Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security.

Conducting urbanization monitoring at key nodes of the Mongolian Plateau holds significant importance for evaluating the economic and social development of cities within the China–Mongolia–Russia transportation corridor. These cities are sparsely distributed in the vast grassland areas, posing challenges for detecting urban changes in the transboundary regions. This study proposes a method for characterizing urban development supported by multi-source remote sensing data, including nighttime light (NTL) data with superior resolution, focusing on 11 major cities along the China-Mongolia-Russia Economic Corridor within the Mongolian Plateau. A City Development Index (CDI) is introduced, utilizing “information entropy” with the entropy weight method. The development status of 11 cities in the study area was analyzed using fractal dimensions, compactness, index of economy, index of social development, and the CDI. Results show Baotou has the highest CDI (0.82), followed by Hohhot (0.77), Ordos (0.73), and Ulanqab (0.66). In contrast, Mongolian cities exhibit significantly lower CDIs, relatively. The capital, Ulaanbaatar, has a CDI of 0.63, while no other Mongolian city exceeds 0.60, correlating with scattered populations. The results indicate that Inner Mongolia and Mongolia exhibit different geographical features in urban development. This approach provides a quantitative method for detecting and assessing urban development of the arid and semi-arid regions using NTL satellite data.