Conclusions and recommendations

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studies, updating has been integrated with variational techniques. The hybrids approaches have shown good performance because take advantage of strength of both techniques.

As a matter of fact, EO data availability will steadily increase in the future. Data will be delivered from sensors with diverse technical characteristics. In this scenario more research is needed with respect to:

 multisource EO data assimilation techniques (optical and microwave data) testing new filter and hybrid approaches;

 fusion of EO data to match the CGMs spatial and temporal scale with user demands;

 coupled CGM, atmosphere and radiative transfer models (RTM), aiming to further reducing uncertainties.

6.4 Maize yield estimation by using GRAMI and SAFY models 6.4.1 Sensitivity analyze of GRAMI and SAFY parameters

The present work proves that crop biomass estimation is highly influenced by Light Use Efficiency. The effect of parameters such as sowing date, GDD to emergence and GDD to maturity is conditioned by the daily temperature. The role of senescence in biomass estimation is stronger in the GRAMI model because there is a direct connection between biomass synthetized and senescence. In the SAFY formulae these two processes are decoupled.

The parameters with a stronger influence in the calibration of the LAI are SLA, A, B and LUE for both models. Moreover, the study shows that these parameters together with K and LAI at emergency (LAIin) are highly correlated. This high correlation explains the ill-posed problem when they are calibrated together. On the other hand, parameters related with senescence and GDD to maturity had a low correlation with the rest of the parameters.

6.4.2 Sensitivity of LUT and PSO to the error level, frequency and gaps of LAI data

The experiment shows that a minimum LAI frequency suitable to calibrate GRAMI and SAFY models is 15 days for LUT and 20 days for PSO.

The calibration techniques are strongly sensitive to LAI data gaps. The yield estimation error increases sharply if the LAI data series used in CGMs calibration has gaps during crop emergency. Therefore, the LAI observations should be well distributed in the crop season.

Finally, the average LAI random error should be no larger than 20%. This threshold has more relevance when the crop yield is estimated using GRAMI and the model parameters are calibrated by LUT approach.

6.4.3 Yield estimation using calibrated GRAMI and SAFY models by iLUT and PSO techniques

We could further prove that the filtering of the retrieved LAI using the Whittaker filter with smoothing parameter λ equal 300 improves the parameter calibration. The smoothed LAI observations have a better fitting with the LAI simulations obtained by SAFY and GRAMI models. Consequently, the biomass estimation is improved. The simple cost function shows an acceptable performance for the LUT and PSO techniques without requiring ancillary data.

Regarding iLUT technique, the regional yield is underestimated by SAFY and overestimated by GRAMI. This trend was verified in the seasons 2013 and 2014. The dispersion of parameter levels and yield as well as the global LAI error is reduced along the LUT iterations.

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SAFY calibrated by PSO at plot scale achieves the best performance with a lower error at plot and regional scales in relation with GRAMI estimations. However, the error yield estimation increases when phenological parameters such as Sw, GDDem, GDDmt are included in the optimization routine.

The present work proves the increase of the yield estimation accuracy by using pixel data. The best results are observed using PSO for a reduced number of free parameters with low inter- correlation. The yield variability and the error at plot and regional scale is reduced, in most of the cases, in relation with the plot approach.

The calibration techniques perform well in forecasting maize yield one and two months before harvesting. A clear trend in yield estimation accuracy is noticed. The error is lower at regional scale than plot scale. Moreover, a better accuracy is achieved one month than two months before crop harvesting. These trends are explained by the compensation of errors at regional scale and the reduction in the uncertainty of the meteorological data.

6.4.4 Updating of GRAMI and SAFY models using EnKF and PF

The sensitivity analysis implemented for EnKF and PF shows us the effect of the model and observation error in the filter performance. The underestimation of the model error has a strong effect on updating performance. Therefore, this study applies an innovative technique, using pixel-based data, to calculate a representative model error.

Regarding the maize yield estimation, the combination calibration + updating shows a good performance. For 2013 SAFY + PSO + EnKF achieve the best accuracy. While in 2014 the combination SAFY + PSO + PF yields the best results. This confirms the better performance of SAFY compared with GRAMI in our study. We further note that the efficiency of updating is constrained by the accuracy of the CGM calibration. The study shows that the filter acts as a fine tuning which allows adding extra information from pixel scale.

Considering the good performance of PF in seasons 2013 and 2014, we recommend the calibration of the SAFY model at plot scale using PSO and the updating at pixel scale using PF. This combination between techniques results in a suitable accuracy both at regional and plot scale. Additionally, the low computational demand of PF makes possible the updating of SAFY at pixel scale in large regions.

As a whole, the study confirms the robustness of simple crop growth models. The main assumption of the GRAMI and SAFY models is verified. The LUE calibrated by LAI observations is able to model the agro-environmental conditions of maize. The approach achieves a good accuracy at regional and plot scale with a minimum demand of input data. The strategy of parameter calibration at plot scale and updating at pixel scale is suitable to estimate accurately maize yield in Marchfeld.

Finally, based on the results, we state that the SAFY model calibrated and updated by remote sensing is able to monitor the dynamics of vegetation, to detect anomalies in crop phenology and to predict accurately above-ground biomass at regional and plot scales.

6.5 Recommendations and further research

Being aware of the computational demand of applying CGM in large regions, we recommend testing a strategy to calibrate cluster of samples instead of individual plots. Then an iterative LUT could run independently for each group of samples. We suggest assessing the effect of the parameter dispersion on the accuracy of CGM estimations. This is relevant when calibrated multi (inter-) correlated parameters (ill-posed problem).

Considering the availability of multi-source remote sensing data and the impact of the LAI accuracy in the calibration of CGMs, further research should focus on testing a routine to assimilate

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the surface reflectance collected by remote sensors directly in SAFY model. Then, SAFY would be coupled with a radiative transfer model and atmosphere model. The main advantage of this strategy would be to avoid the error of the LAI retrieval and the possibility to assimilate multi-source remote sensing data.

We recommend validating our assimilation routine (calibration + updating) in rain feed regions aware of the sensitivity of the crops to the spatial distribution of rainfall and to the soil water content.

In this way, the following assumptions can be validated:

 the LUE is an efficient indicator of the global agro-environmental stress;

 the crop yield can be calculated accurately using the total above biomass and a constant harvest index;

Moreover, one can compare the efficiency of senescence simulation using GRAMI and SAFY approaches.

Finally, we encourage implementing the SAFY model calibrated at plot scale and updated at pixel scale to estimate yield of other crops with importance in the commodities world market such as winter cereals and soybean.

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