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• Existing crop N estimation mainly relies on the correlation of chlorophyll with N. • A comprehensive survey of N-related hyperspectral VIs was made. • Combined use of feature mining method and machine learning was crucial. • Synergy of crop model and hyperspectra could help monitoring of dynamic N status. • Multi-source data integration might reduce uncertainty in crop N status retrieval. Nitrogen (N) is significantly related to crop photosynthetic capacity. Over-and-under-application of N fertilizers not only limits crop productivity but also leads to negative environment impacts. With such a dilemma, a feasible solution is to match N supply with crop needs across time and space. Hyperspectral remote sensing has been gradually regarded as a cost-effective alternative to traditional destructive field sampling and laboratory testing for crop N status determination. Hyperspectral vegetation indices (VIs) and linear nonparametric regression have been the dominant techniques used to estimate crop N status. Machine learning algorithms have gradually exerted advantages in modelling the non-linear relationships between spectral data and crop N. Physically-based methods were rarely used due to the lack of radiative transfer models directly involving N. The existing crop N retrieval methods rely heavily on the relationship between chlorophyll and N. The underlying mechanisms of using protein as a proxy of N and crop protein retrieval from canopy hyperspectral data need further exploration. A comprehensive survey of the existing N-related hyperspectral VIs was made with the aim to provide guidance in VI selection for practical application. The combined use of feature mining and machine learning algorithms was emphasized in the overview. Some feature mining methods applied in the field of classification and chemometrics might be adapted for extracting crop N-related features. The deep learning algorithms need further exploration in crop N status assessment from canopy hyperspectral data. Finally, the major challenges and further development direction in crop N status assessment were discussed. The overview could provide a theoretical and technical support to promote applications of hyperspectral remote sensing in crop N status assessment. [ABSTRACT FROM AUTHOR]

Crop failure detection using UAV images is helpful for precision agriculture, enabling the precision management of failure areas to reduce crop loss. For wheat failure area detection at the seedling stage using UAV images, the commonly used methods are not sufficiently accurate. Thus, herein, a new tool for precision wheat management at the seedling stage is designed. For this purpose, field experiments with two wheat cultivars and four nitrogen (N) treatments were conducted to create different scenarios for the failure area, and multispectral UAV images were acquired at the seedling growth stage. Based on the above data, a new failure detection method was designed by assimilating prior knowledge and a filter analysis strategy and compared with classical filter-based methods and Hough transform-based methods for wheat failure area detection. The results showed that the newly proposed assimilation method had a detection accuracy between 83.86% and 97.67% for different N levels and cultivars. In contrast, the filter-based methods and Hough transform-based methods had detection accuracies between 53.73% and 83.95% and between 20.71% and 75.79%, respectively. Thus, the assimilation method demonstrated the best failure detection performance. • An assimilation method was proposed for wheat failure area detection. • Different methods were compared for wheat failure area detection. • Wheat failure area can be detected using unmanned aerial vehicles imagery. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of crop yield before harvest to allow crop yields management decision-making at a regional scale is crucial for national food policy and security assessments. Modeling dynamic change of crop growth is of great help because it allows researchers to determine crop management strategies for maximizing crop yield. Remote sensing is often used to provide information about important canopy state variables for crop models of large regions. Crop models and remote sensing techniques have been combined and applied in crop yield estimation on a regional scale or worldwide based on the simultaneous development of crop models and remote sensing. Many studies have proposed models for estimating canopy state variables and soil properties based on remote sensing data and assimilating these estimated canopy state variables into crop models. This paper, firstly, summarizes recent developments of crop models, remote sensing technology, and data assimilation methods. Secondly, it compares the advantages and disadvantages of different data assimilation methods (calibration method, forcing method, and updating method) for assimilating remote sensing data into crop models and analyzes the impacts of different error sources on the different parts of the data assimilation chain in detail. Finally, it provides some methods that can be used to reduce the different errors of data assimilation and presents further opportunities and development direction of data assimilation for future studies. This paper presents a detailed overview of the comparative introduction, latest developments and applications of crop models, remote sensing techniques, and data assimilation methods in the growth status monitoring and yield estimation of crops. In particular, it discusses the impacts of different error sources on the different portions of the data assimilation chain in detail and analyzes how to reduce the different errors of data assimilation chain. The literature shows that many new satellite sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. Additionally, new proposed or modified crop models have been reported for improving the simulated canopy state variables and soil properties of crop models. In short, the data assimilation of remote sensing and crop models have the potential to improve the estimation accuracy of canopy state variables, soil properties and yield based on these new technologies and methods in the future. [ABSTRACT FROM AUTHOR]

Coupling remote sensing data with a crop growth model has become an effective tool for estimating grain yields and assessing grain quality. In this study, a data assimilation approach using a particle swarm optimization algorithm was developed to integrate remotely sensed data into the DSSAT-CERES model for estimating the grain yield and protein content of winter wheat. Our results showed that the normalized difference red edge index (NDRE) produced the most accurate selection of spectral indices for estimating canopy N accumulation (CNA), with R 2 and RMSE values of 0.663 and 34.05 kg ha −1 , respectively. A data assimilation method ( R 2 = 0.729 and RMSE = 32.02 kg ha −1 ) performed better than the spectral indices method for estimation of canopy N accumulation. Simulation of grain yield by the data assimilation method agreed well with the measured grain yield, with R 2 and RMSE values of 0.711 and 0.63 t ha −1 , respectively. Estimating grain protein content by gluten type could improve the estimation accuracy, with R 2 and RMSE of 0.519 and 1.53%, respectively. Our study showed that estimating wheat grain yield, and especially quality, could be successfully accomplished by assimilating remotely sensed data into the DSSAT-CERES model. [ABSTRACT FROM AUTHOR]

Timely and accurate forecasting of crop grain protein content (GPC) is helpful in planning to acquire the desired target protein levels. A geographically weighted regression (GWR) model was estimated based on meteorological factors to predict the winter wheat GPC at the county level. In the Huang-Huai-Hai region, the grain protein content of winter wheat increased by 0.29% for every 1° increase in latitude. GPC prediction with this model was more precise than that of the multiple linear regressions (MLR) model. The correlation coefficient (R) and Akaike information criterion (AIC) value ranges were 0.26 ~ 0.66 and 1573.86 ~ 1710.70 for the GWR, and 0.06 ~ 0.46 and 1670.18 ~ 1939.76 for the MLR, respectively. Except for radiation in March (RAD03), radiation in April (RAD04) and radiation in May (RAD05), the sensitivity index of other monthly weather indicators to GPC had a high correlation with latitude. With 36° north latitude (L) as the limit, the correlation between RAD03 (R L<36 ° = 0.36, R L>36 ° = −0.29), RAD04 (R L<36 ° = 0.31, R L>36 ° = −0.35) and RAD05 (R L<36 ° = 0.20, R L>36 ° = −0.20) with latitude all showed an opposite trend. We highlight that spatial information needs to be considered when predicting county-level winter wheat GPC. • The grain protein content (GPC) is positively correlated with latitude. • The effect of meteorological factors on winter GPC showed spatial heterogeneity. • Geographically weighted model has advantages in winter GPC in heterogeneous region. [ABSTRACT FROM AUTHOR]