Water is a crucial resource for agricultural development in the Yellow River Basin. However, the effects of water shortages on the region’s agricultural development are becoming increasingly evident, creating a need to examine the relationship between agricultural water use (AWU) and the agricultural economy. This study uses panel vector autoregression to analyze the relationship between AWU and the agricultural economy in the Inner Mongolia section of the Yellow River Basin from 1998 to 2018. The results indicate the following: (1) AWU in the Inner Mongolia section of the Yellow River Basin significantly declined during the study period, showing clear differences in the AWU’s effectiveness among regions; (2) agriculture in the region stabilized after significant growth, and the share of primary-sector industries in the national economy also stabilized after significant decline; (3) in the long run, AWU and the agricultural economy become cointegrated with the AWU Granger-causing agricultural economy. By deepening our understanding of agricultural water demand in the Yellow River Basin, these findings provide theoretical justification for establishing water-conserving irrigation systems and making sustainable use of water resources.

Vegetation phenology is one of the most sensitive indicators to understanding terrestrial ecosystem status and change. However, few studies have been conducted to reveal vegetation phenology variation characteristics over the past two decades, especially under the background of the global warming hiatus since 1998. The agricultural pastoral ecotone of northern China (APENC) is an ideal place to analyze land surface phenology (LSP) variation. Therefore, the spatiotemporal patterns of LSP were quantitatively analyzed at regional, basin and pixel scales based on time-series MODIS NDVI data (2001–2021). Results showed that: (i) The start of the growing season (SOS) occurred in 105–141 Julian days, the end of the growing season (EOS) was between 257 and 281 Julian days and the length of the growing season (LOS) varied from 130 to 172 days. The later SOS was mainly distributed in croplands and typical grassland areas, while the early SOS was observed in forests and sandy vegetation coverage areas. The early EOS occurred in typical grasslands, and the later EOS was concentrated in the southeast boundary. The magnitude of the SOS and LOS fluctuation was less than EOS. (ii) The SOS and EOS exhibited overall insignificant advanced and delayed trends at a rate of −0.09 days·yr⁻¹ and 0.12 days·yr⁻¹, respectively, and the LOS displayed an insignificant extended trend at a rate of 0.26 days·yr⁻¹ at a regional scale. The trends of phenological metrics were consistent with the APENC in the Yellow River and Haihe River Basins. The shortened trend of LOS occurred due to the delayed SOS and advanced EOS in the Songliao River and Continental Basins. (iii) The SOS variation gradually changed from an advanced trend to a delayed trend from a southwest to northeast direction in cropland and grassland ecosystems, whereas an opposite trend was found for EOS. The LOS exhibited a significant extended trend due to the significant advanced and delayed trend of SOS and EOS at p < 0.01 in forest ecosystems. This work provides a critical reference for the vegetation phenology dynamic research of semi-arid and semi-humid regions.

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and analysis platform, was used to realize the combination of Landsat TM/OLI data images with spectral features and topographic features, and the random forest machine learning classification method was used to supervise and classify the low-cloud composite image data of Ordos City. The results show that: (1) GEE has a powerful computing function, which can realize efficient and high-precision in-depth analysis of long-term multi-temporal remote sensing images and monitoring of land use change, and the accuracy of acquisition can reach 87%. Compared with other data sets in the same period, the overall and local classification results are more distinct than ESRI (Environmental Systems Research Institute) and GlobeLand 30 data products. Slightly lower than the Institute of Aerospace Information Innovation of the Chinese Academy of Sciences to obtain global 30 m of land cover fine classification products. (2) The overall accuracy of the land cover data of Ordos City from 2003 to 2023 is between 79–87%, and the Kappa coefficient is between 0.79–0.84. (3) Climate, terrain, population and other interactive factors combined with socio-economic population data and national and local policies are the main factors affecting land use change between 2003 and 2023.