Spatiotemporal evolution of agricultural drought and its attribution under different climate zones and vegetation types in the Yellow River Basin of China.

Ecological protection and high-quality development of the Yellow River Basin (YRB) are major national strategies in China. Agricultural drought (AD) is one of the most important stress factors of the ecological security of the YRB. Currently, there is a lack of exploration of the spatiotemporal evolution of AD in the YRB under different climatic zones and vegetation types, and the mechanisms by the driving factors influence AD remain unclear. The Temperature Vegetation Dryness Index (TVDI) for the YRB in China during 2000-2020 was calculated using Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). We analyzed the spatiotemporal evolution of AD from the perspective of upstream of the YRB (UYRB), midstream of the YRB (MYRB), and downstream of the YRB (DYRB), as well as different climate zones and vegetation types. The driving factors were selected based on the Pearson correlation analysis, Geographical detector, and Mantel test. Structural equation modeling (SEM) was employed to quantify the direct and indirect effects of the driving factors on AD in the YRB. We found a slowing trend of AD in the YRB, mainly in the Loess Plateau, which is distributed in UYRB and MYRB, but an increasing trend for AD in DYRB. Temperature, which is the most direct influential factor, has exacerbated AD in UYRB and MYRB. However, surface solar radiation (SSR) has the greatest constraining effect on DYRB. AD increased in arid and desert zones, while a decreasing trend is observed for other climatic zones and vegetation types. In arid and semiarid zones, human activities and SSR were the largest indirect factors exacerbating AD. In humid and subhumid zones, the largest indirect factor exacerbating AD was potential evapotranspiration (PET). Temperature is the most direct factor exacerbating AD in cropland and forest, while PET is the largest indirect factor exacerbating AD in grassland. This study elucidates the driving factors and mechanisms of AD in the YRB to provide scientific decision support for mitigating regional drought and promoting regional sustainable development.

An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN.

Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram's spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method's applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.

Effects and contributions of meteorological drought on agricultural drought under different climatic zones and vegetation types in Northwest China.

Meteorological drought is one of the driving forces behind agricultural drought. The response of agricultural drought to meteorological drought remains poorly understood under different climatic zones and vegetation types in Northwest China (NWC). Furthermore, the contribution of climate factors and human activities to agricultural drought in NWC remains unclear. We combined the Standardized Precipitation Evapotranspiration Index (SPEI) and the satellite Vegetation Condition Index (VCI) to characterize meteorological and agricultural drought, respectively. Based on the trend analysis, Spearman's correlation coefficient and residual trend analysis, we studied the variation characteristics and response relationships of meteorological and agricultural drought under different climatic zones and vegetation types in NWC from 2000 to 2019 and evaluated the contributions of climate factors (SPEI and precipitation) and human activities on the agricultural drought. The results showed that under different climatic zones and vegetation types, the SPEI and VCI all showed an upward trend in NWC, indicating that meteorological and agricultural drought slowed down. It was further pointed out that the climate was humidified and the soil moisture increased in NWC. Meteorological drought has a definite effect on agricultural drought, and the effect varied non-linearly along the drought gradient with the strongest responses in the semiarid ecosystems. Drought resistance of different climatic zones and vegetation types was different, caused by the specific sensitivity and uniqueness of local arid environment. Among them, grasslands dominated the regional SPEI-VCI changes in NWC. The combined effects of climatic factors (SPEI and precipitation) and human activities promoted the variation of agricultural drought in NWC. Climatic factors were the main drivers of agricultural drought change in grasslands, with the contribution rate reaching 76.71%. However, human activities all contributed significantly to agricultural drought than climatic factors, especially in the Loess Plateau, Junggar Basin and northern Tianshan Mountains, where the positive contribution of human activities exceeded 80%. Thus, the SPEI and VCI can effectively reveal the change law of meteorological drought and agricultural drought in NWC. This study provides a theoretical basis for drought disaster relationship assessment.