This study proposes a cuboid model for soil moisture assessment. In the model, the three edges were the meteorological, soil, and vegetation feature parameters highly related to soil moisture, and the edge lengths represented the degree of influence of each feature parameter on soil moisture. Soil moisture is assessed by the cuboid diagonal, which is referred to as the cuboid soil moisture index (CSMI) in this paper. The model was applied and validated in the Huang-Huai-Hai Plain. The results showed that (1) the difference in land surface temperature between day and night (ΔLST), land surface water index (LSWI), and accumulated precipitation (AP) were most closely correlated with soil moisture observation data in our study area, and were therefore selected as soil, crop, and meteorological system parameters to participate in CSMI calculations, respectively. (2) CSMI-1, with a cuboid length coefficient of 2/1/2, was the best model. The correlation of soil moisture derived from CSMI-1 with observed values was 0.64, 0.60, and 0.52 at depths of 10 cm, 20 cm, and 50 cm, respectively. (3) CSMI-1 had good applicability to the evaluation of soil moisture under different vegetation coverage. When the normalized difference vegetation index (NDVI)was 0–0.7, CSMI-1 was highly correlated with soil moisture at a significance level of 0.01. (4) The three-dimensional (3D) CSMI model can be easily converted to a two-dimensional (2D) model to adapt to different surface conditions (as long as the weight coefficient of one parameter is set to 0). Irrigation information (if available) can be considered as artificial recharge precipitation added in the AP to improve the accuracy of soil moisture inversion. This study provides a reference for soil moisture inversion using optical remote sensing images by integrating soil, vegetation, and meteorological feature parameters
