Assimilation of Leaf Area Index and Surface Soil Moisture With the CERES-Wheat Model for Winter Wheat Yield Estimation Using a Particle Filter Algorithm

Xie, Yi; Wang, Pengxin; Sun, Huitao; Zhang, Shuyu; Li, Li

doi:10.1109/jstars.2016.2628809

Cited by 26 publications

(22 citation statements)

References 50 publications

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“…Table 5 lists the mean absolute percentage error (MAPE) results of simulated vs. measured total aboveground biomass per site. The standard run produced the highest MAPE in seven out of the fourteen sites (2,4,6,7,8,13,14), all of the CC assimilation approaches in five sites (1,3,9,11,12), and the EM CC approach in one site (5). The results for site 10 show an equal performance of the standard run and the WM CC approach.…”

Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 86%

“…Table 4 lists the per-site RMSE results of simulated vs. measured grain yields at harvest. In 10 sites, the highest RMSE was produced by SR (Sites 1, 2, 4, 6, 7, 8,9,12,13,14), in two sites by WM CC (Sites 3 and 11), and in two sites by EnKF SCC and EM SCC, respectively (Sites 10 and 5). The lowest RMSE was produced in 10 sites by either EnKF SCC or EM SCC.…”

Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 99%

“…Based on these measurements, the model can be modified and used to make predictions about future states of the crop [9]. A range of different observations, either field measurements or derived from remote sensing, have been assimilated into crop models: phenology [10,11], soil moisture content [12][13][14][15][16][17], canopy cover [18,19], and, most-frequently used, leaf area index (LAI) [10,[14][15][16]18,[20][21][22][23][24][25][26][27][28]. Defined as the total one-sided area of leaf tissue per unit of ground surface area (provided in m 2 m −2 ), LAI is one of the key parameters in crop growth analysis due to its influence on light interception, biomass production, plant growth and ultimately on crop yield, and it is critical to understand the functioning of many crop management practices [29,30].…”

Section: Introductionmentioning

confidence: 99%

“…A number of algorithms have been tested to update crop model state variables sequentially, such as the Particle Filter (PF) [14,[37][38][39], the Proper Orthogonal Decomposition-based Ensemble Four-Dimensional Variational Strategy (POD4DVar) [24,40] and the Ensemble Kalman Filter (EnKF) [12,13,15,27,28,[41][42][43]. EnKF, among others, uses a Monte Carlo approach to propagate model responses (state variables) forward in time based on a finite number of model replicates (ensemble) and the incorporated available observations [42].…”

Section: Introductionmentioning

confidence: 99%

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New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

et al. 2020

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The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.

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Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 86%

Section: Results Of Root Mean Squared Error (Rmse) Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

et al. 2020

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“…Some DA studies have successfully coupled microwave radar and optical remote sensing data (Betbeder et al, ; Clevers & van leeuwen, ; Dente et al, ). Various ways to combine LAI with other variables, such as surface soil moisture (Albergel et al, ; Y. Xie, Wang, Sun, et al, ) and ET (Vazifedoust et al, ), have been proven to be successful in regional applications.…”

Section: Lai Applicationsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

551

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data

Zhang

Chen

et al. 2020

Agricultural and Forest Meteorology

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Assimilation of Leaf Area Index and Surface Soil Moisture With the CERES-Wheat Model for Winter Wheat Yield Estimation Using a Particle Filter Algorithm

Cited by 26 publications

References 50 publications

New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data

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