EconPapers    
Economics at your fingertips  
 

Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields

Larona Keabetswe, Yiyin He, Chao Li and Zhenjiang Zhou

Agricultural Water Management, 2024, vol. 306, issue C

Abstract: Estimating actual crop evapotranspiration (ETc act) is a fundamental requirement for effective crop water management, aiming to achieve precision irrigation amidst changing environmental conditions. Despite the introduction of various methods for estimating ETc act values, these methods are still associated with several challenges and limitations. Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ETc act using a limited set of input features. Three models were configured and compared for each CNN-RF (CNN-RF1, CNNRF2, CNNRF3) and CNN-SVM (CNN-SVM1, CNN-SVM2, CNN-SVM3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). The results demonstrated the outstanding performance of the CNN-RF models, showcasing their superiority over the CNN-SVM models. Notably CNN-RF1 model yielded the best results, achieving a normalized root mean squared error (nRMSE) of 11.7 and 31.5 %; Nash–Sutcliffe Efficiency (NSE) of 0.97 and 0.80; Correlation coefficient (CC) of 0.99 and 0.91 at training and testing phases respectively. While, both models exhibited lower performance when using remote sensing satellite-based inputs, CNN-RF2, managed to produce satisfactory estimates of nRMSE = 16.7, 40.9 %; NSE = 0.95, 0.66; CC = 0.98, 0.813; Bias = −0.41, −4.377 W/m2 during training and testing respectively. The ETc act of potato showed to have high correlation with LST (0.74–0.84) and using LST as proxy for Ta, resulted in improved model estimates compared to using satellite data exclusively. These findings suggest that, in situations where climatic data is limited, remote sensing can serve as a viable alternative for modelling potato ETc act when coupled with CNN-RF, and further improvement can be achieved by incorporating meteorological data fusion.

Keywords: Remote sensing; Crop water use; Random Forests; Limited inputs; AmeriFlux (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377424005274
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:306:y:2024:i:c:s0378377424005274

DOI: 10.1016/j.agwat.2024.109191

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-05-25
Handle: RePEc:eee:agiwat:v:306:y:2024:i:c:s0378377424005274