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A two-step approach for detecting Striga in a complex agroecological system using Sentinel-2 data.

Bester Tawona Mudereri1, Elfatih Mohamed Abdel-Rahman2, Timothy Dube3

  • 1Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; International Centre of Insect Physiology and Ecology (icipe), P. O. Box 30772, 00100, Nairobi, Kenya.

The Science of the Total Environment
|November 4, 2020
PubMed
Summary

This study maps parasitic Striga (Striga hermonthica) weeds in Kenyan croplands using Sentinel-2 satellite data and spectral mixture analysis. The method accurately detects weed abundance at a subpixel level, aiding in crop management and preventing further infestation.

Keywords:
Africa, croplandsEndmember selectionGoogle Earth EngineInvasive weedsSpectral mixture modeling

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Area of Science:

  • Agricultural remote sensing
  • Plant pathology
  • Ecology

Background:

  • Accurate weed mapping is crucial for crop management and productivity assessments, yet data on weed occurrence, especially for parasitic weeds like Striga, is often scarce.
  • Previous studies have limitedly explored wide-area mapping of weed abundances in agroecological systems using spaceborne imagery.
  • The parasitic weed genus Striga poses a significant threat to crop yields in many regions, necessitating improved detection methods.

Purpose of the Study:

  • To enhance the detection capacity of Striga (Striga hermonthica) at a subpixel level using high-resolution spaceborne imagery.
  • To develop and validate a remote sensing approach for mapping Striga occurrence and infestation within croplands.
  • To provide actionable data for Striga management and mitigation strategies.

Main Methods:

  • A two-step classification approach was employed using Sentinel-2 (S2) satellite data from 2017-2018.
  • Cropland and non-cropland areas were mapped using the random forest algorithm in Google Earth Engine.
  • Subpixel multiple endmember spectral mixture analysis (MESMA) was applied to S2 data, utilizing in-situ hyperspectral data resampled to S2 bands (Striga, crop/other weeds, soil endmembers).

Main Results:

  • Overall classification accuracies of 88% for cropland mapping and 78% for subpixel Striga detection were achieved.
  • The MESMA subpixel algorithm demonstrated plausible results for predicting relative Striga abundance within S2 pixels, with an F-score of 0.84 and RMSE of 0.0075.
  • The hierarchical approach combining cropland classification and MESMA proved effective for Striga detection in heterogeneous agroecological systems.

Conclusions:

  • The developed remote sensing methodology, integrating Sentinel-2 data and MESMA, is effective for detecting and mapping Striga at a subpixel level.
  • This approach provides valuable information for guiding adaptation, mitigation, and remediation efforts against Striga infestation.
  • The study highlights the potential of spaceborne imagery for large-scale monitoring of parasitic weeds in agricultural landscapes.