Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

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.

Improving Winter Wheat Yield Estimation from the CERES-Wheat Model to Assimilate Leaf Area Index with Different Assimilation Methods and Spatio-Temporal Scales

“…This finding is similar to the observations of Nearing et al and Ines et al [53,68], in which the LAI and soil moisture were assimilated into the decision support system for the agro-technology transfer-cropping system (DSSAT-CSM) model. This finding is also similar to the observations of Huang et al [5], in which the MODIS LAI and ET products were assimilated into the WOFOST model. Thus, assimilating LAI, evapotranspiration (ET) and/or soil moisture represents another important area of research that will be investigated further, especially in arid and semi-arid areas.…”

“…Compared with traditional EnKF-based strategy and re-recalibration strategies (e.g., SCE-UA [5]), the PF-based strategy can sample particles and update particle weights according to any distribution function and is able to express the model estimation as a recursive process that avoids the higher dimensions of control variables and a high computational burden [22]. In this study, the resampling and reperturbation of the resampled particles were used to avoid the particle degeneracy and lack of particle diversity under the constraints of the model by using fewer particles.…”

“…In the past few decades, conventional agro-meteorological models and empirical statistical regression models based on field-measured yields and related remotely-sensed spectral vegetation indices [5] were used to monitor crop growth and yields, with the disadvantages of high-cost, laboriousness, inefficiency and only being applicable for local regions [6][7][8]. Process-oriented crop growth models can offer powerful tools to simulate the crop growth and obtain the crop yield under various environmental and management conditions with the advantages of cost, timeliness, accuracy and suitability at the field scale [6,[9][10][11][12][13][14], while they were also dynamically accounting for several limiting factors (e.g., soil, weather, water, nitrogen and field management data) at the regional scale [3,15].…”

Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

“…There are four advantages to employing such RS data in this study: broad coverage (360 km), a frequent revisit period (2 days), moderately high spatial resolution (30 m), and appropriate spectral resolution (four visible and near-infrared bands: 430-520 nm, 520-600 nm, 630-690 nm, and 760-900 nm). We obtained 15 [27] were performed by CRESDA before data sharing. Precise geometric correction was applied to the CCD images based on the Landsat TM 5 image of the study area.…”

Evaluation of Three Techniques for Correcting the Spatial Scaling Bias of Leaf Area Index

“…Leaf area index (LAI), defined as half the total foliage area per unit ground horizontal surface area [1], is an essential biophysical property of vegetation for environmental assessment [2,3], crop growth monitoring [4,5] and yield prediction [6][7][8]. The accurate monitoring of LAI on multiple scales has become a key technique in those applications.…”