The surface arable land damage and destruction of the original ecosystem caused by the influence of coal mining disturbance are the major ecological disasters in the high underground water mining area. Identifying an arable-damaged area and obtaining its spatial distribution are important for ecological disaster monitoring. The influence of crop waterlogging caused by mining subsidence in high underground water mining areas was taken as an example, and based on the UAV multi-spectral images, the red band was introduced on the basis of traditional vegetation index to expand, which allowed to select the best 22 VI. Univariate regression, multivariable linear regression (MLR) based on the principle of least square method and back propagation neural networks (BPNN) were built accordingly by using the 22 VI along with field measurements of biomass data under the empirical modeling method. There were three indices should be taken into account to determine the optimal model, which were coefficient of determination (R2), root mean square error (RMSE) and estimation accuracy (EA). The spatial distribution inversion and analysis of maize biomass were undertaken in the study area by using the selected optimal model. It was concluded that the selected vegetation index was significantly related to biomass. And the highest estimation accuracy was obtained by using BP model. The value of R2 was 0.83 accordingly, which was generally increased by 0.10~ 0.17. The value of predicted root mean square error (RMSE) was 178.72g/m2, which was generally reduced by 29.65~60.23g/m2. The estimation accuracy (EA) could eventually reach 79.4%, which was increased by 3.3% ~ 7.1%. It can be concluded that the red edge band was more suited to the estimation of crop biomass in the mining subsidence area. Furthermore, the accuracy rate of the inversion model under the influence of coal mining subsidence could be increased dramatically by introducing red edge band to the construction of biomass inversion model. The research showed that the maize biomass in the coal mining subsidence basin was concentrated between an interval of 592 ~ 1050g/m2, which accounted for 74.4% of the total area. There was 14.1% of the crop acreage which represented those above ground biomass below 352g/m2. The overall growth of maize was severely affected by coal mining. There was a trend of maize biomass which was generally decreased from the basin margin to its centre. The research result can be used as an indicator to monitor and evaluate damaged ground in high underground water mining area, and it can also provide fundamental data and theory support for land rehabilitation and ecological restoration.