a b s t r a c tTo predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China's Hebei Province. To reduce cloud contamination, we applied Savitzky-Golay (S-G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model's state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution-University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions (R 2 = 0.48; RMSE = 151.92 kg ha −1 ) compared with the unassimilated results (R 2 = 0.23; RMSE = 373.6 kg ha −1 ) and the TM LAI results (R 2 = 0.27; RMSE = 191.6 kg ha −1 ). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates.

No abstract

No abstract

Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model-data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method-University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy (R 2 = 0.43, RMSE = 619 kg · ha −1 ) than assimilating MODIS LAI data (R 2 = 0.28, RMSE = 889 kg · ha −1 ) or ET data (R 2 = 0.36, RMSE = 1561 kg · ha −1 ) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.Index Terms-Data assimilation, evapotranspiration (ET), leaf area index (LAI), remote sensing, soil water atmosphere plant (SWAP) model.

To estimate regional-scale winter wheat (Triticum aestivum) yield, we developed 21 a data-assimilation scheme that assimilates remote-sensed reflectance into a coupled 22 crop growth-radiative transfer model. We generated a time series of 8-day, 30-m-23 resolution synthetic Kalman Smoothed (KS) reflectance by combining MODIS 24 *Manuscript Click here to view linked References surface reflectance products with Landsat surface reflectance using a KS algorithm. 25 We evaluated the assimilation performance using datasets with different spatial and 26 temporal scales (e.g., three dates for the 30-m Landsat reflectance, 8-day and 1-km 27 MODIS surface reflectance, and 8-day and 30-m synthetic KS reflectance) into the 28 coupled WOFOST-PROSAIL model. Then we constructed a four-dimensional 29 variational data assimilation (4DVar) cost function to account for differences between 30 the observed and simulated reflectance. We used the shuffled complex evolution-31 University of Arizona (SCE-UA) algorithm to minimize the 4DVar cost function and 32 optimize important input parameters of the coupled model. The optimized parameters 33 were used to drive WOFOST and estimate county-level winter wheat yield in a region 34 of China. By assimilating the synthetic KS reflectance data, we achieved the most 35

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.

Sea spray can contribute significantly to the exchanges of heat and momentum across the air–sea interface. However, while critical, sea spray physics are typically not included in operational atmospheric and oceanic models due to large uncertainties in their parameterizations. In large part, this is because of the scarcity of in-situ sea spray observations which prevent rigorous validation of existing sea spray models. Moreover, while sea spray is critically produced through the fundamental interactions between wind and waves, traditionally, sea spray models are parameterized in terms of wind properties only. In this study, we present novel in-situ observations of sea spray derived from a laser altimeter through the adoption of the Beer–Lambert law. Observations of sea spray cover a broad range of wind and wave properties and are used to develop a wind–wave-dependent sea spray volume flux model. Improved performance of the model is observed when wave properties are included, in contrast to a parameterization based on wind properties alone. The novel in-situ sea spray observations and the predictive model derived here are consistent with the classic spray model in both trend and magnitude. Our model and novel observations provide opportunities to improve the prediction of air–sea fluxes in operational weather forecasting models.

No abstract