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Water-flooding reservoirs are the mainstay in developing mature oilfields in eastern China， and they have entered the ultra-high water cut stage as a whole after long-term water injection development. Laboratory studies and field practice show that there is still potential for significantly enhancing oil recovery. However， the difficulty of scientific and efficient development has increased due to the heterogeneity of reservoirs and development dynamics， and further research is needed to enhance oil recovery technology. Uncompartmentalized reservoirs face challenges such as severe extreme water consumption in the later stage of ultra-high water cut， affecting stable production and enhanced oil recovery through water flooding. Therefore， the theory of high water consumption zones was deepened， and the development technology of near-resistance layer recombination， the vector development technology of ultra-high water cut reservoirs， and the development technology of flow field adjustment in the later stage of ultra-high water cut were developed through innovation， achieving water consumption control and reduction， with an average oil recovery of 43.7%. In response to the characteristics of fault block reservoirs with numerous faults and small fault blocks， as well as the challenges of small remaining oil reserves and difficult exploitation during the ultra-high water-cut stage， understanding of the accumulation patterns of remaining oil and hydrodynamic regulation mechanisms was deepened. A series of leading development technologies were developed through innovation， such as artificial peripheral water flooding and stereoscopic development， improving reserve control and water flooding efficiency， with an average oil recovery of 29.4%. These technologies have supported the stable and profitable production of fault block reservoirs. Given the poor physical properties of low-permeability reservoirs and the difficulty of effective water flooding， nonlinear flow mechanisms and pressure flooding development mechanisms were researched， supporting development technologies were formed， and the development limits were constantly broadened. A series of differential development technologies have been innovated， such as water injection of simulated horizontal wells in ultra-low permeability reservoirs， well pattern adaptation for enhanced oil recovery in general low-permeability reservoirs， and pressure flooding in low-permeability and tight reservoirs， with an average oil recovery of 15.7%， realizing rapid and efficient development of low-permeability reservoirs. Through the above technical research， the efficient development of water-flooding reservoirs in ultra-high water cut stage is realized. According to the development characteristics and situation of different types of reservoirs， the directions for further enhancing oil recovery of water-flooding reservoirs are being put forward.

Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions. [ABSTRACT FROM AUTHOR]

Satellite hyperspectral imagery is an important data source for large-scale refined land cover classification and mapping, but the high spatial heterogeneity and spectral variability at low spatial resolution and the high computation cost for massive data remain challenges in the research community. In recent years, convolutional neural network (CNN) models with the capability for feature extraction have been widely used in hyperspectral image classification. However, incomplete feature extraction, inappropriate feature fusion, and high time consumption are still the major problems for CNN applications in large-scale fine land cover mapping. In this study, a Spatial-Convolution Spectral-Transformer Interactive Network (SCSTIN) was proposed to integrate 2D-CNN and Transformer into a dual-branch network to enhance feature extraction capabilities by exploring spatial context information and spectral sequence signatures in a targeted manner. In addition, spatial-spectral interactive fusion (SSIF) units and category-adaptive weighting (CAW) as two feature fusion modules were also adopted between and after the two feature extraction branches to improve efficiency in feature fusion. The ZY1-02D hyperspectral imagery was collected to conduct the experiments in the study area of the eastern foothills of the Helan Mountains (EFHLM), covering an area of about 8800 km2, which is the largest hyperspectral dataset as far as we know. To explore the potential of the proposed network in terms of accuracy and efficiency, SCSTIN models with different depths (SCSTIN-4 and SCSTIN-2) were performed. The results suggest that compared with the previous eight advanced hyperspectral image classifiers, both SCSTIN models achieved satisfactory performance in accuracy and efficiency aspects with low complexity, where SCSTIN-4 achieved the highest accuracy and SCSTIN-2 obtained higher efficiency. Accordingly, the SCSTIN models are reliable for large-scale fast refined land cover classification and mapping. In addition, the spatial distribution pattern of diverse ground objects in EFHLM is also analyzed. [ABSTRACT FROM AUTHOR]