Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models

Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST–ACRM model with Ensemble Kalman Filter

Crop yield estimation at the field scale is essential for farmers, crop insurance companies to make informed decisions. Methodologies based on assimilating remote sensing LAI into crop growth models have shown advantages in crop yield estimates. Compared with MODIS and Landsat, Sentinel-2 satellites provide higher spatial and temporal resolution data, which brings revolutionary opportunities for crop monitoring. This study is to evaluate the performance of assimilating Sentinel-2 LAI into the WOFOST model for winter wheat yield estimation using the Ensemble Kalman Filter algorithm. The results showed that assimilating Sentinel-2 LAI improved the yield estimation (R2 = 0.45; RMSE = 512 kg/ha) compared to the situation without data assimilation (R2 = 0.27; RMSE = 818 kg/ha), which demonstrated the potential usage of the Sentinel-2 LAI for yield estimation at the field scale.

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Accurate and timely regional crop yield information, particularly field-level yield estimation, is essential for commodity traders and producers in planning production, growing, harvesting, and other interconnected marketing activities. In this study, we propose a novel data assimilation framework. Firstly, we construct the likelihood constraints for a process-based crop growth model based on the previous year’s statistical yield and the current year’s field observations. Then, we infer the posterior sets of model-simulated time-series LAI and the final yield of winter wheat with a Markov chain Monte Carlo (MCMC) method for each meteorological data grid of the European Centre for Medium-Range Weather Forecasts Reanalysis (v5ERA5). Finally, we estimate the winter wheat yield at the spatial resolution of 10 m by combining Sentinel-2 LAI and the WOFOST model in Hengshui, the prefecture-level city of Hebei province of China. The results show that the proposed framework can estimate the winter wheat yield with a coefficient of determination R2 equal to 0.29 and mean absolute percentage error MAPE equal to 7.20% compared to within-field measurements. However, the agricultural stress that crop growth models cannot quantitatively simulate, such as lodging, can greatly reduce the accuracy of yield estimates.

Data assimilation has been demonstrated as the potential crop yield estimation approach. Accurate quantification of model and observation errors is the key to determining the success of a data assimilation system. However, the crop growth model error is not fully taken into account in most of the previous studies. The objective of this study is to better quantify the model uncertainty in the data assimilation system. Firstly, we calibrated a crop growth model and inferred its posterior uncertainty based on the Global LAnd Surface Satellite (GLASS) 250-m LAI product, regional statistical data, station observations, and field measurements with a Markov chain Monte Carlo (MCMC) method. Secondly, the model posterior uncertainty was used in the Ensemble Kalman Filter (EnKF) algorithm to better characterize the ensemble distribution of model errors. Our results indicated the proposed Bayesian posterior-based EnKF can improve the accuracy of winter wheat yield estimation at both the point scale (the coefficient of determination R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value increasing from 0.06 to 0.41, the mean absolute percentage error MAPE value decreasing from 12.65% to 7.82%, and the root mean square error RMSE value decreasing from 987 to 688 kg∙ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) and the regional scale (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value from 0.30 to 0.57, MAPE value from 19.67% to 10.13%, and RMSE value from 1275 to 695 kg∙ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) compared with the open-loop estimation. Our analysis also indicated that the Bayesian posterior-based EnKF can perform better compared to the standard Gaussian perturbation-based EnKF. The proposed framework provides an important reference for crop yield estimation at the regional scale in similar agricultural landscapes worldwide.

Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window

The article is devoted to research of problems and substantiation of definition of zones of cultivation of the basic crops in Ukraine. The basic basis for determining the areas of cultivation of basic agricultural crops is recommended natural-agricultural zoning (the allocation of zones, provinces, districts, natural-agricultural areas), which is a consequence of agri-environmental heterogeneity of the territories of Ukraine. But the scheme of natural-agricultural zoning can be considered only as a frame when it comes to thematic in this case agri-environmental differences of the territory of Ukraine. An attempt to understand them and to distinguish them is necessary in the classification of arable land for the suitability of soils for the cultivation of basic crops. If we consider at least briefly the methodological sequence of determining suitability, the first question arises about the allocation of areas of cultivation of these crops, namely: winter wheat, winter rye, barley, oats, corn for grain, sugar beet, sunflower, potatoes, flax.For this purpose, according to published data, it is necessary to study, analyze the requirements of individual crops for heat, moisture, light at different phases of development, determine the quantitative need of each factor during the critical periods of plant growth and development, and accordingly, supporting tables for these crops should be drawn up. On the basis of these data, they form a complex characteristic of arable land of Ukraine in relation to the agro-ecological requirements of these crops. The characteristics include, firstly, the total area of the crop cultivation area in Ukraine and the affiliation of certain parts of it to taxa (units) of natural agricultural areas; secondly, each individual zoning taxon has areas that are subject to the suitability of arable land of a particular crop and a score of that area for yield. The area of the first, second and third subclasses of suitability for the area of all cereals, the first and the second — for other named crops, that is, the area on which the cultivation of crops is not accompanied by radical amelioration measures is related to the suitability of the area. This is information that allows us to identify areas with a relative environmental optimum for each crop. It is very important that having suitable acreage and yield estimation, it is possible to determine the production volumes of individual crops under conditions close to the ecological optimum, which is also one of the decisive factors in creating real prerequisites for environmentally friendly land use.

