Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m ⁻² · yr ⁻¹ , equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to: (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy and good stability. The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3–R6 stages were 0.83–0.44, −0.22 to −5.23, 0.42–0.35, 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.

The effects of meteorological factors on reference evapotranspiration (ET₀) are variable on different time scales, although research tends to focus only on certain time scales. Therefore, using the meteorological data from 1958 to 2017 of Beijing, China, ET₀ values over the last 60 years were calculated using Penman–Monteith method. The variation in ET₀ values was thus analyzed against four meteorological factors over different time scales. The sensitivity of ET₀ to these factors was assessed using a sensitivity coefficient, while the contribution of each factor to ET₀ change was quantified by combining this sensitivity coefficient with the factor’s relative change rate over multiple time scales. The results showed that the sensitivity coefficient of relative humidity over different time scales were all negative, while the sensitivity coefficients of net radiation, temperature and wind speed were mostly positive. The main sensitivity factors of ET₀ on different time scales varied. On annual time scales, the main factors were relative humidity and temperature. Over annual time scales, relative humidity and net radiation alternated as the main sensitivity factor; while over interannual time scales, the most sensitive factor was relative humidity during 1958–1979 and net radiation thereafter. The contribution of these four meteorological factors to ET₀ also fluctuated greatly on intra-annual time scales. On daily time scales, the contributions of temperature and wind speed at the start and end of the year were large, while net radiation and relative humidity were dominant mid-year. On monthly to seasonal time scales, the contributions of these four meteorological factors to ET₀ were notable. The contribution of relative humidity was largest in spring and autumn; net radiation was dominant in summer, while temperature and wind speed were dominant in winter. This research on the temporal variability of ET₀ response factors is of great significance for understanding regional climate change.