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Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery

Author

Listed:
  • Yonela Mndela

    (Department of GIS and Remote Sensing, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa)

  • Naledzani Ndou

    (Department of GIS and Remote Sensing, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa)

  • Adolph Nyamugama

    (Agriculture Research Council, Institute for Soil, Climate and Water (ARC-ISCW), Pretoria 0001, South Africa)

Abstract

A timely irrigation schedule for small-scale farms is imperative for ensuring optimum crop production in the wake of drought and climate change. Owing to the large number of irrigated small-scale farms that grow different crops across all seasons in the Mutale River catchment, this study sought to develop irrigation scheduling for these crops for sustainable water utilization without compromising crop yields. Unmanned aerial vehicle (UAV) images were utilized as the base from which crop water content patterns were derived. A total of four (4) spectral vegetation indices, viz, the Greenness Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Optimized Soil-Adjusted Vegetation Index (OSAVI), were generated to characterize crop types and water content in this study. Crop water content data, in the form of the relative water content (RWC), were measured in the field for each type of crop. Crop water content was modelled based on the empirical relationships between spectral indices and field-measured RWC. The linear regression analysis revealed a significant association between the GNDVI and the water content of sweet potato, maize, sugar beans, and Florida broadleaf mustard, with r 2 values of 0.948, 0.995, 0.978, and 0.953, respectively. The NDVI revealed a strong association with the water content of Solanum retroflexum , pepper, and cabbage, with r 2 values of 0.949, 0.956, and 0.995, respectively. The NDRE, on the other hand, revealed a strong relationship with water content in peas and green beans, with r 2 values of 0.961 and 0.974, respectively. The crop water content patterns simulation revealed that Solanum retroflexum , sweet potato, maize, sugar beans, and Florida broadleaf mustard reached their respective wilting points on day four after irrigation, implying that irrigation of these crops should be scheduled after every four (4) days. Peas, green beans, pepper, and cabbage reached their respective wilting points on day five after irrigation, implying that irrigation of these crops should be scheduled after every five days. The results of this study highlight the significance of considering crop water content derived from spectral bands of UAV imagery in scheduling irrigation for various types of crops. This study also emphasized the on-going significance of remote sensing technology in addressing agricultural issues that impede hunger alleviation and food security goals.

Suggested Citation

  • Yonela Mndela & Naledzani Ndou & Adolph Nyamugama, 2023. "Irrigation Scheduling for Small-Scale Crops Based on Crop Water Content Patterns Derived from UAV Multispectral Imagery," Sustainability, MDPI, vol. 15(15), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:12034-:d:1211518
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