scholarly journals Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 719 ◽  
Author(s):  
Vijaya R. Joshi ◽  
Kelly R. Thorp ◽  
Jeffrey A. Coulter ◽  
Gregg A. Johnson ◽  
Paul M. Porter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Crop Model ◽  
Estimation Accuracy ◽  
Yield Estimation ◽  
Site Specific ◽  
Sensing Data ◽  
Integration Approach

Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.

scholarly journals Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

2021 ◽  
Vol 13 (6) ◽  
pp. 1094
Author(s):  
Xingshuo Peng ◽  
Wenting Han ◽  
Jianyi Ao ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Remote Sensing Data ◽  
Maize Yield ◽  
Estimation Accuracy ◽  
Corn Yield ◽  
Sensing Data ◽  
Water Treatments

In this study, we develop a method to estimate corn yield based on remote sensing data and ground monitoring data under different water treatments. Spatially explicit information on crop yields is essential for farmers and agricultural agencies to make well-informed decisions. One approach to estimate crop yield with remote sensing is data assimilation, which integrates sequential observations of canopy development from remote sensing into model simulations of crop growth processes. We found that leaf area index (LAI) inversion based on unmanned aerial vehicle (UAV) vegetation index has a high accuracy, with R2 and root mean square error (RMSE) values of 0.877 and 0.609, respectively. Maize yield estimation based on UAV remote sensing data and simple algorithm for yield (SAFY) crop model data assimilation has different yield estimation accuracy under different water treatments. This method can be used to estimate corn yield, where R2 is 0.855 and RMSE is 692.8kg/ha. Generally, the higher the water stress, the lower the estimation accuracy. Furthermore, we perform the yield estimate mapping at 2 m spatial resolution, which has a higher spatial resolution and accuracy than satellite remote sensing. The great potential of incorporating UAV observations with crop data to monitor crop yield, and improve agricultural management is therefore indicated.

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

2019 ◽  
Vol 11 (14) ◽  
pp. 1684 ◽  
Author(s):  
Chao Zhang ◽  
Jiangui Liu ◽  
Taifeng Dong ◽  
Elizabeth Pattey ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Winter Wheat ◽  
Water Demand ◽  
Stress Condition ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Actual Evapotranspiration ◽  
Sensing Data ◽  
Water Stress Condition

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 (R2 = 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.

Estimation of winter wheat yield by using remote sensing data and crop model

2012 ◽  
Author(s):  
Jianmao Guo ◽  
Tengfei Zheng ◽  
Qi Wang ◽  
Jia Yang ◽  
Junyi Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Winter Wheat Yield

Simulation of regional rice growth by combination remote sensing data and crop model

2014 ◽  
Author(s):  
Jianmao Guo ◽  
Yanghua Gao ◽  
Junwei Liu ◽  
Dunyue Fei ◽  
Qian Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Crop Model ◽  
Sensing Data ◽  
Rice Growth
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