Potato is an important food crop after rice, wheat, and corn, and its optimal cultivation and production are essential to ensure food security. Crop above-ground biomass (AGB) is widely considered to be closely related to crop growth status and is often directly involved in crop yield prediction and health status parameter assessment. Existing studies show that the remote sensing vegetation index loses sensitivity to crop parameters at medium to high crop cover, i. e., the “saturation phenomenon”, which limits accurate monitoring studies of AGB at mid to late crop growth. The main work of this study is to use a new vertically growing crop AGB model (VGC-AGB) combined with hyperspectral remote sensing for AGB estimation of potatoes at multiple growth stages. In response to the “saturation problem” of remote sensing spectral indices for crop biomass monitoring in multiple growth periods, VGC-AGB defined two parameters, leaf dry matter content (Cm) and vertical organ dry matter content (Csm), to describe the average dry matter content of potatoes leaves and stems, respectively. The aboveground biomass of leaves (AGBl) was calculated by leaf area index (LAI)×Cm, and aboveground biomass of vertical organs (AGBv) was calculated by the product of planting density (Cd), potato plant height (Ch) and Csm, i. e., Cd×Ch×Csm. Based on the 2019 potato field experiment at the National Precision Agriculture Research Demonstration Base, ground-based ASD hyperspectral data, measured plant height, AGB, and LAI data were obtained for four critical growth periods of potato. Hyperspectral reflectance data were used to construct hyperspectral feature parameters and compare the performance of three potato AGB estimation models of (1) hyperspectral feature parameters+plant height, (2) ground-based measurement parameters+VGC-AGB model and (3) hyperspectral feature parameters+VGC-AGB model, respectively. The results show that the new VGC-AGB model combined with hyperspectral remote sensing data can provide higher performance estimation results of potato AGBl, AGBv, and total AGB than the traditional AGB estimation method of hyperspectral remote sensing vegetation index + plant height, and the technique can provide technical support for rapid and nondestructive monitoring of potato AGB.