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TY  - THES
AU  - Hao, Shirui
TI  - Integrating remotely sensed information into the APSIM wheat model to improve crop yield prediction
PB  - Rheinisch-Westfälische Technische Hochschule Aachen
VL  - Dissertation
CY  - Aachen
M1  - RWTH-2024-08168
SP  - 1 Online-Ressource : Illustrationen
PY  - 2024
N1  - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University 2025. - Cotutelle-Dissertation
N1  - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2024
AB  - In agriculture, monitoring crop growth, and predicting crop yield in a timely manner are of great importance. Crop yield modelling and forecasting provide information to test various crop management options, guide crop breeding, understand and explore mitigation of environmental impacts, and optimise production. Process-based crop models, such as the Agricultural Production Systems sIMulator (APSIM), have been widely applied to simulate crop growth and predict yield because they incorporate the current understanding of complex crop-environment dynamics. However, models simplify complex processes and predict yield with uncertainty. Remote sensing technology provides spatially distributed and reliable quantitative estimation of crop status near real-time, which can be integrated with crop models by using data assimilation methods to mitigate prediction uncertainties and improve predictive performance. This thesis (1) reviews the performance of the APSIM-Wheat model and identifies the important factors that influence yield prediction uncertainty; (2) undertakes sensitivity analyses using the Sobol’ method to examine the sensitivity of crop yield prediction to six influential parameters identified in part (1); (3) determines the most suitable APSIM-wheat state variables for data assimilation and develops an observation operator to facilitate model updating with remote sensing observations; and (4) develops a data assimilation scheme to integrate remotely sensed crop information into the APSIM-Wheat model. The proposed scheme improves the wheat yield estimation performance and enables the model to better simulate spatial variability in yield. Chapter 2 reviews the performance of APSIM-Wheat, one of the most popular crop modules in APSIM, and identifies the factors that influence yield prediction uncertainty. Model evaluation results from 76 published studies across thirteen countries on four continents were analysed. In addition, a meta-database of modelled and observed yields was established from 30 papers within these studies. The analysis indicates that with site-specific calibration, APSIM predicts yield with an RMSE typically smaller than 1 t/ha under a wide range of environments. For rainfed wheat, the review and meta-analysis found that estimated soil hydraulic characteristics, soil water conditions, nitrogen availability, heat and frost events, and some other abiotic stresses (lodging and root disease) lead to larger yield prediction residuals and uncertainty. Integrating satellite observations into APSIM-Wheat is hypothesised to improve the yield prediction accuracy and robustness. As an important steppingstone towards the satellite-APSIM integration, a global sensitivity analysis (using the Sobol’ method) was conducted to rank the sensitivity of influential parameters affecting yield prediction (Chapter 3). Results indicate that precipitation, initial soil nitrogen content, and soil parameters have the largest influence on yield variability. APSIM’s yield prediction becomes more sensitive to a factor when that factor becomes more limiting. These results are used to guide the design of data assimilation schemes for crop models. Green leaf area index (GLAI) was selected as the variable to assimilate into the APSIM-Wheat model. Sentinel-2 was chosen to produce the observations due to its high spatial (10-20 m) and temporal (5 days) resolutions and its relevance to estimating GLAI. An observation operator, which provides the mathematical mapping from model state to observation space (or vice versa), was developed to link several vegetation indices with APSIM GLAI in Chapter 4. The results show that Sentinel-2 derived chlorophyll index (CI) calculated using red edge bands have the closest relationship with APSIM simulated GLAI. The uncertainty of the observation operator was also determined and used to represent the background prediction uncertainty in the observation space. Data assimilation (DA) is a set of statistical techniques that can be employed to combine external information (such as in situ measurements or remotely sensed observations) with a model to improve model prediction performance. It enables the updating of model time-step state variables using available information. In Chapter 5, a synthetic data assimilation experiment was conducted to test the updated state variables, the updating periods, the updating intervals, and the uncertainties added to the model and observations. It was found that updating all biomass components in the wheat module (grain, leaf, stem, spike, and root) from the whole duration of sowing-harvest at a daily frequency resulted in the best yield prediction performance. A more realistic 5-day updating interval still resulted in noticeable improvement. The designed data assimilation strategy was validated for eight scenarios representing high-, medium-, and low-yield cases. The results show that updating all biomass states every 5 days across the whole growing season effectively corrected yield prediction residual by 51
LB  - PUB:(DE-HGF)11
DO  - DOI:10.18154/RWTH-2024-08168
UR  - https://publications.rwth-aachen.de/record/992344
ER  -