Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

Wei, Guangfei; Cao, Jiyue; Xie, Hui; Xie, Hui; Yang, Yang; Wu, Conglin; Cui, Yuanlai; Luo, Yufeng

doi:10.3390/rs14051201

Cited by 9 publications

(9 citation statements)

References 48 publications

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“…In contrast, Kaysert et al [61] reported that daily estimates of geeSEBAL yielded an average RMSD of 0.91 mm/day when compared to eddy covariance system data, while Gonçalves et al [62] indicated that geeSEBAL has significant potential for use in the assessment of crop evapotranspiration for irrigation monitoring and management in Brazil, even in areas with missing climate data. The applicability of the SEBAL model for paddy field evapotranspiration estimation was evaluated for the 2000-2017 period in Jiangxi Province (south of the Yangtze River), China, and the results show that the SEBAL model estimated crop evapotranspiration accurately on a daily scale, with R 2 and RMSE values of 0.85 and 0.84 mm/day, respectively [63]. This study showed the applicability of the SEBAL model in paddy fields in subtropical regions and provided a basis and reference for the rational allocation of water resources at a regional scale [63].…”

Section: Maize Daily Actual Evapotranspirationmentioning

confidence: 99%

“…The applicability of the SEBAL model for paddy field evapotranspiration estimation was evaluated for the 2000-2017 period in Jiangxi Province (south of the Yangtze River), China, and the results show that the SEBAL model estimated crop evapotranspiration accurately on a daily scale, with R 2 and RMSE values of 0.85 and 0.84 mm/day, respectively [63]. This study showed the applicability of the SEBAL model in paddy fields in subtropical regions and provided a basis and reference for the rational allocation of water resources at a regional scale [63].…”

Section: Maize Daily Actual Evapotranspirationmentioning

confidence: 99%

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Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

2023

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Actual crop evapotranspiration (ETa) is measured or estimated using different methods, and its accuracy is critical for water management under precision agriculture. The objective of this study was to compare maize ETa estimated by the two-step approach using a locally developed crop coefficient curve with satellite-retrieved evapotranspiration by six models incorporated in the OpenET to identify the best evapotranspiration estimation alternatives to the two-step approach for water management in northern New Mexico. Maize (Zea mays L.) was planted at the NMSU Agricultural Science Center at Farmington from 2017 to 2022 and uniformly managed across years. Water management in plants was based on maize’s actual evapotranspiration estimated as the product of the reference evapotranspiration and the local crop coefficient, which is described as a third-order polynomial function of the accumulated heat units by maize plants. For the same growing seasons, maize ETa was retrieved from satellite, and was estimated by six models listed within the OpenET from 2017 to 2022. The results show that maize daily ETa was consistently smaller when measured by SIMS and PT-JPL during maize initial and actively growing stages, while ETc(kc), SIMS and eeMETRIC showed similar maize daily ETa during maize full canopy development and mid-season, and which overcome the evapotranspiration estimated by DisALEXI, PT-JPL, geeSEBAL, and SSBop. ETc(kc) drastically dropped and became the lowest value among all ETa estimation models after the first fall snow or the first killing frost. Regarding the seasonal average, all six models included in OpenET showed smaller maize evapotranspiration. Maize seasonal evapotranspiration varied from 589.7 to 683.2 mm. eeMETRIC compares most similarly to the ETc(kc) model, followed by SIMS, with percent errors of 2.58 and 7.74% on a daily basis and 2.43 and 7.88% on a seasonal basis, with the lowest MBE and RMSE values, respectively, and could be used as an alternative for maize actual daily evapotranspiration for water management in northern New Mexico. The results of this study could be used by water managers and crop growers to improve water management in the Four Corners region, using eeMETRIC for crop water use to improve water management and conservation under sustainable agriculture.

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Section: Maize Daily Actual Evapotranspirationmentioning

confidence: 99%

Section: Maize Daily Actual Evapotranspirationmentioning

confidence: 99%

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

2023

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“…ET daily (10) where D 1 is the sowing period and D 5 is the maturity period. ET daily is the daily ET during the growing season.…”

Section: Seasonal Et (Et Season )mentioning

confidence: 99%

“…Bastiaanssen and Steduto [1] assessed the global water productivity of wheat, rice, and maize by using the SEBAL model for ET and the dry matter mass-harvest index method. In terms of crop ET estimation, the SEBAL model as a typical single-source remote sensing model simplifies input parameters compared with dual-source models, and is more flexible for processing remote sensing images [10]. In terms of yield estimation, the dry biomass-harvest index method is a widely used crop yield estimation model by remote sensing [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Phenology-Based Remote Sensing Assessment of Crop Water Productivity

Hang

Zhang

Wang

et al. 2023

Water

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The assessment of crop water productivity (CWP) is of practical significance for improving regional agricultural water use efficiency and water conservation levels. The remote sensing method is a common method for estimating large scale CWP, and the assessment errors in CWP by remote sensing originate mainly from remote sensing inversion errors in crop yield and evapotranspiration (ET). The phenological period is the important factor in crop ET and yield estimation. The crop coefficient (Kc) and harvest index (HI), which are closely related to different phenological periods, are considered during the processes of crop ET and yield estimation. The crop phenological period is detected from enhanced vegetation index (EVI) curves using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Sentinel-2 data. The crop ET is estimated using the surface–energy balance algorithm for land (SEBAL) model and Penman‒Monteith (P-M) equation, and the crop yield is estimated using the dry matter mass–harvest index method. The CWP is calculated as the ratio of the crop yield to ET during the growing season. The results show that the daily ET and crop yield estimated from remote sensing images are consistent with the measured values. It is found from the variation in daily ET that the peaks appear at the heading period of wheat and maize, which are in good agreement with the rainfall and growth characteristics of the crop. The relationship between crop yield and ET shows a negative parabolic correlation, and that between CWP and crop yield shows a linear correlation. The average CWPs of wheat and maize are 1.60 kg/m3 and 1.39 kg/m3, respectively. The results indicate that the phenology-based remote sensing inversion method has a good effect on the assessment of CWP in Lixin County.

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“…Based on the platform sensing distance, remote sensing can be classified as follows: satellite remote sensing platforms, unmanned aerial vehicle (UAV) remote sensing platforms, and near-grounded remote sensing platforms. Satellite remote sensing platforms have been applied extensively to monitor crop moisture information [6][7][8], biomass [9], cover [10], evapotranspiration [11], and crop classification [12,13] in large areas. However, the data products derived from satellite remote sensing platforms suffer from excessive reliance on weather conditions.…”

Section: Introductionmentioning

confidence: 99%

Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery

et al. 2023

Self Cite

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The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.

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Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China

Cited by 9 publications

References 48 publications

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

Accuracy of Estimated Crop Evapotranspiration Using Locally Developed Crop Coefficients against Satellite-Derived Crop Evapotranspiration in a Semiarid Climate

Phenology-Based Remote Sensing Assessment of Crop Water Productivity

Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery

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