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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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    1. Duchemin, B. & Hadria, R. & Erraki, S. & Boulet, G. & Maisongrande, P. & Chehbouni, A. & Escadafal, R. & Ezzahar, J. & Hoedjes, J.C.B. & Kharrou, M.H. & Khabba, S. & Mougenot, B. & Olioso, A. & Rodrig, 2006. "Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices," Agricultural Water Management, Elsevier, vol. 79(1), pages 1-27, January.
    2. Paredes, Paula & Pereira, Luis S. & Rodrigues, Gonçalo C. & Botelho, Nuno & Torres, Maria Odete, 2017. "Using the FAO dual crop coefficient approach to model water use and productivity of processing pea (Pisum sativum L.) as influenced by irrigation strategies," Agricultural Water Management, Elsevier, vol. 189(C), pages 5-18.
    3. Qiu, Rangjian & Du, Taisheng & Kang, Shaozhong & Chen, Renqiang & Wu, Laosheng, 2015. "Assessing the SIMDualKc model for estimating evapotranspiration of hot pepper grown in a solar greenhouse in Northwest China," Agricultural Systems, Elsevier, vol. 138(C), pages 1-9.
    4. Ding, Risheng & Kang, Shaozhong & Li, Fusheng & Zhang, Yanqun & Tong, Ling & Sun, Qingyu, 2010. "Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China," Agricultural Water Management, Elsevier, vol. 98(1), pages 87-95, December.
    5. Er-Raki, S. & Rodriguez, J.C. & Garatuza-Payan, J. & Watts, C.J. & Chehbouni, A., 2013. "Determination of crop evapotranspiration of table grapes in a semi-arid region of Northwest Mexico using multi-spectral vegetation index," Agricultural Water Management, Elsevier, vol. 122(C), pages 12-19.
    6. Ding, Risheng & Kang, Shaozhong & Zhang, Yanqun & Hao, Xinmei & Tong, Ling & Du, Taisheng, 2013. "Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching," Agricultural Water Management, Elsevier, vol. 127(C), pages 85-96.
    7. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    8. Rozenstein, Offer & Haymann, Nitai & Kaplan, Gregoriy & Tanny, Josef, 2019. "Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
    9. Han, Ming & Zhang, Huihui & DeJonge, Kendall C. & Comas, Louise H. & Trout, Thomas J., 2016. "Estimating maize water stress by standard deviation of canopy temperature in thermal imagery," Agricultural Water Management, Elsevier, vol. 177(C), pages 400-409.
    10. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
    11. Fan, Junliang & Zheng, Jing & Wu, Lifeng & Zhang, Fucang, 2021. "Estimation of daily maize transpiration using support vector machines, extreme gradient boosting, artificial and deep neural networks models," Agricultural Water Management, Elsevier, vol. 245(C).
    12. Agam, N. & Cohen, Y. & Berni, J.A.J. & Alchanatis, V. & Kool, D. & Dag, A. & Yermiyahu, U. & Ben-Gal, A., 2013. "An insight to the performance of crop water stress index for olive trees," Agricultural Water Management, Elsevier, vol. 118(C), pages 79-86.
    13. Narendra Gontia & Kamlesh Tiwari, 2010. "Estimation of Crop Coefficient and Evapotranspiration of Wheat (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(7), pages 1399-1414, May.
    14. Chen, Han & Huang, Jinhui Jeanne & McBean, Edward, 2020. "Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland," Agricultural Water Management, Elsevier, vol. 228(C).
    15. Pôças, I. & Calera, A. & Campos, I. & Cunha, M., 2020. "Remote sensing for estimating and mapping single and basal crop coefficientes: A review on spectral vegetation indices approaches," Agricultural Water Management, Elsevier, vol. 233(C).
    16. Zhang, Kefeng & Hilton, Howard W. & Greenwood, Duncan J. & Thompson, Andrew J., 2011. "A rigorous approach of determining FAO56 dual crop coefficient using soil sensor measurements and inverse modeling techniques," Agricultural Water Management, Elsevier, vol. 98(6), pages 1081-1090, April.
    17. DeJonge, K.C. & Ascough, J.C. & Andales, A.A. & Hansen, N.C. & Garcia, L.A. & Arabi, M., 2012. "Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation," Agricultural Water Management, Elsevier, vol. 115(C), pages 92-103.
    18. Phogat, V. & Šimůnek, J. & Skewes, M.A. & Cox, J.W. & McCarthy, M.G., 2016. "Improving the estimation of evaporation by the FAO-56 dual crop coefficient approach under subsurface drip irrigation," Agricultural Water Management, Elsevier, vol. 178(C), pages 189-200.
    19. Jiang, Xuelian & Kang, Shaozhong & Tong, Ling & Li, Fusheng & Li, Donghao & Ding, Risheng & Qiu, Rangjian, 2014. "Crop coefficient and evapotranspiration of grain maize modified by planting density in an arid region of northwest China," Agricultural Water Management, Elsevier, vol. 142(C), pages 135-143.
    20. Kullberg, Emily G. & DeJonge, Kendall C. & Chávez, José L., 2017. "Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients," Agricultural Water Management, Elsevier, vol. 179(C), pages 64-73.
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    6. 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.
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