Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China
Stomatal conductance (Gs) reflects the extent of water stress experienced by crops, which plays a crucial role in precision irrigation and water resource management. High spatiotemporal resolution multimodal remote sensing data from unmanned aerial vehicles (UAV) offers great potential for accuratel...
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Elsevier
2025-02-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005894 |
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| author | Quanshan Liu Zongjun Wu Ningbo Cui Shunsheng Zheng Shouzheng Jiang Zhihui Wang Daozhi Gong Yaosheng Wang Lu Zhao Renjuan Wei |
| author_facet | Quanshan Liu Zongjun Wu Ningbo Cui Shunsheng Zheng Shouzheng Jiang Zhihui Wang Daozhi Gong Yaosheng Wang Lu Zhao Renjuan Wei |
| author_sort | Quanshan Liu |
| collection | DOAJ |
| description | Stomatal conductance (Gs) reflects the extent of water stress experienced by crops, which plays a crucial role in precision irrigation and water resource management. High spatiotemporal resolution multimodal remote sensing data from unmanned aerial vehicles (UAV) offers great potential for accurately predicting crop stomatal conductance to monitor crop water stress. In this study, multispectral and thermal infrared remote sensing data of citrus canopies were acquired using UAV. Multimodal features, including RGB, spectral, and thermal information of the citrus canopy, were extracted. Simultaneously, Gs of citrus and soil moisture content (SMC) were collected. The Black-winged Kite Algorithm (BKA) was employed to optimize both the Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) models. Gs estimation models for citrus were constructed by incorporating RGB, multispectral (MS), and thermal infrared (TIR) data, as well as their combinations, using the BKA-KELM, BKA-ELM, KELM, and ELM algorithms. The results showed that Gs had the highest correlation with the average soil moisture content (SMCa) at a depth of 0–40 cm (R² = 0.674, P < 0.05). Additionally, Gs exhibited a strong correlation with 20 cm and 40 cm soil moisture content (SMC20 and SMC40), with R2 of 0.638 and 0.606, respectively (P < 0.05). The fusion of RGB, MS, and TIR multimodal information significantly improved the accuracy of Gs estimation. The Gs models constructed using RGB, MS and TIR as inputs demonstrated the best estimation performance, with R² ranging from 0.859 to 0.989, and RMSE from 1.623 mmol to 5.369 mmol H₂O m⁻²·s⁻². The BKA optimization algorithm effectively enhanced the predictive performance of the KELM and ELM models. The BKA-KELM7 model, using RGB+MS+TIR feature information as inputs, was identified as the optimal model for estimating citrus Gs, with R² ranging from 0.906 to 0.989, and RMSE from 1.623 mmol to 3.997 mmol H₂O m⁻²·s⁻². This study showed that combining multimodal information from low-cost UAV with the optimized machine learning algorithm can provide relatively accurate and robust estimates of citrus Gs. It offers an effective method for estimating Gs using only UAV data, providing valuable support for precision irrigation and field management decisions. |
| format | Article |
| id | doaj-art-828a2725c0284bcca50a192d02ad6676 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
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| series | Agricultural Water Management |
| spelling | doaj-art-828a2725c0284bcca50a192d02ad66762025-01-07T04:16:54ZengElsevierAgricultural Water Management1873-22832025-02-01307109253Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest ChinaQuanshan Liu0Zongjun Wu1Ningbo Cui2Shunsheng Zheng3Shouzheng Jiang4Zhihui Wang5Daozhi Gong6Yaosheng Wang7Lu Zhao8Renjuan Wei9State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; Correspondence to: College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China.State Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; Correspondence to: College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China.Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering & College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaSichuan Water Conservancy Vocational College, Chengdu, Sichuan, ChinaStomatal conductance (Gs) reflects the extent of water stress experienced by crops, which plays a crucial role in precision irrigation and water resource management. High spatiotemporal resolution multimodal remote sensing data from unmanned aerial vehicles (UAV) offers great potential for accurately predicting crop stomatal conductance to monitor crop water stress. In this study, multispectral and thermal infrared remote sensing data of citrus canopies were acquired using UAV. Multimodal features, including RGB, spectral, and thermal information of the citrus canopy, were extracted. Simultaneously, Gs of citrus and soil moisture content (SMC) were collected. The Black-winged Kite Algorithm (BKA) was employed to optimize both the Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) models. Gs estimation models for citrus were constructed by incorporating RGB, multispectral (MS), and thermal infrared (TIR) data, as well as their combinations, using the BKA-KELM, BKA-ELM, KELM, and ELM algorithms. The results showed that Gs had the highest correlation with the average soil moisture content (SMCa) at a depth of 0–40 cm (R² = 0.674, P < 0.05). Additionally, Gs exhibited a strong correlation with 20 cm and 40 cm soil moisture content (SMC20 and SMC40), with R2 of 0.638 and 0.606, respectively (P < 0.05). The fusion of RGB, MS, and TIR multimodal information significantly improved the accuracy of Gs estimation. The Gs models constructed using RGB, MS and TIR as inputs demonstrated the best estimation performance, with R² ranging from 0.859 to 0.989, and RMSE from 1.623 mmol to 5.369 mmol H₂O m⁻²·s⁻². The BKA optimization algorithm effectively enhanced the predictive performance of the KELM and ELM models. The BKA-KELM7 model, using RGB+MS+TIR feature information as inputs, was identified as the optimal model for estimating citrus Gs, with R² ranging from 0.906 to 0.989, and RMSE from 1.623 mmol to 3.997 mmol H₂O m⁻²·s⁻². This study showed that combining multimodal information from low-cost UAV with the optimized machine learning algorithm can provide relatively accurate and robust estimates of citrus Gs. It offers an effective method for estimating Gs using only UAV data, providing valuable support for precision irrigation and field management decisions.http://www.sciencedirect.com/science/article/pii/S0378377424005894Stomatal conductance (Gs)UAV multimodal informationSoil moisture content (SMC)Kernel extreme learning machine (KELM)Black-winged kite algorithm (BKA) |
| spellingShingle | Quanshan Liu Zongjun Wu Ningbo Cui Shunsheng Zheng Shouzheng Jiang Zhihui Wang Daozhi Gong Yaosheng Wang Lu Zhao Renjuan Wei Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China Agricultural Water Management Stomatal conductance (Gs) UAV multimodal information Soil moisture content (SMC) Kernel extreme learning machine (KELM) Black-winged kite algorithm (BKA) |
| title | Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China |
| title_full | Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China |
| title_fullStr | Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China |
| title_full_unstemmed | Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China |
| title_short | Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China |
| title_sort | estimating stomatal conductance of citrus orchard based on uav multi modal information in southwest china |
| topic | Stomatal conductance (Gs) UAV multimodal information Soil moisture content (SMC) Kernel extreme learning machine (KELM) Black-winged kite algorithm (BKA) |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005894 |
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