Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

Zhou, Gaoxiang; Liu, Xiangnan; Liu, Ming

doi:10.3390/rs11030268

Cited by 33 publications

(30 citation statements)

References 37 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: 87%

“…The results for site 10 show an equal performance of the standard run and the WM CC approach. The lowest MAPE was produced by the EnKF SCC approach in five sites (sites 2, 10,11,12,14) and in two sites by the WM SCC approach (sites 5, 6) and by the WM CC approach (sites 7, 13) respectively. In all other sites, several approaches performed equally well (sites 1, 3,4,8,9).…”

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

confidence: 99%

“…Table 3 presents the results for the RMSE analysis of simulated vs. observed biomass yields per site. In six sites, the highest RMSE was produced by the standard run (sites 2, 4, 6, 7, 12, 13, 14), in five sites by either of the two CC assimilation approaches (sites 1,8,9,10,11) and in one site by the EM SCC approach only (site 5). In five sites, the lowest RMSE was produced by the WM SCC approach (sites 4, 5, 6,8,9) and by the EnKF SCC approach, respectively (sites 2, 10, 11, 12, 14), in two sites by EM SCC (sites 1, 3) and WM CC approaches respectively (sites 7, 13).…”

Section: Evaluation Of Model Performancementioning

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%

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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: 87%

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

confidence: 99%

Section: Evaluation Of Model Performancementioning

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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“…Moreover, crop phenology is an indispensable module of some process-based crop models (e.g., World Food Study (WOFOST), Crop Environment Resource Synthesis (CERES), and ORYZA models [47][48][49]). Specifically, accurate phenology is crucial for improving crop growth simulation, net carbon land-atmosphere exchanges, and yield prediction [50,51].…”

Section: Significance For Improving the Satellite Estimation Of Wintementioning

confidence: 99%

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

Yang

Wang

et al. 2019

Remote Sensing

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Identification of complete drivers for phenology changes is crucial for developing prediction models of plant phenology. In addition to climatic factors, the interaction among phenological events has recently been reported as an important driver for the phenology changes of forests, savannas, and grasslands. However, open questions remain as to whether the phenological interaction exists in agricultural ecosystems, among which winter wheat plays a vital role in feeding human beings. In this study, we investigated the interaction among the phenological events of winter wheat in the North China Plain (NCP) using both field and satellite data. Considering the large discrepancies between the existing satellite estimation and field measurements of winter wheat phenology, we first improved the MODIS-based estimation of green-up date (GUD), heading date (HD), and maturity date (MD) through a re-calibrated relative threshold method (RTM) in the NCP. The GUD, HD, and MD were accurately estimated with the mean absolute errors (MAE) and root mean squared errors (RMSE) lower than 7.5 days, compared with the RMSEs ranging from 12.0 to 36.1 days in previous studies. Then, the relationships among the GUD, HD, and MD were analyzed using the field data collected at agricultural meteorological stations. The GUD (HD) showed a significantly positive correlation with the HD (MD). Quantitatively, a one-day earlier GUD (HD) would result in an earlier HD (MD) of 0.57 days (0.60 days). Furthermore, we applied the partial correlation analysis to the improved MODIS estimation of GUD, HD, and MD to investigate their interactions by considering the simultaneous influences from climatic factors. The results showed that the HD (MD) with 85.2% (94.5%) of all winter wheat pixels presented a significantly positive correlation with the GUD (HD). Meanwhile, the GUD (HD) with 84.2% (33.3%) of the entire winter wheat area presented a significantly negative correlation with pre-season temperature. These results suggest that both the climatic factors and phenological interactions should be included in the future development of winter wheat phenology models to improve the prediction accuracies. on NDVI, a novel snow-free type of VIs, including the empirical normalized difference phenology index (NDPI,[27]) and the semi-analytical normalized difference greenness index (NDGI,[26]), were recently proposed. Based on the time-series of the computed VIs, vegetation phenology can be estimated by extracting the day of year (DOY) corresponding to pre-determined absolute or relative thresholds of the VI values [1,15] or the maximum change rate of the trajectories of VIs [11,28]. For winter wheat, the GUDs were previously estimated based on either a relative threshold of 20% or a maximum change rate of the fitted curve of the time series VIs [1,2,10,11]. These studies mainly focused on changing trends of winter wheat phenology and its responses to climatic factors from the perspective of regarding the wheat fields as important agricultural ecosystems to the global...

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Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring

BELDA,

PIPIA,

VERRELST

2024

Multitemporal Earth Observation Image Analysis

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Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

Cited by 33 publications

References 37 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

Interactions among the Phenological Events of Winter Wheat in the North China Plain-Based on Field Data and Improved MODIS Estimation

Trends in Satellite Time Series Processing for Vegetation Phenology Monitoring

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