOpen Access

Integrating Sentinel-1 SAR and Sentinel-2 optical imagery with a crop structure dynamics model to track crop condition

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Authors

XJXianfeng JiaoHMHeather McNairnBYB. Yekkehkhany

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Overview

Satellite modeling study reveals reliable daily canola growth tracking through integrated radar and optical imagery, highlighting scalable tools for agricultural monitoring under climate variability.

Key Points

  • To develop a daily canola monitoring framework by calibrating Sentinel-1 synthetic aperture radar polarimetric features to Sentinel-2 optical vegetation indices and integrating them with a canopy dynamic model.
  • Extracted four polarimetric features from Sentinel-1 Single Look Complex SAR imagery: degree of linear polarization, normalized Shannon entropy, second eigenvalue of the coherency matrix, and ellipticity angle.
  • Trained a Random Forest Regressor to calibrate radar polarimetric parameters against Sentinel-2 Normalized Difference Vegetation Index values, producing a SARcal-NDVI index.
  • Incorporated SARcal-NDVI with a Canopy Structure Dynamics Model and Growing Degree Days to simulate daily crop condition and evaluated the output against ground-measured wet biomass.
  • Random Forest Regressor modeling demonstrated a strong agreement between radar-derived SARcal-NDVI and optical Sentinel-2 NDVI (R2 = 0.89).
  • Model-derived SARcal-NDVI time series achieved strong correlation with in situ wet biomass during early-to-mid-season rapid biomass accumulation (R2 = 0.88).
  • Correlation between SARcal-NDVI and ground-measured biomass remained statistically significant but weakened during pod development and senescence (R2 = 0.42).

Cite This Study

Jiao et al. (2022) studied this question.

synapsesocial.com/papers/6a782e762e130d02afed065dhttps://doi.org/10.1080/01431161.2022.2142077
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