Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields
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 as...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005274 |
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| author | Larona Keabetswe Yiyin He Chao Li Zhenjiang Zhou |
| author_facet | Larona Keabetswe Yiyin He Chao Li Zhenjiang Zhou |
| author_sort | Larona Keabetswe |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a800d14df6da436e8ba91c2923c987c6 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-a800d14df6da436e8ba91c2923c987c62024-12-14T06:29:47ZengElsevierAgricultural Water Management1873-22832024-12-01306109191Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fieldsLarona Keabetswe0Yiyin He1Chao Li2Zhenjiang Zhou3College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR ChinaCorresponding author.; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR ChinaEstimating 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.http://www.sciencedirect.com/science/article/pii/S0378377424005274Remote sensingCrop water useRandom ForestsLimited inputsAmeriFlux |
| spellingShingle | Larona Keabetswe Yiyin He Chao Li Zhenjiang Zhou Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields Agricultural Water Management Remote sensing Crop water use Random Forests Limited inputs AmeriFlux |
| title | Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields |
| title_full | Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields |
| title_fullStr | Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields |
| title_full_unstemmed | Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields |
| title_short | Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields |
| title_sort | estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning based models in potato fields |
| topic | Remote sensing Crop water use Random Forests Limited inputs AmeriFlux |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005274 |
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