Field crop yield prediction is crucial to grain storage, agricultural field management, and national agricultural decision-making. Currently, crop models are widely used for crop yield prediction. However, they are hampered by the uncertainty or similarity of input parameters when extrapolated to field scale. Data assimilation methods that combine crop models and remote sensing are the most effective methods for field yield estimation. In this study, the World Food Studies (WOFOST) model is used to simulate the growing process of spring maize. Common assimilation methods face some difficulties due to the scarce, constant, or similar nature of the input parameters. For example, yield spatial heterogeneity simulation, coexistence of common assimilation methods and the nutrient module, and time cost are relatively important limiting factors. To address the yield simulation problems at field scale, a simple yet effective method with fast algorithms is presented for assimilating the time-series HJ-1 A/B data into the WOFOST model in order to improve the spring maize yield simulation. First, the WOFOST model is calibrated and validated to obtain the precise mean yield. Second, the time-series leaf area index (LAI) is calculated from the HJ data using an empirical regression model. Third, some fast algorithms are developed to complete assimilation. Finally, several experiments are conducted in a large farmland (Hongxing) to evaluate the yield simulation results. In general, the results indicate that the proposed method reliably improves spring maize yield estimation in terms of spatial heterogeneity simulation ability and prediction accuracy without affecting the simulation efficiency.

The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time series of remote sensing (T-RS) observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy.

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R² = 0.74, and root mean square error RMSE = 0.53 m² m⁻²). At the reproductive stage, a significant underestimation was found (R² = 0.41, and 0.89 m² m⁻² in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval.

Indicative value of nematode food web indices and trophic group abundance was assessed for differentiating habitats in two soil types. A total of 604 soil samples were collected from croplands, grassy borders, turfgrass lawns, shrublands and forests in Huron County, Ohio characterized by muck soils. Also, a total of 242 soil samples were collected from two habitats, turfgrass lawns and croplands, in Wayne County, Ohio characterized by mineral soils. All nematodes extracted from soil samples were identified to genus level, categorized to trophic groups and various nematode community indices were calculated. Multivariate analysis of variance using composite of nematode measures revealed that croplands were significantly different from forests, shrublands, turfgrass lawns and grassy borders in the muck soils. Turfgrass lawns in muck soils were not different from forests but were different from shrublands. No differences were observed between forests and shrublands. Turfgrass lawns in mineral soils differed significantly from croplands in the mineral soil. No differences were observed between the soil types when nematode data from croplands and turfgrass lawns from each county were lumped together. All habitats showed moderately enriched food webs [enrichment index (EI) 69–78%] in both soil types. Forests, shrublands and turfgrass lawns in muck soils had high maturity (MI) and structure index (SI) whereas grassy borders and turfgrass lawns in mineral soils had intermediate and croplands in both muck and mineral soils had the lowest MI and SI values. Principal component analysis (PCA) indicated that MI and EI contributed the most to differences between habitats in the PC1 while SI and channel index significantly influenced PC2 axis. Forests and shrublands had highly structured food webs as expected but their high enrichment may be unique to the muck soils. Ordination of habitats using nematode indices revealed that grassy borders and turfgrass lawns hold an intermediary position between undisturbed habitats (forests and shrublands) and highly disturbed habitats (croplands). Although overall contribution of plant-parasitic nematode index (PPI) was less compared to the other indices, PPI explained the variation among the habitats in the PC3 axis. Ordination using trophic group abundance showed lower resolution compared to the food web indices.

Information on soil available nutrient (SAN) at key crop growth stages is critical to generate prescription maps for implementing variable rate fertilization (VRF). Our previous study showed that integrating time-series remote sensing (RS) data with the modified World Food Studies (WOFOST) crop model provides a useful approach (preliminary RS-WOFOST-based method) to acquiring information on field SAN; however, the estimation accuracy was low for VRF application. In this paper, three steps were proposed to further improve the SAN estimation accuracy. At the first step, the rapid-nutrient assimilation (RNA) method was used to optimize the crop growth simulation process. Compared with the ensemble Kalman filter (EnKF) method, the RNA method showed an improved performance in estimating soil available nitrogen (N), phosphorus (P), and potassium (K) content [EnKF: R ² = 0.48 (N), 0.37 (P), 0.15 (K); RNA: R ² = 0.59 (N), 0.46 (P), and 0.18 (K)]. The improved K estimation at the first step was clearly lower than that of N and P; hence, the K content estimation was further optimized at the second step and the accuracy was improved (R ² = 0.27) by using the estimated N as an input variable during the K estimation. At the third step, an iteration algorithm was implemented based on the first two steps, and the final R ² = 0.71 (N), 0.58 (P), 0.49 (K); root-mean-square error = 14.35 (N), 3.70 (P), and 14.87 (K). In general, the optimized approach can overcome the limitations of the preliminary RS-WOFOST-based method and improve the SAN estimation accuracy.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R² = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R² = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R² = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R² = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R² = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R² = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R² = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.