Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and  FORMOSAT-2 Images

Duchemin, B.; Fieuzal, Rémy; Rivera, M. A.; Ezzahar, Jamal; Jarlan, Lionel; Rodríguez, Julio; Hagolle, Olivier; Watts, Christopher J.

doi:10.3390/rs70505951

Cited by 56 publications

(40 citation statements)

References 75 publications

(108 reference statements)

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“…This underestimation corresponds to a global overestimation of the ETa (Table 3). This finding is in agreement with what has been reported by Duchemin et al [29], who observed a 20% error in ETa model simulation compared to the measurements by eddy covariance. The overestimation of water demands provide false information to decision maker for more irrigation than actually needed.…”

Section: Discussionsupporting

confidence: 93%

“…The SAFYE model, developed by Duchemin, et al [29] and Battude, et al [30], was used in this study. This model couples the original SAFY crop growth model [25] with the FAO-56 soil water budget model [31] to simulate crop growth and soil moisture at a daily step, including LAI, aboveground biomass, grain yield, crop transpiration, and soil water content.…”

Section: The Safye Modelmentioning

confidence: 99%

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Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

et al. 2019

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Accurate information of crop growth conditions and water status can improve irrigation management. The objective of this study was to evaluate the performance of SAFYE (simple algorithm for yield and evapotranspiration estimation) crop model for simulating winter wheat growth and estimating water demand by assimilating leaf are index (LAI) derived from canopy reflectance measurements. A refined water stress function was used to account for high crop water stress. An experiment with nine irrigation scenarios corresponding to different levels of water supply was conducted over two consecutive winter wheat growing seasons (2013-2014 and 2014-2015). The calibration of four model parameters was based on the global optimization algorithms SCE-UA. Results showed that the estimated and retrieved LAI were in good agreement in most cases, with a minimum and maximum RMSE of 0.173 and 0.736, respectively. Good performance for accumulated biomass estimation was achieved under a moderate water stress condition while an underestimation occurred under a severe water stress condition. Grain yields were also well estimated for both years (R 2 = 0.83; RMSE = 0.48 t·ha −1 ; MRE = 8.4%). The dynamics of simulated soil moisture in the top 20 cm layer was consistent with field observations for all scenarios; whereas, a general underestimation was observed for total water storage in the 1 m layer, leading to an overestimation of the actual evapotranspiration. This research provides a scheme for estimating crop growth properties, grain yield and actual evapotranspiration by coupling crop model with remote sensing data.

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Section: Discussionsupporting

confidence: 93%

Section: The Safye Modelmentioning

confidence: 99%

Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

et al. 2019

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“…Meanwhile, the yield of different water deficit scenarios in the late growth stage was hard to differentiate due to similar LAI and dry matter. To overcome this weakness of the model, Duchemin et al (2015) proposed to add a stress factor to response the effect of soil water deficit to crop development by integrating water budget (Allen et al, 1998) into the SAFY model. Moreover, the dynamic positive and negative effects of HI generated by some functions or modules related to water and heat stress could improve adaptability and accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…All of the stresses induced by water, nutrient, salinity, etc., are assumed to be integrated in a parameter of effective light use efficiency (ELUE). To date, the SAFY model has been verified on several crops such as corn (Claverie et al, 2012; Battude et al, 2016) and wheat (Fieuzal et al, 2011; Duchemin et al, 2015; Dong et al, 2016) under different climatic conditions. Claverie et al (2012) used the model to estimate sunflower yield with good accuracy ( r = 0.97, p < 0.03).…”

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confidence: 99%

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

et al. 2019

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Winter wheat (Triticum aestivum L.) is one of the major grain crops grown in Guanzhong Plain, China. Rapid and accurate crop growth and yield estimation are important for agricultural monitoring and policy decision‐making. Crop model simulation is an effective way to provide objective growth and yield forecast. In this study, we investigated the applicability of the Simple Algorithm for Yield Estimate (SAFY) model for estimating winter wheat dry shoot biomass (MS,d) and grain yield, using two growing seasons field data from different irrigation scenarios. Results showed that the leaf area index (LAI) could be reasonably well simulated, with a minimum root mean square estimate (RMSE) of 0.11. The water stress intensity of each irrigation scenario was well accounted for through stress factor of effective radiation use efficiency. Good accuracy were achieved for MS,d simulation in both calibration (RMSE = 0.054–0.183 kg m−2) and validation (RMSE = 0.146 kg m−2) datasets, but showed a general overestimation during later growth stages. The grain yield was well estimated with a relative error of 1.9% to 16.7% in 2013 to 2014 and 0.1% to 16.7% in 2014 to 2015. The comparison between estimated and measured yield of all irrigation scenarios were robust in both seasons, with the minimum RMSE and MRE of 35.0 g m−2 and 4.7%, respectively. This work demonstrates the potential of a simple crop model for estimating biomass and yield only by calibrate the LAI without further in situ data, and prefigures assimilation application with remote sensing data in future. Core Ideas The SAFY model was evaluated in different irrigation scenarios. The variation of LAI was highly sensitive to leaf partition parameters. SAFY effectively simulated wheat biomass and yield under various irrigation scenarios. A general overestimation on biomass was noted during later growth stages.

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“…The same modification had been formerly proposed by [47,48], introducing a dependence of biomass yield on crop water stress. This modification was made since it allows an explicit assessment of the crop response to water availability and the characterization of water stress, which is lacking in the original model.…”

Section: Crop Growth Simulation Modelsmentioning

confidence: 90%

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

et al. 2017

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Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models' specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. Keywords: leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO)

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Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and FORMOSAT-2 Images

Cited by 56 publications

References 75 publications

Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

Coupling Hyperspectral Remote Sensing Data with a Crop Model to Study Winter Wheat Water Demand

Evaluation of the Simple Algorithm for Yield Estimate Model in Winter Wheat Simulation under Different Irrigation Scenarios

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

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