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High-precision mapping of agricultural crops in complex planting areas is a prerequisite for precision agricultural management. This paper first proposes a novel multi-task neural network, Multi-task Multi-Scale Convolutional Pooling Unet (MSCPUnet), for extracting vector plots. MSCPUnet is based on the Unet model and enhances performance through the incorporation of attention mechanisms, multi-scale pooling layers, and a multi-task learning approach for parallel processing. A ZiYuan-1 02D (ZY1E) satellite image collected from the He-Tao irrigation district in China is selected for plot extraction experiments, where MSCPUnet is compared with three other deep learning network variants. The Inter section over Union (IoU) and Accuracy metrics for the MSCPUnet model achieve the highest values, converging at 0.837 and 0.928, respectively. Leveraging this capability, a crop classification framework is proposed, which first extracts crop attributes from Sentinel-2 (S2) time series data. The plot information obtained from the MSCPUnet model is then combined with the area dominance method to assign crop attributes to vector plots, facilitating crop structure identification at the plot scale. Results indicate that this method significantly improves classification accuracy for fragmented farmlands, with overall accuracy (OA) rising to 91 % and Kappa coefficient increasing to 0.84 compared to a random forest classifier. This integrated crop classification approach has been validated in this study for high-precision crop mapping in complex planting areas.

Vegetation carbon sequestration is a fundamental process that supports ecosystem biodiversity and ecological services. It is a key factor in shaping ecosystem state and energy flow. Global climate change has intensified in recent years. Frequent drought events affect the stabilization of carbon cycle. In this study, we used correlation analysis method to explore the relationship between standardized precipitation evapotranspiration index (SPEI) and gross primary productivity (GPP). Our study found that the global drought degree is decreasing, and drought sensitivity of global surface vegetation decreased. The drought index value increased 91.3% and the sensitivity decreased 35.71% during the 2010–2020 period (P2) compared to the 2000–2010 period (P1). Our study also found that the global area of drought decreased by 4.03% in P2, but the global area with high drought frequency increased by 0.21%. The drought response time scale shortened by 5.19%. GPP showed an increasing trend, with the largest increase in agricultural land. By studying the interaction between drought and different vegetation types, we can better understand the mechanisms by which vegetation responds, adapts and regulates to climate change. It is necessary for understanding the sustainable development of global ecosystems and climate change response.

Study region: Inner Mongolia Autonomous Region, North China Study focus: Sensible heat flux (H) quantifies the intensity of water evapotranspiration, making the factors influencing H pivotal to water consumption. North China's semi-arid grasslands are characterized by widespread grazing and strong wind conditions. Grazing strategy modifies surface air heat transfer resistance via vegetation and consequently alters H. Nevertheless, the majority of studies overlook the impact of the observed variables' time scale on H estimation. This paper employs a ''big-leaf'' model to estimate H, incorporating both grazing management strategy and data time scale, validated using the Bowen ratio, and attempted to analyze the factors that affect H. New hydrological insights for the region: Aerodynamic characteristics estimated from daily data typically exceed estimates derived from hourly data. The influence of surface radiative temperature and air temperature on H outweighs that of aerodynamic resistance, whereas aerodynamic resistance accounts for the disparity in estimating H between daily and hourly data. In grazing-prohibited grasslands, the daily H values estimated by the Big Leaf model closely match those obtained from the Bowen ratio. When estimating the hourly H value for grazing prohibited and grazing grasslands, the increase was 25.95 w/m2 and 52.27 w/m2 compared to the daily scale, respectively. For the estimation of water and heat fluxes across different regions, the temporal scale of input data is a pivotal factor.