Understanding the spatial–temporal distribution of photosynthetically active radiation (PAR) within crop canopies is crucial for optimizing planting structures to enhance resource use efficiency and improve crop yields. While high planting densities are commonly employed to increase yields, this practice can lead to issues such as early leaf senescence and reduced biomass. This study investigates the impact of varying planting densities on PAR dynamics, canopy structure, and yield formation in maize over two years. Key findings include the following: (1) higher planting density significantly increased grain yield, biological yield, and LAI, although HI decreased; (2) canopy light distribution varied with planting density, with the middle layers intercepting the most light, particularly during the grain filling stage; (3) a density of 83,000 plants·ha⁻¹ was the most efficient for maximizing yield and WUE, although high accumulated temperatures negatively impacted yields. These results suggest that adjusting planting density can enhance resource use efficiency in maize farming, particularly in regions with variable water availability and climate challenges. Future research should explore the long-term effects of planting density on soil health, water use efficiency, and crop resilience under varying environmental conditions. Additionally, studies integrating precision agriculture technologies to fine-tune planting density and water management in response to climate change are essential for ensuring sustainable maize production and food security in the future.

The timely and accurate estimation of soil water content (SWC) and evapotranspiration (ET) is of great significance in drought estimation, irrigation management, and water resources comprehensive utilization. The unsupervised classification was used to identify the crops in the region. Based on MOD16A2 and the meteorological data, a SEBS model was used to estimate the ET in the Jiefangzha Irrigation Field from 2011 to 2015. Based on the crop water stress index (CWSI), the SWC in 2014 was retrieved and verified with the measured SWC on different underlying surfaces (sunflower, corn, wheat, and pepper). The results showed that: (1) The positional accuracy of maize, sunflower, wheat, and pepper are 0.81, 0.80, 0.90, and 0.82, respectively; (2) The annual ET from 2011 to 2015 presented well the spatial distribution of the ET within the field; (3) The validation results of the estimated SWC on the underlying surface of wheat and sunflower showed a good robustness, the R² was 0.748 and 0.357, respectively, the RMSE was 2.61% and 2.309%, respectively, and the MAE was 2.249% and 1.975%, respectively. However, for maize and pepper with more irrigation times, the SWC estimation results, based on the CWSI were poor, indicating that the method was more sensitive to soil drought and suitable for the crop SWC estimation with less irrigation and drought tolerance. The results can provide a reference for the agricultural water resources management and the irrigation forecast at a regional scale.

Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R²) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R² of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts.

Climate change has led to significant changes in water and heat conditions in crop production areas, which have in turn affected the spring maize growth and yield. This study analyzed the spatiotemporal variation characteristics of the water and heat conditions, such as the growth degree-day (GDD), killing degree-day (KDD), sunshine hours (SD), effective precipitation (Pe), and irrigation water requirement (IR), of spring maize in Inner Mongolia based on data from 50 meteorological stations. The relationship between hydrothermal conditions and yield was revealed using methods that included stepwise regression analysis. The results showed that the GDD during the spring maize growth period ranged from 513 to 2011 °C, with high-value areas concentrated in western and southeastern regions of Inner Mongolia. The GDD showed an increasing trend during all the growth periods. High-KDD areas were mainly distributed in the Alxa League region in western Inner Mongolia, and the KDD showed an increasing trend during all periods except the rapid fertility period. The spatial distribution of the SD was consistent with that of the GDD, and the SD values for each reproductive period showed a decreasing trend. The average Pe and IR values in the last 60 years were 111 mm and 386 mm, respectively, and showed opposite spatial distribution trends. The overall Pe trend was decreasing and that of the IR was increasing, which will aggravate future water resource consumption in the region. Stepwise regression analysis showed that the Pe was the main factor affecting the spring maize yield. Overall, the spring maize fertility period in various regions of Inner Mongolia was extremely uneven in terms of the hydrothermal conditions. This study provides a basis for the regional spatial spring maize layout and the sustainable use of water and heat resources in the region.

Drought is a major meteorological disaster that affects the growth of spring maize in Inner Mongolia. Understanding the spatiotemporal characteristics of drought is crucial for maintaining crop production. This study categorized Inner Mongolia into five climatic regions based on geography and climate characteristics: hyper-arid, arid, semi-arid, dry and semi-humid, and moist and semi-humid, reflecting Regions I to V, respectively. Based on data collected from 107 meteorological stations, the standardized precipitation evapotranspiration index (SPEI) was used to assess the characteristics of drought occurrence in different climatic regions during the spring maize growing season over the past 60 years, as well as the impact of drought on yield. SPEI decreased across all regions during growth periods, with mid-growth stages showing the smallest decline and a slight slowdown in drought severity. Spatially, 15 stations showed reduced drought during the early growth stage, which increased to 20 stations during the mid-growth stage. The overall drought trend of intensification was obtained across various climate regions, with Region I showing the most severe drought. The drought frequency during the growth period of spring maize ranged from 20% to 42%, and the drought frequency decreased from light drought, moderate drought, and severe drought to extreme drought. The drought range in each growth stage increased, with localized and regional drought dominating the region. The drought intensity also increased. Precipitation is the main factor affecting spring maize yield, and water replenishment during the early and middle stages of growth is crucial. These findings provide a basis for efficient water resource management in the region.