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【Objective】 Tracking the change in cultivation areas of different crops in a basin or catchment is essential for agricultural management but challenging. The objective of this paper is to investigate the feasibility of threshold feature - random forest classification model for extracting crop structure from remote sensing imageries of irrigation district. We took Qingtongxia irrigation as an example. 【Method】 Using field survey and visual interpretation, the threshold feature-random forest classification method in the Google Earth Engine platform was used to delineate the planting areas of spring wheat, spring maize, and rice within the Qingtongxia irrigation district from 2013 to 2020. 【Result】 The threshold feature-random forest classification method is effective for extracting planting structure in the Qingtongxia irrigation district in the seven years, with an overall accuracy of 0.88 and a Kappa coefficient of 0.76. The linear fitting between the extracted areas from the remote sense images and areas obtained from survey gave a R2 which was 0.80, 0.93 and 0.86, for spring wheat, spring maize and rice, respectively. Data analysis revealed that the planting area of spring maize surpassed that of rice and spring wheat in 2013 to 2020. Spring maize cultivation had notably increased, while spring wheat cultivation initially increased and then declined; rice cultivation also decreased. Spring maize was mainly grown in the North and South of the irrigation district, while the overall irrigation area increased from the North to the South. Spring wheat was primarily cultivated in central and Southern parts of the irrigation district, and rice was in the central region. Spring wheat and rice cultivation areas increased from the South to the North. 【Conclusion】 The threshold feature-random forest classification model is effective and accurate for extracting annual change in planting structures in irrigation districts in arid regions. It is novel and perspective for understanding long-term evolution in planting structure in a region.

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin. [ABSTRACT FROM AUTHOR]

Porous carbon is one of the favorable materials for electrodes in supercapacitors owing to its huge particular area of the surface, high intensity, and stable characteristics of chemistry. The metal–organic framework [Zn (NH2-bdc) (4,4′-bpy)] was synthesized using the hydrothermal method. The porous carbon ZNBC-T-A was synthesized through the chemical activation of ZnCl2. Further, whether the micromorphology and electrochemical characteristics of porous carbon ZNBC-T-A are related to the carbonization temperature and ZnCl2 activator ratio was investigated. The results showed that the porous carbon ZNBC-700-1:3 had a hive-like architecture with a particular area of the surface of 2078 m2 g−1. The activator ZnCl2 entered the pores of the precursor as a template, which prevented the pores from collapsing. This template served as a template agent. When the current density was 1.0 A g−1, the porous carbon ZNBC-700-1:3 exhibited a specific capacitance of 210.6 F g−1. The retention of capacitance was 90.5% after 5000 repetitions, while coulomb efficiency was kept at nearly 100%. [ABSTRACT FROM AUTHOR]