Abstract: Identif the extent,area,and spatial distribution information of cultivated land subsidence damage is an ob- jective demand for land reclamation and compensation in mining areas. Leaf chlorophyll is a good indicator when crops are damaged This paper focused on the impact of crop waterlogging caused by mining subsidence on high groundwater mining Dong Tan coalmine as an example. Based on the multi-spectral remote sensing image of UAV (Unmanned Aer- ial Vehicle),the traditional VI (Vegetation Index) was extended by adding the red edge band,and the new vegetation index was put forward. Then,based on the corresponding VI and combind ynchronous field measurements of chloro- phyll data,nivariate and multivariate chlorophyll inversion models were constructed using empirical modelling meth- ods,respectively,the remote sensing inversion models of chlorophyll in corn leaves were built,validated,and com- pared. Finally,the spatial distribution of chlorophyll in corn leaves was analyzed using the best model in the study are- a. The results showed that the correlation between the vegetation index and the leaf chlorophyll after introducing the red edge was significantly enhanced compared to the traditional vegetation index. In all univariate models,MVIred-edge (Red-edge Modified Vegetation Index) which were added red edge were selected as the best vegetation index. Using nivariate and MLR ( Multivariate Stepwise Linear Regression) method, the precision of the models based on the MVIred-edge was improved compared to the VI with the calibration coefficient of determination (R2 ) raised between 0. 10 and 0. 20,and the calibration RMSE (Root Mean Squares Error) reduced between 0. 11 and 1. 98,the validation R2 raised between 0. 03 and 0. 12,the validation RMSE reduced between 1. 0 and 1. 5,and finally the EA ( Estimation Accuracy) reached 83. 4% . The MLR models composed of MVIred-edge,GNDVI (Green Normalized Difference Vegeta- tion Index),NDVI(Normalized Difference Vegetation Index) obtained generally good performance with the EA greater than 80% . The main reasons improved the model precision were that red edge band are more ensitive to egetation hlo- rophyll esponses. Among them,when the vegetation biomass is large,the pigment content is high,and the growth is vig- orous,the red edge will move to the long wave direction (red shift). When the vegetation suffers from diseases,pests, pollution,leaf aging and other factors,the red edge will move to the short wave direction ( blue shift). It is indicated that the red-edge band is sensitive to the chlorophyll response of the crops in the coal mining subsidence area. The red edge band is introduced to construct the chlorophyll inversion model,which can be applied to remote sensing inversion of chlorophyll drones in maize leaves under the influence of coal mining subsidence. The study shows that the corn chlorophyll content in the coal mining subsidence basin is mainly concentrated in the 52 ~ 61 SPAD ( Soil and Plant Analyzer Development),and the area accounts for 75. 1% of the total. The chlorophyll content in the 9 ~ 52 SPAD crop area accounts for 5. 7% . The overall growth of the corn is affected by the disturbance of coal mining. The chloro- phyll content gradually decreased from the edge of the subsidence basin to the center. The study provides basic data and theoretical support for land damage monitoring and evaluation,land reclamation and ecological restoration.