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• W3209308521 abstract "Abstract Accurate crop growth 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 show low efficiency and can only return limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. As well, crop growth models can dynamically reflect the biological processes of crop growth and development. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this paper, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data in the crop growth model of the simple algorithm for yield estimation (SAFY). The SP-UCI optimization algorithm is used for fine-tuning 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 in 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.734, 0.831 and 0.487, respectively, with corresponding root-mean-squared error (RMSE) values of 0.716, 1.13 and 1.14, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.853, and 0.607, respectively, with respective RMSE values of 0.828, 1.217, and 1.389, 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." @default.
• W3209308521 created "2021-11-08" @default.
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• W3209308521 date "2021-10-25" @default.
• W3209308521 modified "2023-09-24" @default.
• W3209308521 title "Wheat Growth Monitoring And Yield Estimation Based On Remote Sensing Data Assimilation Into The SAFY Crop Growth Model" @default.
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• W3209308521 doi "https://doi.org/10.21203/rs.3.rs-960801/v1" @default.
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