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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Ultra-Stable Optical Oscillator Transfer to the UV for Primary Thermometry
摘要： To improve the performance of crop models for regional crop yield estimates, a particle filter (PF) was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis (CERES)—Wheat model. Two experiments involving winter wheat yield estimations were conducted at a field plot and on a regional scale to test the feasibility of the PF-based data assimilation strategy and to analyze the effects of the PF parameters and spatio-temporal scales of assimilating observations on the performance of the crop model data assimilation. The significant improvements in the yield estimation suggest that PF-based crop model data assimilation is feasible. Winter wheat yields from the field plots were forecasted with a determination coefficient (R2) of 0.87, a root-mean-square error (RMSE) of 251 kg/ha, and a relative error (RE) of 2.95%. An acceptable yield at the county scale was estimated with a R2 of 0.998, a RMSE of 9734 t, and a RE of 4.29%. The optimal yield estimates may be highly dependent on the reasonable spatiotemporal resolution of assimilating observations. A configuration using a particle size of 50, LAI maps with a moderate spatial resolution (e.g., 1 km), and an assimilation interval of 20 d results in a reasonable tradeoff between accuracy and effectiveness in regional applications.
关键词： particle filter (PF),yield estimation,data assimilation,Crop model,leaf area index,remote sensing
更新于2025-09-19 17:13:59
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Unsteady pressure estimation and compensation capabilities of the hybrid simulation combining PIV and DNS
摘要： The hybrid unsteady-flow simulation, which has been developed to assimilate the Navier–Stokes simulation into a time-resolved particle image velocimetry (PIV) field by synchronizing the frame rate with the direct-numerical-simulation time step, can simultaneously produce an unsteady pressure field. The hybrid process typically introduces a ‘patch function’, which transplants the PIV fields inside the camera window into a larger computational domain. This study evaluates the pressure fields produced by the hybrid simulation inside the patch function (i.e. estimation) and outside of it (i.e. compensation). We take time-resolved planar PIV data of a separated flow past the NACA 0012 airfoil at an angle of attack of 30° in a water tunnel as an example. First, an unsteady pressure field produced from the hybrid simulation is compared with other existing pressure-estimation methods in two dimensions over the entire domain of the camera window. The results of the two-dimensional (2D) hybrid simulation are found to agree best with those of the sequential-integration method with much lower noise levels in time and space. Next, the domain of the forcing is reduced by truncating the patch function in the hybrid simulation, and the pressure fields in the missing parts of the patch function are compared with those solved using the full patch function. When at least the regions in which 2D vortical structures collapse are included in the patch function downstream, the rest of the pressure fields can be compensated for relatively well. In contrast, the forces acting on the airfoil can be estimated accurately only if it is enclosed by the camera window.
关键词： hybrid simulation,data assimilation,pressure estimation,particle image velocimetry
更新于2025-09-10 09:29:36
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Radiometric Cross-Calibration of GF-4 PMS Sensor Based on Assimilation of Landsat-8 OLI Images
摘要： Earth observation data obtained from remote sensors must undergo radiometric calibration before use in quantitative applications. However, the large view angles of the panchromatic multispectral sensor (PMS) aboard the GF-4 satellite pose challenges for cross-calibration due to the effects of atmospheric radiation transfer and the bidirectional reflectance distribution function (BRDF). To address this problem, this paper introduces a novel cross-calibration method based on data assimilation considering cross-calibration as an optimal approximation problem. The GF-4 PMS was cross-calibrated with the well-calibrated Landsat-8 Operational Land Imager (OLI) as the reference sensor. In order to correct unequal bidirectional reflection effects, an adjustment factor for the BRDF was established, making complex models unnecessary. The proposed method employed the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to find the optimal calibration coefficients and BRDF adjustment factor through an iterative process. The validation results revealed a surface reflectance error of <5% for the new cross-calibration coefficients. The accuracy of calibration coefficients were significantly improved when compared to the officially published coefficients as well as those derived using conventional methods. The uncertainty produced by the proposed method was less than 7%, meeting the demands for future quantitative applications and research. This method is also applicable to other sensors with large view angles.
关键词： GF-4 PMS,cross-calibration,SCE-UA,data assimilation,Landsat-8 OLI,BRDF
更新于2025-09-09 09:28:46
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Comprehensive Remote Sensing || A Data Assimilation-Based Approach for Estimating Land Surface Variables
摘要： The generation of timely, high-quality, long-term global information by remote sensing benefits society in numerous ways. Thus, geographers and scientists from other Earth science disciplines are developing various process-oriented models to characterize Earth system components. These models consolidate our scientific understanding of the range of physical processes that drive the Earth system, thereby facilitating predictions and providing knowledge that is useful for policy and management decision-making processes by federal agencies and international organizations (Liang, 2007). To advance global and regional models at various scales as well as improving their predictive capabilities, numerous biogeophysical variables are required to calibrate, validate, and drive these models. Estimating land surface variables from remote-sensing data is the only feasible method for generating land surface variable products at regional and global scales.
关键词： Data Assimilation,MODIS,EnKF,Land Surface Variables,Remote Sensing
更新于2025-09-04 15:30:14