Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

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Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

We present a panel dataset of COVID-19 vaccine policies, with data from 01 January 2020 for 185 countries and a number of subnational jurisdictions, reporting on vaccination prioritization plans, eligibility and availability, cost to the individual and mandatory vaccination policies. For each of these indicators, we recorded who is targeted by a policy using 52 standardized categories. These indicators document a detailed picture of the unprecedented scale of international COVID-19 vaccination rollout and strategy, indicating which countries prioritized and vaccinated which groups, when and in what order. We highlight key descriptive findings from these data to demonstrate uses for the data and to encourage researchers and policymakers in future research and vaccination planning. Numerous patterns and trends begin to emerge. For example: ‘eliminator’ countries (those that aimed to prevent virus entry into the country and community transmission) tended to prioritize border workers and economic sectors, while ‘mitigator’ countries (those that aimed to reduce the impact of community transmission) tended to prioritize the elderly and healthcare sectors for the first COVID-19 vaccinations; high-income countries published prioritization plans and began vaccinations earlier than low- and middle-income countries. Fifty-five countries were found to have implemented at least one policy of mandatory vaccination. We also demonstrate the value of combining this data with vaccination uptake rates, vaccine supply and demand data, and with further COVID-19 epidemiological data.

The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R2 = 0.73–0.85, nRMSE = 0.17–0.23; MS: R2 = 0.77–0.84, nRMSE = 0.17–0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R2 = 0.67–0.88, nRMSE = 0.15–0.25; MS: R2 = 0.60–0.77, nRMSE = 0.19–0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping.

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