Continuous time series crop growth monitoring during the main crop growth and development period at field scales is very important for crop management and yield estimation. For more than a decade, the time series leaf area index (LAI) products obtained from high temporal resolution satellites have been widely used in global crop growth monitoring. However, the spatial resolutions (250–1000 m) of these satellite sensors are too coarse for areas with complex and diverse land-use types, especially in China, which causes great uncertainties in crop growth monitoring and yield estimation results. In addition, due to the influence of clouds, optical remote sensing satellites cannot obtain continuous time series data at a given time step over the main crop growth and development period. In this paper, a method based on spatiotemporal data fusion and singular vector decomposition (SVD) is proposed to reconstruct field-scale time series LAI imagery over the main growth and development period of winter wheat. In this method, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) is used to fuse the reflectance imagery of Sentinel-2 and Sentinel-3, and a linear regression model between the LAI data retrieved from the fused reflectance data and the singular vectors derived from the 4-day interval Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data is established to reconstruct the continuous time series field-scale LAI imagery at a given time step. The accuracy of the reconstructed LAI and its capability for winter wheat yield estimation were tested on the Guanzhong Plain of China. The results indicate that (1) the ESTARFM model can fuse the reflectance bands from visible to shortwave infrared of Sentinel-2 and Sentinel-3 on the Guanzhong Plain accurately within a 20-day interval of the winter wheat growth and development period; (2) the 4-day interval field-scale LAI imagery over the main winter wheat growth and development period can be accurately reconstructed based on the linear regression models between the fused LAI data and the singular vectors derived from the 4-day interval MODIS LAI data; and (3) the yield map estimated from the reconstructed field-scale LAI shows more yield distribution details than MODIS yield estimation results. This study shows the feasibility of reconstructing continuous time series field-scale LAI data over the main winter wheat growth and development period on the Guanzhong Plain by combining the spatiotemporal data fusion model with SVD and the potential for estimating the winter wheat yield at field scales.

Accurate regional crop growth monitoring and yield prediction is very critical for the national food security assessment and sustainable development of agriculture, especially for China, which has the largest population in the world. Remote sensing data and crop growth model have been successfully used in the crop production prediction. However, both of them have inherent limitation and uncertainty. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The aim of this paper is to improve the estimation of regional winter wheat yield of crop growth model by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. WOrld FOod STudies (WOFOST) crop growth model was chosen as the crop growth model which was calibrated and validated by the field measured data. MODIS Leaf Area Index (LAI) values were used as remote sensing observations to adjust the LAI simulated by the WOFOST model based on EnKF. The results illustrate that the EnKF algorithm has significantly improved the regional winter wheat yield estimates over the WOFOST simulation without assimilation in both potential and water-limited modes. Although this study clearly implies that the assimilation of the remotely sensed data into crop growth model with EnKF algorithm has the potential to improve the prediction of regional crop yield and has great potential in agricultural applications, high resolution meteorological data and detailed crop field management are necessary to reach a high accuracy of regional crop yield estimation.

Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

Crop Growth Monitoring System (CGMS) was originally developed by European Union based on a crop growth model (WOFOST) with the goal of regional yield estimations for major crops. The CGMS, driven by the geographic information system and the crop growth model, is composed of three main components: weather monitoring, crop monitoring and crop yield forecasting. In this study, the CGMS is introduced into China Agriculture Remote Sensing Monitoring System (CHARMS) to improve its ability of crop yield estimation. To do that, Hebei Province in North China Plain is selected as the study area. The CGMS is localized and the important parameters are verified by using local ground truth data. Its input datasets is divided into two parts, i.e., static and dynamic data. The former includes soil, crop distribution, phenology, administrative maps, the later includes the weather data, time-series remotely sensed data, and crop statistics. After that, the CGMS is used to estimate the crop yield for winter wheat in Hebei province. The historical crop yield data is used to validate the model estimations. The results show that the localized CGMS can monitor well the crop growth processes and is able to estimate the crop yield at regional scale. The CGMS can serve the agricultural macro-economic regulation and control, the management of agricultural production, as well as the food warning system of China.

Timely and accurate crop growth monitoring and yield estimation are vital for guaranteeing food security and agricultural sustainable development. As two main methods of crop yield estimation, crop growth simulation models and remotely sensed methods both have their respective advantages. Crop growth models are widely used in simulating crop development stage and yield in a small scale while satellite remote sensing has a great advantage in agriculture monitoring for its spatial continuity and temporal dynamic property. Data assimilation has become an effective way of estimating crop yield for it overcomes the limitations by combining satellite remote sensing data and crop growth models. Leaf Area Index (LAI) is an important vegetation biophysical parameter, which has been extensively applied in crop yield estimation. This study presents a method of assimilation of Moderate Resolution Imaging Spectrometer (MODIS) LAI data product into World Food Studies (WOFOST) model for winter wheat yield with Ensemble Kalman Filter (EnKF). We take winter wheat of Xinghua in Jiangsu province as the study object and chose WOFOST as the crop growth simulation model. Several winter wheat variety inheritance parameters and soil parameters are adjusted by the field measured data. Other sensitive parameters are adjusted by using FSEOPT optimization program to recalibrate the model and simulate the development stage and eco-physiological processes more accurately. The values of MODIS LAI (MCD15A3) are relatively low because of cloud contamination and mixed pixels. To solve this problem, this study firstly applies a Savitzky-Golay (S-G) filtering algorithm to MODIS LAI products to obtain filtered LAIs and then correct the filtered LAIs with field measurements data. This method can eliminate the anomalies and improve the accuracy of the MODIS LAI effectively. We take LAI as the assimilation state variable of EnKF algorithm and corrected MODIS LAI as observed data. Finally, the time-continuous LAI values are input WOFOST model to estimate winter wheat yield. We use statistics yield from Xinghua station to validate the accuracy of simulated yield. The root mean square error (RMSE) reduces from 587 kg/ha to 361 kg/ha compared to the official statistical yield data. Our results indicate that the accuracy of winter wheat yield is improved after the assimilation.