Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Novelli, Francesco; Spiegel, Heide; Sandén, Taru; Vuolo, Francesco

doi:10.3390/agronomy9050255

Cited by 53 publications

(34 citation statements)

References 46 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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 method is based on vegetation indices derived from spectral measurements and explained 78% of the LAI variability observed, slightly lower than the value achieved by the DL model developed in this study. Satellite images have also been used as inputs of an artificial neural network trained with radiative transfer models, PROSPECT (leaf optical properties model) and SAIL (canopy bidirectional reflectance model), to derive LAI maps in winter wheat (Novelli et al [69]). The authors obtained a coefficient of determination higher than 0.7 in two crop periods after comparing LAI estimates at ground level performed with the optical sensor LAI-2200 Plant Canopy Analyzer and satellite-derived observations.…”

Section: Novelty Of the DL Model Against Current Approaches Used For mentioning

confidence: 99%

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

et al. 2020

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Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

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“…Ref [13] assimilated Sentinel-2 derived LAI into the EPIC (Environmental Policy Integrated Climate) model to estimate winter wheat grain yield on Austrian experimental fields under four fertilization management strategies over the course of two growing seasons, using field-measured soil characteristics as model input. They found that assimilation via recalibration of parameters defining the LAI curve improved the model's performance notably during the first year (RMSE 317 kg ha −1 , RRMSE 6% compared to RMSE 572 kg ha −1 , RRMSE 11% without assimilation), but only slightly during the second year (RMSE 1961 kg ha −1 , RRMSE 55% compared to RMSE 2019 kg ha −1 , no RRMSE reported), with a strong underestimation of the observed yield.…”

Section: Sentinel-2 Lai Estimationsmentioning

confidence: 99%

“…A range of different observations (either field-measured or derived from remote sensing) have been assimilated into crop models, e.g. phenology [4], soil water content [5][6][7][8], canopy cover [9], and most-prominently used, leaf area index [2,7,8,[10][11][12][13][14][15]. Defined as the total one-sided area of leaf tissue per unit ground surface area, it is one of the key parameters in crop growth analysis because of its influence on light interception, biomass production, plant growth, and ultimately on crop yield, and it is critical to understanding the function of many crop management practices [16].…”

Section: Introductionmentioning

confidence: 99%

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

et al. 2020

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The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

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Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

Cited by 53 publications

References 46 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

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

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