Drought and floods affect the growth and yield of maize, affecting food security. Therefore, it is crucial to assess maize’s drought and waterlogging status in various growth stages. We used phenological and daily meteorological data and spatial analysis to identify the drought and waterlogging conditions of spring maize in Northeast China in eight growth stages. We calculated the crop water surplus/deficit index and used the national standard for maize drought and waterlogging. The results indicate a significant decreasing trend of effective precipitation in Northeast China. The maize’s water requirements changed during the growing period. The ranking of the daily water requirements of maize from high to low in the different growth stages was the flowering stage to the silking stage (6.9 mm/d), the tasseling stage to the flowering stage (6.1 mm/d), the jointing stage to the tasseling stage (4.9 mm/d), the seven-leaf stage to the jointing stage (3.4 mm/d), the silking stage to the harvesting stage (2.0 mm/d), the emergence stage to the three-leaf stage (1.4 mm/d), the three-leaf stage to the seven-leaf stage (1.3 mm/d), and the sowing stage to the emergence stage (1.2 mm/d). Drought occurred primarily in the early growth and development stage, and the most severe drought conditions were observed in the sowing to emergence stages and the emergence to the three-leaf stages in most areas in Northeast China. Waterlogging occurred predominantly in the flowering to the silking stages and the silking to the maturity stages in southeast Liaoning and parts of Jilin. Inner Mongolia had the lowest soil moisture conditions and was unsuitable for maize growth, followed by Heilongjiang, Jilin, and Liaoning. The dominant meteorological factors affecting the drought and waterlogging status of maize in different growth stages were precipitation and wind speed, followed by the minimum temperature, relative humidity, sunshine hours, and maximum temperature. The average temperature did not influence the drought and waterlogging status. The results provide a basis for selecting drought-resistant varieties and preventing waterlogging.

Detailed traits are required for early warning and prediction of crop-related meteorological hazards. Currently, data sets describing maize phenological traits in Northeast China are few and incomplete, resulting in poor spatial interpolation results that do not accurately reflect the spatial distributions and temporal development patterns of maize phenology in the region. In this study, a maize-phenology data set is produced containing nine phenological stages and phenological stage maps based on three sets of in situ maize-phenology data from three different sources. First, the relationship between each phenological stage and date of the previous stage, longitude, latitude, and altitude, is uncovered using a multiple stepwise regression method. Then, the spatial variation of each phenological stage using ArcGIS is explored. Finally, a maize phenological stage data set and a phenological stage atlas are established for the average state of 2010–2020 in Northeast China. The data set was validated using phenological data from agricultural weather stations run by the China Meteorological Administration. The validated data set can be used for various purposes, including real-time warning and prediction of maize-related meteorological hazards.

The maximum surface relative humidity (h_s,max) is a key factor that affects surface vapor pressure (e_s), which governs evapotranspiration (ET) rates. Existing empirical parameterizations lack a robust physical basis and exhibit limited generalizability. To address these limitations, we propose a remote sensing–based framework for the first global-scale estimation of h_s,max, which integrates physical constraints and data-driven methods. The framework first back-calculated h_s,max by leveraging coupling mechanisms in the surface energy balance equation, using ET and meteorological data from 195 global flux tower sites. Subsequently, XGBoost–based inversion models were constructed separately for different land surface types, with site-level meteorological variables and the back-calculated h_s,max as inputs. The models were applied to global remote sensing and reanalysis datasets, enabling the global-scale estimation of monthly h_s,max from 2001 to 2020. Results demonstrate that the h_s,max models for different land surface types achieved high accuracy, with a mean root mean square error (RMSE) of 0.079 and a correlation coefficient (R) of 0.92. A contribution analysis using SHAP (Shapley Additive Explanations) reveals that relative humidity (RH) is the dominant predictor, while secondary factors vary by land surface type. The global monthly h_s,max estimates for 2001–2020 exhibit distinct climate-driven spatial patterns. Temporal variability is low (CV < 10%) in humid regions but exceeds 20% in arid regions. In conclusion, the proposed parameterization framework shows both the physical consistency and high accuracy in global scale h_s,max estimation, providing essential parameter support for studies of land–atmosphere interactions.

Evapotranspiration (ET) plays a crucial role in the regional water-energy cycle, illustrating intricate interactions among climate, vegetation and soil. Eddy covariance (EC) technology is a primary method for measuring ET. However, incomplete data due to adverse weather conditions and equipment malfunctions are common. This study utilizes the full-factorial method to address the ET gaps at 339 sites across multiple global flux networks, and the filled ET data are compared with three ET products: the Land component of the Fifth Generation of European Reanalysis (EAR5-Land), Global Land Evaporation Amsterdam Model (GLEAM), and Breathing Earth System Simulator (BESS). Results indicate high consistency between filled ET data and three ET products at 264 out of 339 sites. The absolute average mean error (|MAE|) and root mean system error (|RMSE|) are 0.32 mm/d and 0.92 mm/d, respectively. Among the remaining 75 sites, 49 sites exhibit better agreement between filled ET and measured ET data than ET products in terms of seasonal variations and numerical ranges. Further verification is needed for the reliability of filled ET data at the other 26 sites due to a scarcity of measured ET data. Overall, the gap-filled ET data from 313 sites (2210 site-years) demonstrate high-quality. These sites exhibit a strong correlation between available energy and turbulent fluxes, with R², MAE, and RMSE for different surface types ranging from 0.84 to 0.94, 21.49 to 28.67 W/m², and 28.37 to 36.91 W/m², respectively. And the average energy balance closure rate is 0.73, indicating a relatively high degree of closure in the energy balance. These 313 sites with high-quality filled ET data can be utilized for ET model validation, ET product verification, water demand assessment, and other related tasks. The filled ET dataset can be publicly accessed at https://doi.org/10.57760/sciencedb.11651 (Wang & Jiang, 2024).