Drought monitoring is an important scientific basis for drought impact evaluation and the selection of mitigation strategies. Since the drivers of drought vary among regions, there is no universal drought index applicable to different regions. The Shiyang River Basin, an inland river basin, located in Gansu Province, China, has a closed water cycle system. Drought is a dominant nature disaster for the sustainable development of the region. Thus, this is an ideal area to explore the suitability of drought-monitoring indices. Here, we took the Shiyang River Basin as an example, in order to explore suitable indicators for agricultural drought monitoring in inland river basins. This study assessed the twelve different widely used drought indices used for monitoring the impact of drought on crop growth, represented by net primary production (NPP). The results showed that the vegetation status-based drought indices (VCI and NVSWI) had the highest significant correlation (0.6 ≤ |R| ≤ 1, p < 0.05) on NPP, and the integrated drought indices (DSI and ISDI) had the strong significant correlation (0.4 ≤ |R| < 0.6, p < 0.1). These four indices are good indicators for agricultural drought monitoring. Studies based on these four indices showed that agricultural drought has a tendency to slow down from 1982 to 2020. This is inconsistent with the monitoring of drought indices based on the meteorological variables that show a trend of increasing drought. This is mainly due to the increased efficiency of water management and its use in inland river basins. This indicates that other water resource information, such as runoff, should be included to construct an integrated agricultural drought-monitoring indices in management intensive regions, such as in an inland river basin.

Structure from Motion (4D-SfM) photogrammetry can capture the changes in surface processes with high spatial and temporal resolution, which is widely used to quantify the dynamic change process of the ground surface. However, the low accuracy and uncertainty of the reconstructed digital elevation models (DEM) with current 4D-SfM photogrammetry hinder its application due to the simple survey pattern with multiple cameras. Hence, this study aims to develop a single-camera-based 4D-SfM photogrammetry device and adopt the “lawn-mower’ survey pattern zigzagging over a 4 × 4 m bare slope to improve the accuracy and stability of reconstructed DEM. Four different image network geometries were generated based on the zigzag-based survey pattern. Two processing settings for Agisoft PhotoScan Pro were tested to reconstruct the 4D-SfM model. In total, we achieved eight different 4D models over a bare slope over a month-long period. The differences, stability and accuracy of eight models were analyzed. The results of the study showed that the different image network geometry and processing settings resulted in significant differences among the eight models of 4D data sequences. Among them, the image network geometry has the greatest influence on the accuracy of 4D data, and the different processing settings cause the least difference for the zigzag image network geometry with a large number of photos. The 49-ultra-high model could achieve submillimeter scale precision and its relative accuracy is superior to most of previous studies. The results of the above study show that the zigzag image network geometry can greatly improve the accuracy and stability of ground-based 4D-SfM photogrammetry.