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Droughts present substantial challenges to agriculture, food security, and water resources. Employing a drought index based on soil moisture dynamics is a common and effective approach for agricultural drought monitoring. However, the precision of a drought index heavily relies on accurate soil moisture and soil hydraulic parameters. This study leverages remote sensing soil moisture data from the Climate Change Initiative (CCI) series products and model-generated soil moisture data from the Variable Infiltration Capacity (VIC) model. The extended triple collocation (ETC) method was applied to merge these datasets from 1992 to 2018, resulting in enhanced accuracy by 28% and 15% compared to the CCI and VIC soil moisture, respectively. Furthermore, this research establishes field capacity and a wilting point map using multiple soil datasets and pedotransfer functions, facilitating the development of an enhanced Soil Water Deficit Index (SWDI) based on merged soil moisture, field capacity, and wilting points. The findings reveal that the proposed enhanced SWDI achieves a higher accuracy in detecting agricultural drought events (probability of detection = 0.98) and quantifying their severity (matching index = 0.33) compared to an SWDI based on other soil moisture products. Moreover, the enhanced SWDI exhibits superior performance in representing drought-affected crop areas (correlation coefficient = 0.88), outperforming traditional drought indexes such as the Standardized Precipitation Index (correlation coefficient = 0.51), the Soil Moisture Anomaly Percent Index (correlation coefficient = 0.81), and the Soil Moisture Index (correlation coefficient = 0.83). The enhanced SWDI effectively captures the spatiotemporal dynamics of a drought, supporting more accurate agricultural drought monitoring and management strategies. [ABSTRACT FROM AUTHOR]

Vision-based automatic noise-barrier inspection of high-speed railway, instead of manual patrol, remains a great challenge. Even though many supervised learning-based methods have been developed, massive redundant video frames and scarce defective samples are the main obstacles to leverage the performance of the noise-barrier inspection task. To tackle the problems, we present a novel Vision-based Noise-barrier Inspection System (VNIS), which is deployed on the bullet train to inspect the noise-barrier defects by using motion video. VNIS uses the proposed panorama generation model based on motion video to obtain panoramic images from massive redundant video sequences. Then, we employ a self-supervised learning deep network to solve the problem of the scarce defective samples. Comprehensive experiments are conducted on a large-scale video dataset of bullet train. VNIS yields competitive performance on noise-barrier defects inspection. Specifically, an average accuracy of 99.14% is achieved for noise-barrier defects inspection. [ABSTRACT FROM AUTHOR]