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• A water footprint quantification method based on data assimilation was proposed. • Joint assimilation (LAI+SM) was more efficient than the open-loop simulation. • The results can better describe the spatial heterogeneity of wheat water footprint. • This method was suitable for regional water footprint quantification. Water scarcity in agricultural production has emerged as a significant constraint to food security in China. To improve agricultural output and ensure its stability, it is imperative to assess the agricultural water use efficiency. The crop water footprint (WF) is an effective tool to assess the type, amount, and efficiency of agricultural water usage. However, existing quantitative studies on the crop water footprint within regions exhibit limitations in terms of their low spatial resolution and deficient spatial heterogeneity. In this study, the World Food Studies (WOFOST) model was combined with the Ensemble Kalman filter (EnKF) assimilation algorithm, with the leaf area index (LAI) and soil moisture (SM) obtained by remote sensing as state variables. An improved method for quantifying the WF of wheat based on crop model-remote sensing information data assimilation was proposed. Regarding the results, the average blue water footprint (WF blue), green water footprint (WF green), and total water footprint (WF) of wheat were 363 m3/t, 551 m3/t, and 914 m3/t, respectively. In the southern of the study area, WF green exhibits higher values, with lower levels in the north. WF blue and WF demonstrated inverse spatial patterns, with higher values observed in the northern areas and lower values in the southern regions. The spatial heterogeneity of WF blue and WF was more significant than that of WF green. The WF of wheat quantified by the data assimilation method had a high spatial resolution, and could be effectively used to explore the spatial heterogeneity of WF. This study can provide a reliable reference for the effective use of water resources. [ABSTRACT FROM AUTHOR]

With the warming trend and the increasing frequency of extreme weather events, accurate crop yield estimation is becoming urgent. Crop yield estimation mainly consists of two methods: crop model simulation and remote sensing observations. Crop models can achieve accurate simulations of crop growth at field scales. However, in regional applications, they are limited by the spatial heterogeneity of certain input parameters. Remote sensing observations can obtain crop status over large areas quickly and conveniently, while lacking knowledge of crop growth processes. By combining the advantages of crop models and remote sensing, crop yield estimation with spatiotemporal continuity can be achieved using data assimilation methods. The research progress of the three elements of data assimilation system has been quantitatively reviewed in this paper. And the relevant literature was quantitatively screened and reviewed to provide a systematic overview of the application of data assimilation in crop yield estimation. In this study, the scientific background of the data assimilation system for crop yield estimations was described, and basic principles of data assimilation were introduced. A second part of this review screened and reviewed the relevant literature quantitatively. The answers to problems on: the most widely used crop model, the assimilation algorithm, and the assimilation variables were reported. Finally, a synthesis of the emerging directions and challenges of data assimilation systems for crop yield estimation were discussed. The results show that: a) the sequential assimilation method is the most widely used algorithm in the field of data assimilation, especially EnKF. b) WOFOST, DSSAT, AquaCrop and SAFY are the most common models in the research of data assimilation for yield estimation. c) In terms of assimilation variables, LAI (leaf area index), SM (soil moisture), and VIs (vegetation indexes) are relatively common assimilation variables. Research progress of data assimilation system for crop yield estimation is summarized from the aspects of algorithm improvement, model coupling research, multi-source remote sensing data assimilation and multiple assimilation variables. This review quantitatively examined and contrasted the research progress of data assimilation systems providing researchers with a more comprehensive background. It also suggests ideas and references for higher resolution, more accurate and reliable yield estimation for future research. [Display omitted] • Data assimilation is a vital tool to estimate and forecast regional crop yield. • The 143 papers on crop yield estimation by data assimilation published during 2000–2021 were searched and reviewed. • The paper focused on current models, algorithms, variables, spatial resolution and period of assimilation. • The progress of multisource and multivariable research was discussed. • The challenges and future directions were expounded. [ABSTRACT FROM AUTHOR]