Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and  Quality Estimation

Li, Zhenhai; Wang, Jihua; Xu, Xingang; Zhao, Chunjiang; Jin, Xiuliang; Yang, Guijun; Feng, Haikuan

doi:10.3390/rs70912400

Cited by 56 publications

(34 citation statements)

References 51 publications

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“…More recently, Ban et al (2019) [30] also integrated MODIS-derived seasonal LAI as well as water stress factors into the CERES-Maize model for improved accuracy of maize yield estimations. Similar improvements with the use of data integration have been reported by Guo et al [35], Launay and Guerif [32], and Li et al [36]. Although data integration has been shown to be an effective method for enhancing site specific yield estimation, most of the previous conclusions have been drawn from state-or regional-level studies [28][29][30]34] or plot-scale experiments [36].…”

Section: Introductionsupporting

confidence: 60%

“…Similar improvements with the use of data integration have been reported by Guo et al [35], Launay and Guerif [32], and Li et al [36]. Although data integration has been shown to be an effective method for enhancing site specific yield estimation, most of the previous conclusions have been drawn from state-or regional-level studies [28][29][30]34] or plot-scale experiments [36]. Limited studies have been carried out to address within-field variability [35,37].…”

Section: Introductionmentioning

confidence: 56%

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

et al. 2019

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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.

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Section: Introductionsupporting

confidence: 60%

Section: Introductionmentioning

confidence: 56%

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

et al. 2019

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“…Others have used it in combination with multiple classifiers and algorithms for crop classification [25]. The most frequent application, however, is the (re-)calibration of crop models such as the WOrld FOod STudies model (WOFOST) [26], the Simple Algorithm for Yield Estimate (SAFY) [27], the Decision Support System for Agrotechnology Transfer (DSSAT) [28,29], or AquaCrop [30,31].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Wagner

Slawig

Taravat

et al. 2020

IJGI

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A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel-and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.

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“…At the plant level, proximal hyperspectral imaging and field spectroscopy has been used extensively to estimate plant biophysical properties and as phenotyping tool in agricultural research [73][74][75]. In an effort to assimilate these data to crop models, Li et al [76] used two assimilation variables (LAI and canopy nitrogen accumulation, CNA) derived from a field spectrometer, in DSSAT-CERES crop model to improve yield prediction of winter wheat in an area in Beijing China. The results showed that both variables were accurately estimated with spectral indices and the integration of two state variables in the model was more robust than single variable integration.…”

Section: Estimation Of Crop Parameters From Remote Sensingmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

Cited by 56 publications

References 51 publications

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

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

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

Contribution of Remote Sensing on Crop Models: A Review

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