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Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices

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  • Shao, Guomin
  • Han, Wenting
  • Zhang, Huihui
  • Liu, Shouyang
  • Wang, Yi
  • Zhang, Liyuan
  • Cui, Xin

Abstract

Rapid and accurate acquisition of crop coefficient (Kc) values is essential for estimating field crop evapotranspiration (ET). The lack of rapid access to the high-resolution spatial and temporal distribution of Kc values hinders obtaining a crop Kc value for application in precision irrigation agriculture. This study aimed to explore the potential of leaf area index (LAI) and multispectral vegetation indices (VIs) obtained by an unmanned aerial vehicle (UAV) for estimating the Kc value for a maize crop on a field scale and to obtain a high-resolution spatial-temporal map of Kc values. Hence, the performance of the estimation model for daily maize Kc derived by two machine learning algorithms (random forest regression-RFR and multiple linear regression-MLR) based on the ground-based LAI and six types of UAV-based multispectral VIs (normalized difference vegetation index, NDVI; soil adjusted vegetation index, SAVI; enhanced vegetation index, EVI; transformed chlorophyll absorption in reflectance index, TCARI; green normalized vegetation index, GNDVI; and visual atmospheric resistance index, VARI), was evaluated under multiple irrigation conditions during the entire cropping cycle. Maize RFR with VIs-LAI-based ET was compared to soil water balance (SWB) and FAO-56-based ET. The results showed that the RFR algorithm effectively (R2 = 0.65) estimated maize Kc values based on ground-based LAI and UAV-based VIs. The UAV-based VIs based on Red-edge-Red and Green-Red spectral bands and ground-based LAI were suitable predictors in the Kc prediction model under different irrigation conditions. Further, we successfully obtained a high resolution (pixel size of centimeter) spatial distribution of maize Kc values based on EVI-based LAI and UAV-based VIs. Furthermore, the results indicated that the combination of UAV multispectral remote sensing technology and the RFR algorithm provides a potential solution for the distribution of water use and precision irrigation on a field scale.

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  • Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:agiwat:v:252:y:2021:i:c:s0378377421001712
    DOI: 10.1016/j.agwat.2021.106906
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    3. Rozenstein, Offer & Fine, Lior & Malachy, Nitzan & Richard, Antoine & Pradalier, Cedric & Tanny, Josef, 2023. "Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network," Agricultural Water Management, Elsevier, vol. 283(C).
    4. Maria Theresia Sri Budiastuti & Djoko Purnomo & Bambang Pujiasmanto & Desy Setyaningrum, 2023. "Response of Maize Yield and Nutrient Uptake to Indigenous Organic Fertilizer from Corn Cobs," Agriculture, MDPI, vol. 13(2), pages 1-11, January.
    5. Qian Cheng & Honggang Xu & Shuaipeng Fei & Zongpeng Li & Zhen Chen, 2022. "Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
    6. Zhang, Yu & Han, Wenting & Zhang, Huihui & Niu, Xiaotao & Shao, Guomin, 2023. "Evaluating maize evapotranspiration using high-resolution UAV-based imagery and FAO-56 dual crop coefficient approach," Agricultural Water Management, Elsevier, vol. 275(C).

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